Datasets:

nielsr
/

paper-url-extraction-v1

paper_id int64	arxiv_id string	title string	abstract string	missing_github bool	missing_project_page bool	known_github_url null	known_project_page_url null	reference_github_url string	reference_project_page_url string	github_label_quality string	project_page_label_quality string	source string	split string	stratum string	sampling_weight float64	priority_source string
39,446	2410.23933	Language Models can Self-Lengthen to Generate Long Texts	Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training gap where pre-training lacks effective instructions for long-text generation, and post-training dat...	true	true	null	null	https://github.com/qwenlm/self-lengthen	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
36,837	2409.08563	Second-order difference subspace	Subspace representation is a fundamental technique in various fields of machine learning. Analyzing a geometrical relationship among multiple subspaces is essential for understanding subspace series' temporal and/or spatial dynamics. This paper proposes the second-order difference subspace, a higher-order extension of ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
69,005	2508.21430	Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models	Multimodal large language models (MLLMs) hold significant potential in medical applications, including disease diagnosis and clinical decision-making. However, these tasks require highly accurate, context-sensitive, and professionally aligned responses, making reliable reward models and judges critical. Despite their i...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
83,272	2605.08734	AdaPreLoRA: Adafactor Preconditioned Low-Rank Adaptation	Low-Rank Adaptation (LoRA) reparameterizes a weight update as a product of two low-rank factors, but the Jacobian J_{G} of the generator mapping the factors to the weight matrix is rank-deficient, so the factor-space preconditioner J_{G}^* {F}_t J_{G} induced by any {W}-space preconditioner {F}_t is singular, and conse...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
6,236	2107.06231	Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model	The aim of this work is to define a model based on deep learning that is able to identify different instrument timbres with as few parameters as possible. For this purpose, we have worked with classical orchestral instruments played with different dynamics, which are part of a few instrument families and which play not...	true	true	null	null	https://github.com/carlosholivan/Timbre-Classification-MultiHeadAttention	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
50,797	2506.07963	Reinforcing Multimodal Understanding and Generation with Dual Self-rewards	Building upon large language models (LLMs), recent large multimodal models (LMMs) unify cross-model understanding and generation into a single framework. However, LMMs still struggle to achieve accurate image-text alignment, prone to generating text responses contradicting the visual input or failing to follow the text...	true	true	null	null	https://github.com/hongjx175/suder	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
55,658	2601.22060	Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models	Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gai...	true	true	null	null	https://github.com/Osilly/Vision-DeepResearch	https://osilly.github.io/Vision-DeepResearch/	silver	silver	pwc_db_reviewed_urls	train	year_2026_plus:has_both	20.513158	representative_stratified_sample
50,409	2506.01863	Unified Scaling Laws for Compressed Representations	Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, the rise in computational cost for AI has motivated model compression techniques, notably quantization and sparsification, which have emerged...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
2,473	1908.11587	Copy-and-Paste Networks for Deep Video Inpainting	We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal cohere...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_pre_2020:unlabeled	20.375839	representative_stratified_sample
5,484	2104.08189	TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction	We propose TalkNet, a non-autoregressive convolutional neural model for speech synthesis with explicit pitch and duration prediction. The model consists of three feed-forward convolutional networks. The first network predicts grapheme durations. An input text is expanded by repeating each symbol according to the predic...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
65,512	2511.01066	HPLT 3.0: Very Large-Scale Multilingual Resources for LLM and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models	We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. These datasets are derived from web crawls from different so...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
41,920	2412.15177	Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying	Studies have underscored how, regardless of the recent breakthrough and swift advances in AI research, even state-of-the-art Large Language models (LLMs) continue to struggle when performing logical and mathematical reasoning. The results seem to suggest that LLMs still work as (highly advanced) data pattern identifier...	true	true	null	null	https://github.com/fcast07/cqot	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
33,778	2406.18958	AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation	The field of text-to-image (T2I) generation has made significant progress in recent years, largely driven by advancements in diffusion models. Linguistic control enables effective content creation, but struggles with fine-grained control over image generation. This challenge has been explored, to a great extent, by inc...	true	true	null	null	https://github.com/open-mmlab/anycontrol	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
63,224	2512.23448	Dynamic Subspace Composition: Efficient Adaptation via Contractive Basis Expansion	Mixture of Experts (MoE) models scale capacity but often suffer from representation collapse and gradient instability. We propose Dynamic Subspace Composition (DSC), a framework that approximates context-dependent weights via a state-dependent, sparse expansion of a shared basis bank. Formally, DSC models the weight up...	true	true	null	null	https://github.com/VladimerKhasia/DSC	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
102,065	2510.03690	From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning	Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions. In this work, we propose a unified framework that explicitly models graph data as a mixture of probabilistic graph generative models represented by graphons. To characterize an...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
51,506	2506.21298	Exploring Adapter Design Tradeoffs for Low Resource Music Generation	Fine-tuning large-scale music generation models, such as MusicGen and Mustango, is a computationally expensive process, often requiring updates to billions of parameters and, therefore, significant hardware resources. Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly adapter-based methods, have emerged as...	true	true	null	null	https://github.com/atharva20038/ACMMM_Adapters/tree/main	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
98,807	2606.24937	The Hitchhiker's Guide to Agentic AI: From Foundations to Systems	The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not ju...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
8,338	2203.10316	Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction	Solving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly performing relational reasoning between quantities in the given context. While...	true	true	null	null	https://github.com/allanj/deductive-mwp	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
31,254	2405.07896	Almanac Copilot: Towards Autonomous Electronic Health Record Navigation	Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout. Despite the promise of electronic medical records (EMR), the transition from paper-based records has been negatively associated with clinician wellness, in part due to poor user ex...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
55,723	2602.01362	Balancing Understanding and Generation in Discrete Diffusion Models	In discrete generative modeling, two dominant paradigms demonstrate divergent capabilities: Masked Diffusion Language Models (MDLM) excel at semantic understanding and zero-shot generalization, whereas Uniform-noise Diffusion Language Models (UDLM) achieve strong few-step generation quality, yet neither attains balance...	true	true	null	null	https://github.com/MzeroMiko/XDLM	https://mzeromiko.github.io/public/XDLM	silver	silver	pwc_db_reviewed_urls	train	year_2026_plus:has_both	20.513158	representative_stratified_sample
20,771	2309.12855	Cross-Modal Translation and Alignment for Survival Analysis	With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images. However, existing methods either directly adopt a straightforward fusion of pathological features and genomic prof...	true	true	null	null	https://github.com/ft-zhou-zzz/cmta	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
89,577	2405.15441	Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances	Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is desirable when applied to high-dimensional data with low-dimensional structures. The kernel max-sliced (KMS) Wasserstein distance is develope...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
35,947	2408.09530	PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding	The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies has demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-sp...	true	true	null	null	https://github.com/ddw2aigroup2cqupt/pa-llava	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
77,584	2403.16073	Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications	Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart contracts, but the state-of-the-art indicates that even GPT-4 can achieve only 30% precision (when both decision and justification are correct...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
6,409	2108.05818	PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management	The pre-trained model (PTM) is revolutionizing Artificial Intelligence (AI) technology. However, the hardware requirement of PTM training is prohibitively high, making it a game for a small proportion of people. Therefore, we proposed PatrickStar system to lower the hardware requirements of PTMs and make them accessibl...	true	true	null	null	https://github.com/Tencent/PatrickStar	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
66,192	2510.16829	Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation	Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed -- creates specific needs for an appropriate response. However, most evaluations, while capturing the model's capability to respond, often ignore who is asking. This gap is especia...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
94,625	2510.06203	Reference Grounded Skill Discovery	Scaling unsupervised skill discovery algorithms to high-DoF agents remains challenging. As dimensionality increases, the exploration space grows exponentially, while the manifold of meaningful skills remains limited. Therefore, semantic meaningfulness becomes essential to effectively guide exploration in high-dimension...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
80,778	2104.06722	WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems	Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solve MWPs require full supervision in the form of intermediate equations. However, labeling every MWP with its corresponding equations is a time-consuming and expensive task. In order to add...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
98,631	2203.01252	A Unified Query-based Paradigm for Point Cloud Understanding	3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ- Paradigm) for 3D understanding tasks including detection, segmentation, and classification. EQ-Paradigm is a unified paradigm that enables the combination of any ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
705	1705.03633	Inferring and Executing Programs for Visual Reasoning	Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning. Inspired by module...	true	true	null	null	https://github.com/facebookresearch/clevr-iep	null	silver	unknown	pwc_db_current_urls	train	year_pre_2020:has_github	20.369231	representative_stratified_sample
30,488	2404.11826	AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence	As the integration of large language models into daily life is on the rise, there is a clear gap in benchmarks for advising on subjective and personal dilemmas. To address this, we introduce AdvisorQA, the first benchmark developed to assess LLMs' capability in offering advice for deeply personalized concerns, utilizin...	true	true	null	null	https://github.com/minbeomkim/advisorqa	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
90,082	2503.17109	Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval	Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
1,323	1806.02643	Re-evaluating Evaluation	Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversarial attacks, and other complications has diluted the basic evaluation model by overwhelming researchers with choices. Deliberate or accident...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_pre_2020:unlabeled	20.375839	representative_stratified_sample
26,569	2402.00433	Merging Multi-Task Models via Weight-Ensembling Mixture of Experts	Merging various task-specific Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified by task arithmetic, have been proven to be both effective and scalable. Existing methods have primarily focused on seeking a static optimal ...	true	true	null	null	https://github.com/tanganke/weight-ensembling_moe	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
302	1602.02068	From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification	We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smooth and convex loss func...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_pre_2020:unlabeled	20.375839	representative_stratified_sample
15,927	2305.13242	MAGE: Machine-generated Text Detection in the Wild	Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods on specific domains or particular language models. ...	true	true	null	null	https://github.com/yafuly/mage	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
100,246	2602.18700	Watermarking LLM Agent Trajectories	LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering. Despite the high cost of creating these datasets, existing literature has overlooked copyright protection for ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
77,423	2404.10843	Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators	We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators. We show how GNPs can be used (i) to estimate geometric properties, such as the metric and curvatures, (ii) to approximate Partial Differential Equations (PDEs) on manifolds, (iii) learn s...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
90,514	2412.12617	PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection	Point cloud anomaly detection under the anomaly-free setting poses significant challenges as it requires accurately capturing the features of 3D normal data to identify deviations indicative of anomalies. Current efforts focus on devising reconstruction tasks, such as acquiring normal data representations by restoring ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
16,372	2305.16896	MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting	Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that leverages chain-of-thought (CoT) prompting to incorporate multiple external tools, such as a calculator and a knowledge retriever, during the...	true	true	null	null	https://github.com/inabatatsuro/multitool-cot	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
98,362	2606.08670	WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis	Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting, but existing approaches either incur prohibitive computational cost at volumetric scale or rely on lossy latent compression that may compr...	true	true	null	null	https://github.com/sisinflab/WaveDiT	https://danesed.github.io/wavedit-page/	silver	silver	pwc_db_reviewed_urls	train	year_2026_plus:has_both	20.513158	representative_stratified_sample
19,195	2308.06692	SimMatchV2: Semi-Supervised Learning with Graph Consistency	Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and unlabeled data...	true	true	null	null	https://github.com/mingkai-zheng/simmatchv2	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
17,321	2306.07290	Value function estimation using conditional diffusion models for control	A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address, sooner than later, the potential problem of running out of high-quali...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
31,373	2405.10480	Lean Attention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers	Transformer-based models have emerged as one of the most widely used architectures for natural language processing, natural language generation, and image generation. The size of the state-of-the-art models has increased steadily reaching billions of parameters. These huge models are memory hungry and incur significant...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
75,674	2411.18871	Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment	With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical ener...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
9,255	2205.14690	CoNT: Contrastive Neural Text Generation	Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive l...	true	true	null	null	https://github.com/shark-nlp/cont	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
17,826	2306.15782	UTRNet: High-Resolution Urdu Text Recognition In Printed Documents	In this paper, we propose a novel approach to address the challenges of printed Urdu text recognition using high-resolution, multi-scale semantic feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model, demonstrates state-of-the-art performance on benchmark datasets. To address the limitations of p...	true	true	null	null	https://github.com/abdur75648/UTRNet-High-Resolution-Urdu-Text-Recognition	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
16,776	2306.00800	FigGen: Text to Scientific Figure Generation	The generative modeling landscape has experienced tremendous growth in recent years, particularly in generating natural images and art. Recent techniques have shown impressive potential in creating complex visual compositions while delivering impressive realism and quality. However, state-of-the-art methods have been f...	true	true	null	null	https://github.com/joanrod/figure-diffusion	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
40,660	2411.19233	Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes	State-of-the-art novel view synthesis methods achieve impressive results for multi-view captures of static 3D scenes. However, the reconstructed scenes still lack "liveliness," a key component for creating engaging 3D experiences. Recently, novel video diffusion models generate realistic videos with complex motion and ...	true	true	null	null	https://github.com/wimmerth/gaussians2life	https://wimmerth.github.io/gaussians2life.html	silver	silver	pwc_db_reviewed_urls	train	year_2024:has_both	20.44	representative_stratified_sample
59,004	2601.13719	Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search	Long video understanding presents significant challenges for vision-language models due to extremely long context windows. Existing solutions relying on naive chunking strategies with retrieval-augmented generation, typically suffer from information fragmentation and a loss of global coherence. We present HAVEN, a unif...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
3,041	2001.11077	stream-learn -- open-source Python library for difficult data stream batch analysis	stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incrementa...	true	true	null	null	https://github.com/w4k2/stream-learn	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
42,295	2412.20787	SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity	Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code generation. Existing benchmarks like MMLU, C-Eval, and HumanEval assess general LLM performance but lack focus on specific expert domains su...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
35,252	2407.18961	MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains	Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that driv...	true	true	null	null	https://github.com/apple/axlearn	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
66,715	2510.08318	LinVideo: A Post-Training Framework towards O(n) Attention in Efficient Video Generation	Video diffusion models (DMs) have enabled high-quality video synthesis. However, their computation costs scale quadratically with sequence length because self-attention has quadratic complexity. While linear attention lowers the cost, fully replacing quadratic attention requires expensive pretraining due to the limited...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
56,711	2603.12634	Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents	Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
23,097	2311.04902	Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models	Large Language Models (LLMs) with billions of parameters are prime targets for network pruning, removing some model weights without hurting performance. Prior approaches such as magnitude pruning, SparseGPT, and Wanda, either concentrated solely on weights or integrated weights with activations for sparsity. However, t...	true	true	null	null	https://github.com/vila-lab/gblm-pruner	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
2,476	1909.00015	Adaptively Sparse Transformers	Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are de...	true	true	null	null	https://github.com/deep-spin/entmax	null	silver	unknown	pwc_db_current_urls	train	year_pre_2020:has_github	20.369231	representative_stratified_sample
60,087	2603.00947	Mobile-VTON: High-Fidelity On-Device Virtual Try-On	Virtual try-on (VTON) has recently achieved impressive visual fidelity, but most existing systems require uploading personal photos to cloud-based GPUs, raising privacy concerns and limiting on-device deployment. To address this, we present Mobile-VTON, a high-quality, privacy-preserving framework that enables fully of...	true	true	null	null	https://github.com/tmllab/2026_cvpr_mobile-vton	https://zhenchenwan.github.io/Mobile-VTON/	silver	silver	pwc_db_reviewed_urls	train	year_2026_plus:has_both	20.513158	representative_stratified_sample
23,113	2311.05092	GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)	Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility. Our proposed model is rigoro...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
15,533	2305.08144	Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction	The Graphical User Interface (GUI) is pivotal for human interaction with the digital world, enabling efficient device control and the completion of complex tasks. Recent progress in Large Language Models (LLMs) and Vision Language Models (VLMs) offers the chance to create advanced GUI agents. To ensure their effectiven...	true	true	null	null	https://github.com/x-lance/mobile-env	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
70,935	2507.15335	ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis	Industrial defect detection systems face critical limitations when confined to one-class anomaly detection paradigms, which assume uniform outlier distributions and struggle with data scarcity in realworld manufacturing environments. We present ExDD (Explicit Dual Distribution), a novel framework that transcends these ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
98,884	2601.17756	MV-S2V: Multi-View Subject-Consistent Video Generation	Existing Subject-to-Video Generation (S2V) methods have achieved high-fidelity and subject-consistent video generation, yet remain constrained to single-view subject references. This limitation renders the S2V task reducible to an S2I + I2V pipeline, failing to exploit the full potential of video subject control. In th...	true	true	null	null	https://github.com/szy-young/mv-s2v	https://szy-young.github.io/mv-s2v	silver	silver	pwc_db_current_urls	train	year_2026_plus:has_both	20.513158	representative_stratified_sample
44,389	2502.11921	Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier	Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance, or (ii) jointly using a single measure that accounts for fairness with respect to relevance. However, approach (i) often does not provide a...	true	true	null	null	https://github.com/theresiavr/dpfr-recsys-evaluation	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
35,380	2407.21646	Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent	In this paper, we present Cross Language Agent -- Simultaneous Interpretation, CLASI, a high-quality and human-like Simultaneous Speech Translation (SiST) System. Inspired by professional human interpreters, we utilize a novel data-driven read-write strategy to balance the translation quality and latency. To address th...	true	true	null	null	https://github.com/byteresearchcla/realsi	https://byteresearchcla.github.io/clasi	silver	silver	pwc_db_reviewed_urls	train	year_2024:has_both	20.44	representative_stratified_sample
34,413	2407.07402	ActionVOS: Actions as Prompts for Video Object Segmentation	Delving into the realm of egocentric vision, the advancement of referring video object segmentation (RVOS) stands as pivotal in understanding human activities. However, existing RVOS task primarily relies on static attributes such as object names to segment target objects, posing challenges in distinguishing target obj...	true	true	null	null	https://github.com/ut-vision/actionvos	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
523	1611.09309	Gaze Embeddings for Zero-Shot Image Classification	Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability t...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_pre_2020:unlabeled	20.375839	representative_stratified_sample
72,724	2505.19620	Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs	Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these chall...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
80,481	2110.00362	Dynamics of targeted ransomware negotiation	In this paper, we consider how the development of targeted ransomware has affected the dynamics of ransomware negotiations to better understand how to respond to ransomware attacks. We construct a model of ransomware negotiations as an asymmetric non-cooperative two-player game. In particular, our model considers the i...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
93,850	2505.12300	HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models	Fine-tuning large language models (LLMs) on a mixture of diverse datasets poses challenges due to data imbalance and heterogeneity. Existing methods often address these issues across datasets (globally) but overlook the imbalance and heterogeneity within individual datasets (locally), which limits their effectiveness. ...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
6,086	2106.12059	Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning	We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning. Unlike acquiring the top-K points from the pool set, score- or rank-based sampling takes into account that acquisition scores change as new data are acquired. This simple strategy for adapt...	true	true	null	null	https://github.com/baal-org/baal	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
76,166	2410.16278	Edge Computing in Distributed Acoustic Sensing: An Application in Traffic Monitoring	Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tracking vehicles using DAS data, focusing on real-time processing throug...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
1,576	1810.12440	TallyQA: Answering Complex Counting Questions	Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world's larges...	true	true	null	null	https://github.com/manoja328/tallyqacode	null	silver	unknown	pwc_db_current_urls	train	year_pre_2020:has_github	20.369231	representative_stratified_sample
56,603	2603.09951	Towards a Neural Debugger for Python	Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreters (FAIR CodeGen Team et al., 2025). However, developers rarely execute programs step by step; instea...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
81,284	2006.01463	An ASR Guided Speech Intelligibility Measure for TTS Model Selection	The perceptual quality of neural text-to-speech (TTS) is highly dependent on the choice of the model during training. Selecting the model using a training-objective metric such as the least mean squared error does not always correlate with human perception. In this paper, we propose an objective metric based on the pho...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2020_2023:unlabeled	20.4	representative_stratified_sample
47,711	2504.10903	Efficient Reasoning Models: A Survey	Reasoning models have demonstrated remarkable progress in solving complex and logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to arriving at a final answer. Yet, the emergence of this "slow-thinking" paradigm, with numerous tokens generated in sequence, inevitably introduces substantial comp...	true	true	null	null	https://github.com/fscdc/awesome-efficient-reasoning-models	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
85,741	2509.23323	LLM Interpretability with Identifiable Temporal-Instantaneous Representation	Despite Large Language Models' remarkable capabilities, understanding their internal representations remains challenging. Mechanistic interpretability tools such as sparse autoencoders (SAEs) were developed to extract interpretable features from LLMs but lack temporal dependency modeling, instantaneous relation represe...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
13,021	2302.05496	MaskSketch: Unpaired Structure-guided Masked Image Generation	Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation ...	true	true	null	null	https://github.com/google-research/masksketch	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
16,389	2305.17020	Diable: Efficient Dialogue State Tracking as Operations on Tables	Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn. This approach is inefficient, especially when the number of slots is large a...	true	true	null	null	https://github.com/amazon-science/efficient-dialogue-state-tracking-by-sequential-information-processing	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
26,571	2402.00474	SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models	Recent advances in large language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, their effective application in the medical domain is hampered by a lack of medical domain knowledge. In this study, we present SA-MDKIF, a scalable and adaptable framewo...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
13,190	2302.09462	MedViT: A Robust Vision Transformer for Generalized Medical Image Classification	Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attacks since inaccurate diagnosis could lead to disastrous consequences in ...	true	true	null	null	https://github.com/Omid-Nejati/MedViT	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
67,457	2509.24696	T-POP: Test-Time Personalization with Online Preference Feedback	Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses. However, current personalization methods are ill-suited for new users, as they typically require either slow, resource-intensive fine-tuning or a substantial amount of pre-existi...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
86,972	2410.12609	Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs	Inspired by the success of large language models, there is a trend toward developing graph foundation models to conduct diverse downstream tasks in various domains. However, current models often require extra fine-tuning to apply their learned structural and semantic representations to new graphs, which limits their ve...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
50,583	2506.04392	Phi-Omni-ST: A multimodal language model for direct speech-to-speech translation	Speech-aware language models (LMs) have demonstrated capabilities in understanding spoken language while generating text-based responses. However, enabling them to produce speech output efficiently and effectively remains a challenge. In this paper, we present Phi-Omni-ST, a multimodal LM for direct speech-to-speech tr...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
103,450	2511.04952	LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model	Long context inference scenarios have become increasingly important for large language models, yet they introduce significant computational latency. While prior research has optimized long-sequence inference through operators, model architectures, and system frameworks, tokenization remains an overlooked bottleneck. Ex...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
44,192	2502.10050	A Survey on LLM-powered Agents for Recommender Systems	Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language inte...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
36,428	2409.00097	Large Language Models for Disease Diagnosis: A Scoping Review	Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic v...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
56,150	2602.13516	SPILLage: Agentic Oversharing on the Web	LLM-powered agents are beginning to automate user's tasks across the open web, often with access to user resources such as emails and calendars. Unlike standard LLMs answering questions in a controlled ChatBot setting, web agents act "in the wild", interacting with third parties and leaving behind an action trace. Ther...	true	true	null	null	https://github.com/jrohsc/SPILLage	null	silver	unknown	pwc_db_current_urls	train	year_2026_plus:has_github	20.301887	representative_stratified_sample
61,824	2601.21633	A Tilted Seesaw: Revisiting Autoencoder Trade-off for Controllable Diffusion	In latent diffusion models, the autoencoder (AE) is typically expected to balance two capabilities: faithful reconstruction and a generation-friendly latent space (e.g., low gFID). In recent ImageNet-scale AE studies, we observe a systematic bias toward generative metrics in handling this trade-off: reconstruction metr...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
102,136	2605.17989	Predictive Prefetching for Retrieval-Augmented Generation	Retrieval-Augmented Generation (RAG) improves factual grounding in large language models but suffers from substantial latency due to synchronous retrieval. While recent work explores asynchronous retrieval, existing approaches rely on heuristic coordination between retrieval and generation and assume stable information...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample
34,498	2407.08569	Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene	The unsupervised 3D object detection is to accurately detect objects in unstructured environments with no explicit supervisory signals. This task, given sparse LiDAR point clouds, often results in compromised performance for detecting distant or small objects due to the inherent sparsity and limited spatial resolution....	true	true	null	null	https://github.com/ruiyang-061x/lise	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
74,865	2502.04227	Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks	We explore the feasibility and effectiveness of using LLM-driven autonomous systems for Assumed Breach penetration testing in enterprise networks. We introduce a novel prototype that, driven by Large Language Models (LLMs), can compromise accounts within a real-life Active Directory testbed. Our research provides a com...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
70,817	2507.17699	Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations	Large Reasoning Models (LRMs) have become a central focus in today's large language model (LLM) research, where models are designed to output a step-by-step thinking process before arriving at a final answer to handle complex reasoning tasks. Despite their promise, recent empirical studies (e.g., [Shojaee et al., 2025]...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
53,028	2509.23285	Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning	Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to improve their internal reasoning ability by integrating external tools. However, models employing TIR often display suboptimal behaviors, such as insufficient or excessive tool usage and overthinking after tool calls. The challenge of incentivizing...	true	true	null	null	https://github.com/asilverlight/Tool-Light	null	silver	unknown	pwc_db_current_urls	train	year_2025:has_github	20.381546	representative_stratified_sample
32,360	2406.03070	A-Bench: Are LMMs Masters at Evaluating AI-generated Images?	How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments required for user studies, many researchers have turned towards employing large multi-modal models (LMMs) as AIGI evaluators, the precision and val...	true	true	null	null	https://github.com/q-future/a-bench	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
73,617	2504.06559	TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network	Tabular data analysis presents unique challenges that arise from heterogeneous feature types, missing values, and complex feature interactions. While traditional machine learning methods like gradient boosting often outperform deep learning, recent advancements in neural architectures offer promising alternatives. In t...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2025:unlabeled	20.403595	representative_stratified_sample
41,764	2412.13018	OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain	As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical domains where LLMs may lack domain-specific knowledge. In this paper, we introduce an omnidirectional and automatic RAG benchmark, OmniEval, i...	true	true	null	null	https://github.com/ruc-nlpir/omnieval	null	silver	unknown	pwc_db_current_urls	train	year_2024:has_github	20.412587	representative_stratified_sample
4,967	2102.00714	NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning	Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current offline RL benchmarks commonly have a large reality gap, because they involve large datasets collected by highly exploratory policies, and ...	true	true	null	null	https://github.com/polixir/NeoRL	null	silver	unknown	pwc_db_current_urls	train	year_2020_2023:has_github	20.390505	representative_stratified_sample
77,417	2404.11595	A Deep Dive into Large Language Models for Automated Bug Localization and Repair	Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug fixing utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug locations, rely on line...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2024:unlabeled	20.408791	representative_stratified_sample
61,553	2602.01056	From shape to fate: making bacterial swarming expansion predictable	Microbial swarming on mucosal surfaces reshapes microbial communities and influences mucosal healing and antibiotic tolerance. Yet even with time-lapse microscopy and deep learning, analyses of swarming colonies remain descriptive and cannot forecast how their fronts reorganize in time. This limitation is significant b...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_2026_plus:unlabeled	20.368601	representative_stratified_sample
103,351	2505.20196	Temporal Sampling for Forgotten Reasoning in LLMs	Fine-tuning large language models (LLMs) is intended to improve their reasoning capabilities, yet we uncover a counterintuitive effect: models often forget how to solve problems they previously answered correctly during training. We term this phenomenon Temporal Forgetting and show that it is widespread across model si...	true	true	null	null	null	null	unknown	unknown	pwc_db_current_urls	train	year_unknown:unlabeled	20.415966	representative_stratified_sample

End of preview. Expand in Data Studio

Papers With Code URL Extraction

A representative dataset for training and evaluating tool-using agents that find the official GitHub repository and project page for an AI research paper. It was prepared for the pwc-url-extraction-v1 Prime/verifiers environment.

Splits

Split	Rows
train	4,000
validation	500
test	500

Rows were sampled with seed 13 from up to 120,000 Papers With Code candidates, stratified by paper year and known URL state. metadata.json contains the population and sampled counts for every stratum.

Fields

paper_id: Papers With Code database identifier.
arxiv_id, title, abstract: paper metadata given to the agent.
missing_github, missing_project_page: URL fields the agent must research.
known_github_url, known_project_page_url: URLs already known in the task prompt, when applicable.
reference_github_url, reference_project_page_url: canonical targets used for deterministic scoring.
github_label_quality, project_page_label_quality: one of gold, silver, verified_null, or unknown.
source: provenance of the label.
split, stratum, sampling_weight: sampling metadata.
priority, priority_source: curation priority metadata.

Label semantics

Recorded URLs are silver labels unless independently upgraded. A missing URL is a negative label only when its quality is verified_null. An unknown label means the field was not conclusively reviewed and must be excluded from reward; it must not be treated as a negative example.

Intended use

The dataset is intended for supervised or reinforcement-learning experiments with agents that can inspect Hugging Face paper metadata, read papers, and search the web. The original environment scores canonical URL equality and tracks tool-use and research-completeness metrics.

Limitations

Paper URLs and repository ownership can change after export. Silver labels may contain stale or imperfect associations. Evaluation should preserve the provided held-out splits and report infrastructure errors separately from valid low-reward outcomes.

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