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5b01231738f559ee87d357cc95ff2f0b096dcc8bb8c15f4a3f079eac1d082dab
2026-01-15T07:00:10+00:00
Complex mesoscale landscapes beneath Antarctica mapped from space
Science, Volume 391, Issue 6782, Page 314-319, January 2026.
https://www.science.org/doi/abs/10.1126/science.ady2532?af=R
Academic Papers
svg
564f8a12ff3b57c194351f4cb81157af3ed984e86a6d3545866b10aace134490
2025-11-27T07:00:00+00:00
Characterizing transport in a quantum gas by measuring Drude weights
Science, Volume 391, Issue 6782, Page 290-293, January 2026.
https://www.science.org/doi/abs/10.1126/science.ads8327?af=R
Academic Papers
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16d8fc0fbb73fe292e2ca415f0385c63b57112b77b49f2c60749be1ca5f3c2ba
2026-01-15T07:00:10+00:00
Transforming mental health research and care through artificial intelligence
Science, Volume 391, Issue 6782, Page 249-258, January 2026.
https://www.science.org/doi/abs/10.1126/science.adz9193?af=R
Academic Papers
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990dde5e11a4da6fbbeacfb5d4b8a8da1ea6c9dd1b63a162e06c6aff2cf72b20
2026-01-15T07:00:10+00:00
Growing pains
Science, Volume 391, Issue 6782, Page 322-322, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef3527?af=R
Academic Papers
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7e115610b05ce854362a3abbc4aa21540a64147d0c9132f0b85f10fa9dbdb436
2026-01-15T07:00:10+00:00
In Other Journals
Science, Volume 391, Issue 6782, Page 260-261, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4211?af=R
Academic Papers
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ebf2adfee2443c831852a1f882056b4d2b232c9234c5743ed169b9611a9dc59c
2026-01-15T07:00:10+00:00
Blood vessels under pressure
Science, Volume 391, Issue 6782, Page 237-238, January 2026.
https://www.science.org/doi/abs/10.1126/science.aed9277?af=R
Academic Papers
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b390f8eaa667bdab15583d1e6f7378be56499d4d6fdf727683a4bd7b9619c6ba
2026-01-15T07:00:10+00:00
A new cell type drove human brain complexity
Science, Volume 391, Issue 6782, Page 240-240, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee0974?af=R
Academic Papers
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24dcdadee86ff5ac8a51590a638c338af1f76abbb63b3cbe80694a72897e2887
2026-01-15T07:00:10+00:00
Robust perovskite nanocrystal emitters
Science, Volume 391, Issue 6782, Page 238-239, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee0989?af=R
Academic Papers
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feaaf8291404cc6426d42719cc1f40cf4b91e7f4c259f245a018b309de3449d2
2026-01-15T07:00:10+00:00
Not a big baby
Science, Volume 391, Issue 6782, Page 234-235, January 2026.
https://www.science.org/doi/abs/10.1126/science.aed8356?af=R
Academic Papers
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bee94ddd6e1e6c2201d995249e73b322c17d7b1c5c4808414e5d6f0e3ab2be07
2026-01-15T07:00:10+00:00
Uncovering Antarctica’s ice-draped landscape
Science, Volume 391, Issue 6782, Page 235-236, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee4245?af=R
Academic Papers
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7c887dcf92a3ff431316767fdf377780a3d2355464a9c1f3e6fbe4ccc3934117
2026-01-15T07:00:10+00:00
Canada’s dismantled safeguards threaten salmon
Science, Volume 391, Issue 6782, Page 247-248, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee3537?af=R
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68f48b4d3a6fce26ac99e248408e98a7dd1d01d21a87310325e3cdd575a7f5d0
2026-01-15T07:00:10+00:00
Climate-change extremes threaten Iraq
Science, Volume 391, Issue 6782, Page 248-248, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee9226?af=R
Academic Papers
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b869a6b272458637c91707154df07f871c116ab9404458969abe4ef31a7fb055
2026-01-15T07:00:10+00:00
Misusing research to trap songbirds in Spain
Science, Volume 391, Issue 6782, Page 247-247, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee3825?af=R
Academic Papers
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db805d41d960d17a2dc8d2a918368a5af6c4a7b386da5cba6fa5ac0970e1029a
2026-01-15T07:00:10+00:00
A difficult rebirth
Science, Volume 391, Issue 6782, Page 228-232, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4208?af=R
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5aa7f61083509f0f0f4be870b3f338fec3bf54d0c1e1d91e5238a8484fa94611
2026-01-15T07:00:10+00:00
Scientists reject call to retest childhood vaccines
Science, Volume 391, Issue 6782, Page 220-221, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4614?af=R
Academic Papers
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c40b5216d1b65d49ed61c253a80ae38b97012693ed533bebb5354b730f0dfe21
2026-01-15T07:00:10+00:00
Cellular ‘vaults’ deployed to spy on gene activity
Science, Volume 391, Issue 6782, Page 222-223, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4615?af=R
Academic Papers
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b8f36efe7135930bbdb9c82d5d275776388f6240f03a18103342693fc0731e7c
2026-01-15T07:00:10+00:00
Low doses of insecticide speed fish aging and death
Science, Volume 391, Issue 6782, Page 224-225, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4616?af=R
Academic Papers
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2cd4fc1eb4db2d294f47162dacdba74d572e378211803983b062dee1e93d5c26
2026-01-15T07:00:10+00:00
Arctic’s ‘last ice area’ is on thin ice
Science, Volume 391, Issue 6782, Page 225-226, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4617?af=R
Academic Papers
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c88c9e7c470aac7df0b7965949583a91a6c833fa1535ee9088ca5b3e6b149a3f
2026-01-15T07:00:10+00:00
Ex–Google CEO funds private space telescope bigger than Hubble
Science, Volume 391, Issue 6782, Page 226-227, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4618?af=R
Academic Papers
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72a8c465445ce8a4e90325fd7ce506ebca4d17e6ed712a61e0a13588a65733ff
2026-01-15T08:00:00+00:00
The mirage of AI deregulation
Science, Volume 391, Issue 6782, January 2026.
https://www.science.org/doi/abs/10.1126/science.aee4900?af=R
Academic Papers
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ec44f9126ee51029f9bb713e9e7f21308284c2de2a9765335f55da3c3cdd40f2
2026-01-15T07:00:10+00:00
The High Seas Treaty, at last
Science, Volume 391, Issue 6782, Page 219-219, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef3177?af=R
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b6aadab79ec75444773c7cbdbf3017f96d84e34302c9dd0aa9da46cfd4f4131d
2026-01-15T07:00:10+00:00
A theory of change approach to enhance the post-2030 sustainable development agenda
Science, Volume 391, Issue 6782, Page 241-244, January 2026.
https://www.science.org/doi/abs/10.1126/science.adz5704?af=R
Academic Papers
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77354b6255170146328d8805cbf55bdc46c0110a0998578fd0e53c6a46d14815
2026-01-15T07:00:10+00:00
In Science Journals
Science, Volume 391, Issue 6782, Page 259-261, January 2026.
https://www.science.org/doi/abs/10.1126/science.aef4210?af=R
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0db0d92a323ccbe0a8d56505fba4a34fc8c44426b393b1dd4b4b84cc16c4f938
2026-01-16T00:00:00-05:00
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
arXiv:2601.09709v1 Announce Type: new Abstract: Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circu...
https://arxiv.org/abs/2601.09709
Academic Papers
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f7b907bef486c4120c8184508862d93d724f46026f9a48bb0ce09f6ed5c84795
2026-01-16T00:00:00-05:00
Segmenta\c{c}\~ao Comportamental, Do Not Track e o desenvolvimento jur\'idico europeu e holand\^es
arXiv:2601.09711v1 Announce Type: new Abstract: This paper discusses legal developments in Europe and the Netherlands. Recent decisions show that European data protection law, or privacy law, applies to behavioral targeting in most cases. Dutch law explicitly presumes that data protection law applies to behavioral targ...
https://arxiv.org/abs/2601.09711
Academic Papers
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68cfc4b17418b91d8aba66f7ba9dcf498b0bbf604b9d1589897da598ae74f400
2026-01-16T00:00:00-05:00
Behavioral Targeting, a European Legal Perspective
arXiv:2601.09712v1 Announce Type: new Abstract: Behavioral targeting, or online profiling, is a hotly debated topic. Much of the collection of personal information on the Internet is related to behavioral targeting, although research suggests that most people don't want to receive behaviorally targeted advertising. The...
https://arxiv.org/abs/2601.09712
Academic Papers
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162b606f228bf44728259aae4c43e12995e4e621504474c5a33a5940433ad2f6
2026-01-16T00:00:00-05:00
LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialogue
arXiv:2601.09713v1 Announce Type: new Abstract: Proactively predicting a users next utterance in human-machine dialogue can streamline interaction and improve user experience. Existing commercial API-based solutions are subject to privacy concerns while deploying general-purpose LLMs locally remains computationally exp...
https://arxiv.org/abs/2601.09713
Academic Papers
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95cef6abd43dc0988c6f16848bb2ab8e2c170bd3a586b7d41fa3d52b1e015258
2026-01-16T00:00:00-05:00
Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines
arXiv:2601.09714v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into the scientific ecosystem raises fundamental questions about the creativity and originality of AI-generated research. Recent work has identified ``smart plagiarism'' as a concern in single-step prompting approaches, wher...
https://arxiv.org/abs/2601.09714
Academic Papers
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487233275c74f8ab4ee6a1c7629d82c9cfd1055a5e4d815993c7a267afb3416e
2026-01-16T00:00:00-05:00
Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents
arXiv:2601.09715v1 Announce Type: new Abstract: The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating comple...
https://arxiv.org/abs/2601.09715
Academic Papers
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2514ff80acdd1e7bdcd0ac2c0d38be39c752c5ea7369619fedb7c4f351478ede
2026-01-16T00:00:00-05:00
Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research
arXiv:2601.09716v1 Announce Type: new Abstract: Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepresented in this technological shift. This paper provides the first comprehensive overview of NLP progress and challenges for ...
https://arxiv.org/abs/2601.09716
Academic Papers
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46101a05ba9a2af19f4decd52a7c68cc6a0470766aa1e60fcb5002ecd4b09e4d
2026-01-16T00:00:00-05:00
SALP-CG: Standard-Aligned LLM Pipeline for Classifying and Grading Large Volumes of Online Conversational Health Data
arXiv:2601.09717v1 Announce Type: new Abstract: Online medical consultations generate large volumes of conversational health data that often embed protected health information, requiring robust methods to classify data categories and assign risk levels in line with policies and practice. However, existing approaches la...
https://arxiv.org/abs/2601.09717
Academic Papers
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9dacfd675b72b3141afb52b0d50c40350a3f3bbb1995a08cfe6f35e8c505e2bf
2026-01-16T00:00:00-05:00
StatLLaMA: A multi-stage training framework for building a domain-optimized statistical language model
arXiv:2601.09718v1 Announce Type: new Abstract: This study investigates how to efficiently build a domain-specialized large language model (LLM) for statistics using the lightweight LLaMA-3.2-3B family as the foundation model (FM). We systematically compare three multi-stage training pipelines, starting from a base FM ...
https://arxiv.org/abs/2601.09718
Academic Papers
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d22623716d5009a15b483f8e9707b3664bbac91c259a50dfbc6858c09ada3bc5
2026-01-16T00:00:00-05:00
Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models
arXiv:2601.09719v1 Announce Type: new Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective transfer learning. However, Pre-LN is inefficient due to repeated statistical calculations and suffers from the curse of depth. As ...
https://arxiv.org/abs/2601.09719
Academic Papers
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b9306b4b419280ca1475281b55234eca2c2a95b221b1784442280ebd8a1bef9f
2026-01-16T00:00:00-05:00
Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering
arXiv:2601.09720v1 Announce Type: new Abstract: Question answering (QA) systems are increasingly deployed across domains. However, their reliability is undermined when retrieved evidence is incomplete, noisy, or uncertain. Existing knowledge graph (KG) based QA frameworks typically represent facts as static and determi...
https://arxiv.org/abs/2601.09720
Academic Papers
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edd8024a0b7e012acea66ba54d127c71a8266a43690503e72ad7983c496c99d6
2026-01-16T00:00:00-05:00
Cross-Platform Evaluation of Large Language Model Safety in Pediatric Consultations: Evolution of Adversarial Robustness and the Scale Paradox
arXiv:2601.09721v1 Announce Type: new Abstract: Background Large language models (LLMs) are increasingly deployed in medical consultations, yet their safety under realistic user pressures remains understudied. Prior assessments focused on neutral conditions, overlooking vulnerabilities from anxious users challenging sa...
https://arxiv.org/abs/2601.09721
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7d30733019501884f2c02cac87149b84c4237cf7c267d378e714255b15d9ea08
2026-01-16T00:00:00-05:00
ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language
arXiv:2601.09722v1 Announce Type: new Abstract: In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teacher model to distill the expert knowledge needed for tagging medical texts in Polish. This work is part of a larger project called A...
https://arxiv.org/abs/2601.09722
Academic Papers
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0a6bfa2a53bf2f17be0e0ab89ef81e22584a70040ea138fc45945c0acfbc3f00
2026-01-16T00:00:00-05:00
SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels
arXiv:2601.09723v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. ...
https://arxiv.org/abs/2601.09723
Academic Papers
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066930686448308e7e58024254c7cb797800dd7fc1badd9933dac2ef1408a757
2026-01-16T00:00:00-05:00
Syntactic Framing Fragility: An Audit of Robustness in LLM Ethical Decisions
arXiv:2601.09724v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in consequential decision-making settings, yet their robustness to benign prompt variation remains underexplored. In this work, we study whether LLMs maintain consistent ethical judgments across logically equivalent b...
https://arxiv.org/abs/2601.09724
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5c22c8c5dc734977d6931f7dc995e9d97a3ecb00171e3e1ef6e83811bfb46b84
2026-01-16T00:00:00-05:00
Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation
arXiv:2601.09725v1 Announce Type: new Abstract: Punctuation plays a critical role in resolving semantic and structural ambiguity in written language. Machine Translation (MT) systems are now widely applied across diverse domains and languages, including many low-resource settings. In this work, we focus on Marathi, a l...
https://arxiv.org/abs/2601.09725
Academic Papers
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1efb8eb0cb08e7cd2b2c950846c57accb6d3356406dbe9884dc557bb53d7d9a0
2026-01-16T00:00:00-05:00
Forgetting as a Feature: Cognitive Alignment of Large Language Models
arXiv:2601.09726v1 Announce Type: new Abstract: Large Language Models (LLMs) are often evaluated against ideals of perfect Bayesian inference, yet growing evidence suggests that their in-context reasoning exhibits systematic forgetting of past information. Rather than viewing this behavior as a limitation, we reinterpr...
https://arxiv.org/abs/2601.09726
Academic Papers
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08d9784e872721d51bfe2e06c4cd96f361b1c36f94c6664fc2d5fef34c79d392
2026-01-16T00:00:00-05:00
SciNets: Graph-Constrained Multi-Hop Reasoning for Scientific Literature Synthesis
arXiv:2601.09727v1 Announce Type: new Abstract: Cross-domain scientific synthesis requires connecting mechanistic explanations across fragmented literature, a capability that remains challenging for both retrieval-based systems and unconstrained language models. While recent work has applied large language models to sc...
https://arxiv.org/abs/2601.09727
Academic Papers
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7983aad81cebe2f6edf60e199b40d209e392ed5510d284daaf965f87769f93c4
2026-01-16T00:00:00-05:00
Eliminating Agentic Workflow for Introduction Generation with Parametric Stage Tokens
arXiv:2601.09728v1 Announce Type: new Abstract: In recent years, using predefined agentic workflows to guide large language models (LLMs) for literature classification and review has become a research focus. However, writing research introductions is more challenging. It requires rigorous logic, coherent structure, and...
https://arxiv.org/abs/2601.09728
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570a08cfec0947d9d72ae1b80596756fc8d853562edafe677ec7e24ae3da8ab4
2026-01-16T00:00:00-05:00
Enhancing Business Analytics through Hybrid Summarization of Financial Reports
arXiv:2601.09729v1 Announce Type: new Abstract: Financial reports and earnings communications contain large volumes of structured and semi structured information, making detailed manual analysis inefficient. Earnings conference calls provide valuable evidence about a firm's performance, outlook, and strategic prioritie...
https://arxiv.org/abs/2601.09729
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1bf09099237eef164d8b8b0eac43f1ed623c79bad5d3fda1b6e963efa5e6791b
2026-01-16T00:00:00-05:00
Clinical Document Metadata Extraction: A Scoping Review
arXiv:2601.09730v1 Announce Type: new Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time chall...
https://arxiv.org/abs/2601.09730
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ed2d4a2507f0fad7cc6375a8920450531d124d9b8eb706dcbfbb4529a61aabeb
2026-01-16T00:00:00-05:00
Geometric Patterns of Meaning: A PHATE Manifold Analysis of Multi-lingual Embeddings
arXiv:2601.09731v1 Announce Type: new Abstract: We introduce a multi-level analysis framework for examining semantic geometry in multilingual embeddings, implemented through Semanscope (a visualization tool that applies PHATE manifold learning across four linguistic levels). Analysis of diverse datasets spanning sub-ch...
https://arxiv.org/abs/2601.09731
Academic Papers
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a4aa0a99c07ebfcde800fa8c31fe3b3c886bc0e712e5e310f37a5509856a5467
2026-01-16T00:00:00-05:00
Benchmarking Cross-Lingual Semantic Alignment in Multilingual Embeddings
arXiv:2601.09732v1 Announce Type: new Abstract: With hundreds of multilingual embedding models available, practitioners lack clear guidance on which provide genuine cross-lingual semantic alignment versus task performance through language-specific patterns. Task-driven benchmarks (MTEB) may mask fundamental alignment s...
https://arxiv.org/abs/2601.09732
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391803660a10f81d135cba66f1a5624be0399345a2ef6005ea78367f7495867c
2026-01-16T00:00:00-05:00
Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets
arXiv:2601.09733v1 Announce Type: new Abstract: The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often rely on heuristic aggregation without a systematic understanding of how individual samples ...
https://arxiv.org/abs/2601.09733
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a3fdbb4f4029c85471d983232d362ed37eee368180bf255a04b6f865aa398008
2026-01-16T00:00:00-05:00
From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis
arXiv:2601.09734v1 Announce Type: new Abstract: Hallucinations in Large Language Models (LLMs), defined as the generation of content inconsistent with facts or context, represent a core obstacle to their reliable deployment in critical domains. Current research primarily focuses on binary "detection" approaches that, w...
https://arxiv.org/abs/2601.09734
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5e07b775ab1f3af6d55a56667fb6015375d2f0478ecfc8471b5ce2e1b6748e3f
2026-01-16T00:00:00-05:00
Multiverse: Transactional Memory with Dynamic Multiversioning
arXiv:2601.09735v1 Announce Type: new Abstract: Software transactional memory (STM) allows programmers to easily implement concurrent data structures. STMs simplify atomicity. Recent STMs can achieve good performance for some workloads but they have some limitations. In particular, STMs typically cannot support long-ru...
https://arxiv.org/abs/2601.09735
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fe6949ce549adf3c949311e7a56182244b6f6ed815484102d7b8be3afb1c47bb
2026-01-16T00:00:00-05:00
Reinforced Linear Genetic Programming
arXiv:2601.09736v1 Announce Type: new Abstract: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to...
https://arxiv.org/abs/2601.09736
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5426f7fc1c3dfe51f08a5fbb8362e861afa904a0fccb214d5357fec23c5aa767
2026-01-16T00:00:00-05:00
Filtering for Copyright Enforcement in Europe after the Sabam cases
arXiv:2601.09739v1 Announce Type: new Abstract: Sabam, a Belgian collective rights management organisation, wanted an internet access provider and a social network site to install a filter system to enforce copyrights. In two recent judgments, the Court of Justice of the European Union decided that the social network s...
https://arxiv.org/abs/2601.09739
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ccacdf71db9aa0676713a3d5ccd309b1abdd742eb3827a2ac15d0a1408a1b3c3
2026-01-16T00:00:00-05:00
Formal Safety Guarantees for Autonomous Vehicles using Barrier Certificates
arXiv:2601.09740v1 Announce Type: new Abstract: Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonom...
https://arxiv.org/abs/2601.09740
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8382e44b6fec1d233aae20fe822eea0162c39aea8dc31dd2af635e4ef4e7c302
2026-01-16T00:00:00-05:00
Putting green software principles into practice
arXiv:2601.09741v1 Announce Type: new Abstract: The need and theoretical methods for measuring and reducing CO2 emitted by computing systems are well understood, but real-world examples are still limited. We describe a journey towards green software for a live product running on a public cloud. We discuss practical sol...
https://arxiv.org/abs/2601.09741
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d1642c5b03550e1e53e558402c9d916675b4b402bdd7007072e82a9b41290413
2026-01-16T00:00:00-05:00
Adaptive Orchestration: Scalable Self-Evolving Multi-Agent Systems
arXiv:2601.09742v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents, they face a critical scalability bottleneck known as the "Generalization-Specialization Dilemma." Monolithic agents equipped with extensive toolkits suffer from context pollution and attention...
https://arxiv.org/abs/2601.09742
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54381d839bbaf736021cbf34de34bfb18301b68e403fcebb3e0d8ea8ddcd2acd
2026-01-16T00:00:00-05:00
A Governance Model for IoT Data in Global Manufacturing
arXiv:2601.09744v1 Announce Type: new Abstract: Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic ...
https://arxiv.org/abs/2601.09744
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7eecc221e48f128e13f24136a734db237f9cf7df0aee13de72281f3b6c6afca3
2026-01-16T00:00:00-05:00
Enhancing Formal Software Specification with Artificial Intelligence
arXiv:2601.09745v1 Announce Type: new Abstract: Formal software specification is known to enable early error detection and explicit invariants, yet it has seen limited industrial adoption due to its high notation overhead and the expertise required to use traditional formal languages. This paper presents a case study s...
https://arxiv.org/abs/2601.09745
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270262e2577aefc32c0af52b6a2bb2acbf9adc85998f5b58df0c157e75e151a1
2026-01-16T00:00:00-05:00
Multi-Agent Cooperative Learning for Robust Vision-Language Alignment under OOD Concepts
arXiv:2601.09746v1 Announce Type: new Abstract: This paper introduces a novel Multi-Agent Cooperative Learning (MACL) framework to address cross-modal alignment collapse in vision-language models when handling out-of-distribution (OOD) concepts. Four core agents, including image, text, name, and coordination agents, co...
https://arxiv.org/abs/2601.09746
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cb9d2b7aa0c1ffe3be384818c036dfeb509189dc873e59f2d38ff775c600788e
2026-01-16T00:00:00-05:00
Instalaci\'on, configuraci\'on y utilizaci\'on de un nodo Bitcoin en Linux
arXiv:2601.09748v1 Announce Type: new Abstract: This paper documents the installation, configuration, and operation of a full Bitcoin node in a Linux environment, from manual compilation of the source code to complete synchronization with the network. The technical phases of the process are described, the main files ge...
https://arxiv.org/abs/2601.09748
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61e3f783008550a23952e0f75fc68919ead6339ef62c534e3374242c3cc94f50
2026-01-16T00:00:00-05:00
R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation
arXiv:2601.09749v1 Announce Type: new Abstract: Large Action Models (LAMs) extend large language models by enabling autonomous decision-making and tool execution, making them promising for automating scientific workflows. However, scientific workflows impose strict requirements on reproducibility, auditability, and det...
https://arxiv.org/abs/2601.09749
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9848f89eaf3ccaaabea08ecb132a74d4b9f863571c9514e19ae3e572dcf02917
2026-01-16T00:00:00-05:00
SAGE: Tool-Augmented LLM Task Solving Strategies in Scalable Multi-Agent Environments
arXiv:2601.09750v1 Announce Type: new Abstract: Large language models (LLMs) have proven to work well in question-answering scenarios, but real-world applications often require access to tools for live information or actuation. For this, LLMs can be extended with tools, which are often defined in advance, also allowing...
https://arxiv.org/abs/2601.09750
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668f8e828a7ff18dd200995258b88b74079d7a9cd7f314908b7629611ef2809a
2026-01-16T00:00:00-05:00
Critically Engaged Pragmatism: A Scientific Norm and Social, Pragmatist Epistemology for AI Science Evaluation Tools
arXiv:2601.09753v1 Announce Type: new Abstract: Crises in peer review capacity, study replication, and AI-fabricated science have intensified interest in automated tools for assessing scientific research. However, the scientific community has a history of decontextualizing and repurposing credibility markers in inapt w...
https://arxiv.org/abs/2601.09753
Academic Papers
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45afa25e128bb4b7ad94d621aa2cffb1292cc33198cb447084fd529881b88fa5
2026-01-16T00:00:00-05:00
Heterogeneous computing platform for real-time robotics
arXiv:2601.09755v1 Announce Type: new Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of...
https://arxiv.org/abs/2601.09755
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3c45b9746099a95dcff9818f5e45a5589d5a3bcb7b12511f1e58ba8500b7dad8
2026-01-16T00:00:00-05:00
Synthetic Data for Veterinary EHR De-identification: Benefits, Limits, and Safety Trade-offs Under Fixed Compute
arXiv:2601.09756v1 Announce Type: new Abstract: Veterinary electronic health records (vEHRs) contain privacy-sensitive identifiers that limit secondary use. While PetEVAL provides a benchmark for veterinary de-identification, the domain remains low-resource. This study evaluates whether large language model (LLM)-gener...
https://arxiv.org/abs/2601.09756
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985935610148b74f13bae9adf5d40c9d00ba9a05931f17e75c89563570175556
2026-01-16T00:00:00-05:00
Democracy and Distrust in an Era of Artificial Intelligence
arXiv:2601.09757v1 Announce Type: new Abstract: This essay examines how judicial review should adapt to address challenges posed by artificial intelligence decision-making, particularly regarding minority rights and interests. As I argue in this essay, the rise of three trends-privatization, prediction, and automation ...
https://arxiv.org/abs/2601.09757
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7d05e0171d956f518ea19c8e6032015be0302558d9570902afb5d8fc7a8665b7
2026-01-16T00:00:00-05:00
Investigating Tool-Memory Conflicts in Tool-Augmented LLMs
arXiv:2601.09760v1 Announce Type: new Abstract: Tool-augmented large language models (LLMs) have powered many applications. However, they are likely to suffer from knowledge conflict. In this paper, we propose a new type of knowledge conflict -- Tool-Memory Conflict (TMC), where the internal parametric knowledge contra...
https://arxiv.org/abs/2601.09760
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7e09fba1bcd229c81560a6e193d0aa86684702aaa4a9463af8bb46e7d388f8ec
2026-01-16T00:00:00-05:00
Explicating Tacit Regulatory Knowledge from LLMs to Auto-Formalize Requirements for Compliance Test Case Generation
arXiv:2601.09762v1 Announce Type: new Abstract: Compliance testing in highly regulated domains is crucial but largely manual, requiring domain experts to translate complex regulations into executable test cases. While large language models (LLMs) show promise for automation, their susceptibility to hallucinations limit...
https://arxiv.org/abs/2601.09762
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e4fb5e1229937260b0f32b4bacb5e84758bbb9a6f48e6f2d8d26b4051f956100
2026-01-16T00:00:00-05:00
AI Survival Stories: a Taxonomic Analysis of AI Existential Risk
arXiv:2601.09765v1 Announce Type: new Abstract: Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two premise argument that AI systems pos...
https://arxiv.org/abs/2601.09765
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b22a48d91b4767300c8db5cc9a254d8bfe710e2f725de58ff2671db776682ad3
2026-01-16T00:00:00-05:00
GUI-Eyes: Tool-Augmented Perception for Visual Grounding in GUI Agents
arXiv:2601.09770v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) and reinforcement learning (RL) have driven progress in GUI automation. However, most existing methods rely on static, one-shot visual inputs and passive perception, lacking the ability to adaptively determine when, whether...
https://arxiv.org/abs/2601.09770
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8876bceae5ea35059f32e3fa5b391bcd60e813e40a052139c8816e0fc528fc28
2026-01-16T00:00:00-05:00
PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation
arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-...
https://arxiv.org/abs/2601.09771
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464b67b89b2badcc08b35e995381715b42b4447d1ad58681c3f03d9960e8b83c
2026-01-16T00:00:00-05:00
Antisocial behavior towards large language model users: experimental evidence
arXiv:2601.09772v1 Announce Type: new Abstract: The rapid spread of large language models (LLMs) has raised concerns about the social reactions they provoke. Prior research documents negative attitudes toward AI users, but it remains unclear whether such disapproval translates into costly action. We address this questi...
https://arxiv.org/abs/2601.09772
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3457a017822a3fd91bcd794fda7b322730197e1732be636293caced729706853
2026-01-16T00:00:00-05:00
Enhancing LUT-based Deep Neural Networks Inference through Architecture and Connectivity Optimization
arXiv:2601.09773v1 Announce Type: new Abstract: Deploying deep neural networks (DNNs) on resource-constrained edge devices such as FPGAs requires a careful balance among latency, power, and hardware resource usage, while maintaining high accuracy. Existing Lookup Table (LUT)-based DNNs -- such as LogicNets, PolyLUT, an...
https://arxiv.org/abs/2601.09773
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938b91f1418f8943bd2bf569391120bc7568592f8c015779eb68b35c533c89b0
2026-01-16T00:00:00-05:00
The Geometry of Thought: Disclosing the Transformer as a Tropical Polynomial Circuit
arXiv:2601.09775v1 Announce Type: new Abstract: We prove that the Transformer self-attention mechanism in the high-confidence regime ($\beta \to \infty$, where $\beta$ is an inverse temperature) operates in the tropical semiring (max-plus algebra). In particular, we show that taking the tropical limit of the softmax at...
https://arxiv.org/abs/2601.09775
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8c092b32907408dfdfc117e6cee3e1797fe9cfe9a52215598edcee8062677e27
2026-01-16T00:00:00-05:00
TimeSAE: Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
arXiv:2601.09776v1 Announce Type: new Abstract: As black box models and pretrained models gain traction in time series applications, understanding and explaining their predictions becomes increasingly vital, especially in high-stakes domains where interpretability and trust are essential. However, most of the existing ...
https://arxiv.org/abs/2601.09776
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5f25f42b69479d8cc8fd5d0d47ecd3e2bbba1eec5e19e8b7ff8e35c8d28568be
2026-01-16T00:00:00-05:00
Improving Chain-of-Thought for Logical Reasoning via Attention-Aware Intervention
arXiv:2601.09805v1 Announce Type: new Abstract: Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong lo...
https://arxiv.org/abs/2601.09805
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66b3b5c48e792f396c1a660f7c6d07f04091f13d8870de0c187319a1b16c924a
2026-01-16T00:00:00-05:00
Diffusion-Driven Deceptive Patches: Adversarial Manipulation and Forensic Detection in Facial Identity Verification
arXiv:2601.09806v1 Announce Type: new Abstract: This work presents an end-to-end pipeline for generating, refining, and evaluating adversarial patches to compromise facial biometric systems, with applications in forensic analysis and security testing. We utilize FGSM to generate adversarial noise targeting an identity ...
https://arxiv.org/abs/2601.09806
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7801fecc1ad0ba89e6cb980d7e1d5501f1651cfcec13f1355e9ec6293f1b090d
2026-01-16T00:00:00-05:00
From Dynamic to Lexical: A Comparative Exploration of Scoping Rules in SAS and R
arXiv:2601.09808v1 Announce Type: new Abstract: Variable scoping dictates how and where variables are accessible within programming languages, playing a crucial role in code efficiency and organization. This paper examines the distinct scoping rules in SAS and R, focusing on SAS's dynamic scoping and R's lexical scopin...
https://arxiv.org/abs/2601.09808
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4a46c63e6638e6a0b65cb455448829a874fa0e83761ed6b8f04599c5b88fddf5
2026-01-16T00:00:00-05:00
QFed: Parameter-Compact Quantum-Classical Federated Learning
arXiv:2601.09809v1 Announce Type: new Abstract: Organizations and enterprises across domains such as healthcare, finance, and scientific research are increasingly required to extract collective intelligence from distributed, siloed datasets while adhering to strict privacy, regulatory, and sovereignty requirements. Fed...
https://arxiv.org/abs/2601.09809
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ef18025d45501c9a6313bb78b7cedc0251f88e22ac70a1eeac602209560e92de
2026-01-16T00:00:00-05:00
Learning Ecological and Epidemic Processes using Neural ODEs, Kolmogorov-Arnold Network ODEs and SINDy
arXiv:2601.09811v1 Announce Type: new Abstract: We consider epidemic and ecological models to investigate their coupled dynamics. Starting with the classical Susceptible-Infected-Recovered (SIR) model for basic epidemic behavior and the predator-prey (Lotka-Volterra, LV) system for ecological interactions, we then comb...
https://arxiv.org/abs/2601.09811
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6238812d797753652e7663203e882ffc824f1ced2514d94f78b2883b1caa88e3
2026-01-16T00:00:00-05:00
LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving
arXiv:2601.09812v1 Announce Type: new Abstract: Accurately localizing 3D objects like pedestrians, cyclists, and other vehicles is essential in Autonomous Driving. To ensure high detection performance, Autonomous Vehicles complement RGB cameras with LiDAR sensors, but effectively combining these data sources for 3D obj...
https://arxiv.org/abs/2601.09812
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a5a736040f57346535a5416843ebfd2ec36f56752bd48a03f31fd1892316be1a
2026-01-16T00:00:00-05:00
Explainable Deep Learning for Pediatric Pneumonia Detection in Chest X-Ray Images
arXiv:2601.09814v1 Announce Type: new Abstract: Background: Pneumonia remains a leading cause of morbidity and mortality among children worldwide, emphasizing the need for accurate and efficient diagnostic support tools. Deep learning has shown strong potential in medical image analysis, particularly for chest X-ray in...
https://arxiv.org/abs/2601.09814
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916cc8df2e9799849cc9d0884908bb796a69efb52a61c21e0b2aadf65399dde4
2026-01-16T00:00:00-05:00
LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
arXiv:2601.09822v1 Announce Type: new Abstract: Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their ap...
https://arxiv.org/abs/2601.09822
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efecccfdb239bf259624b7fa0de19def2a1f55ac4d30c2ab3bd7c1065456cb9f
2026-01-16T00:00:00-05:00
NanoSD: Edge Efficient Foundation Model for Real Time Image Restoration
arXiv:2601.09823v1 Announce Type: new Abstract: Latent diffusion models such as Stable Diffusion 1.5 offer strong generative priors that are highly valuable for image restoration, yet their full pipelines remain too computationally heavy for deployment on edge devices. Existing lightweight variants predominantly compre...
https://arxiv.org/abs/2601.09823
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a2920813aeae1f14fd4790da6321766ba0b239ad5e76ca413425f1e39bb35fc9
2026-01-16T00:00:00-05:00
Eluder dimension: localise it!
arXiv:2601.09825v1 Announce Type: new Abstract: We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot lead to first-order regret bounds. To address this, we introduce a localisation method for the eluder dimension; our analys...
https://arxiv.org/abs/2601.09825
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57095d57d9ea62512af8d72b30746f8f7507119ab632625bb68050ee1429f85c
2026-01-16T00:00:00-05:00
UniHash: Unifying Pointwise and Pairwise Hashing Paradigms for Seen and Unseen Category Retrieval
arXiv:2601.09828v1 Announce Type: new Abstract: Effective retrieval across both seen and unseen categories is crucial for modern image retrieval systems. Retrieval on seen categories ensures precise recognition of known classes, while retrieval on unseen categories promotes generalization to novel classes with limited ...
https://arxiv.org/abs/2601.09828
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c8744d13587fc9ba0ae413d0e0b211122c66bca2df004a1f2b32b417f0010904
2026-01-16T00:00:00-05:00
A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch
arXiv:2601.09831v1 Announce Type: new Abstract: In this work, we provide a new convergence theory for plug-and-play proximal gradient descent (PnP-PGD) under prior mismatch where the denoiser is trained on a different data distribution to the inference task at hand. To the best of our knowledge, this is the first conve...
https://arxiv.org/abs/2601.09831
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b5dfb04844b91397e5634c28f845117ebdc8d8faaa868db3337dfb29b91bfa69
2026-01-16T00:00:00-05:00
Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects
arXiv:2601.09832v1 Announce Type: new Abstract: Following code style conventions in software projects is essential for maintaining overall code quality. Adhering to these conventions improves maintainability, understandability, and extensibility. Additionally, following best practices during software development enhanc...
https://arxiv.org/abs/2601.09832
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fb0f3ea06ca6d77ac5e6cccd9ba2128775b74a9cffa278561fddf30fc053c704
2026-01-16T00:00:00-05:00
Stable and Explainable Personality Trait Evaluation in Large Language Models with Internal Activations
arXiv:2601.09833v1 Announce Type: new Abstract: Evaluating personality traits in Large Language Models (LLMs) is key to model interpretation, comparison, and responsible deployment. However, existing questionnaire-based evaluation methods exhibit limited stability and offer little explainability, as their results are h...
https://arxiv.org/abs/2601.09833
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5cae24ea5f1b6e6109aab9fbdc893008ad898d0cb275a75aef873891c673909f
2026-01-16T00:00:00-05:00
A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts
arXiv:2601.09836v1 Announce Type: new Abstract: Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability (SWC-12...
https://arxiv.org/abs/2601.09836
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8e9d2c10c07e3450570c33857d33aca4424c262c0666ee3be8a4c6dd37ef69b7
2026-01-16T00:00:00-05:00
Interprofessional and Agile Development of Mobirobot: A Socially Assistive Robot for Pediatric Therapy Across Clinical and Therapeutic Settings
arXiv:2601.09838v1 Announce Type: new Abstract: Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper present...
https://arxiv.org/abs/2601.09838
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71155163ebc6f6f9c8eb020a1759b0489affd9f6e44637bd00c9d4679eb33321
2026-01-16T00:00:00-05:00
Lazy Evaluation: A Comparative Analysis of SAS MACROs and R Functions
arXiv:2601.09839v1 Announce Type: new Abstract: Lazy evaluation is a powerful technique that can optimize code execution by deferring evaluations until their results are required, thus enhancing efficiency. In most modern programming languages, like R, lazy evaluation is commonly applied to function arguments. However,...
https://arxiv.org/abs/2601.09839
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ded2f522da06a0a816f73cb30fc4be5e9a95e5c0ca86fae9356a5b2206a7c2c5
2026-01-16T00:00:00-05:00
A pipeline for enabling path-specific causal fairness in observational health data
arXiv:2601.09841v1 Announce Type: new Abstract: When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare biases. Although many definitions of fairness exist, we focus on path-specific causal fairne...
https://arxiv.org/abs/2601.09841
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2fc9219d751e88f4f2a1c04948c45c579999f1167a979fa0152751b089994b16
2026-01-16T00:00:00-05:00
On Fun for Teaching Large Programming Courses
arXiv:2601.09842v1 Announce Type: new Abstract: Teaching software development basics to hundreds of students in a frontal setting is cost-efficient and thus still common in universities. However, in a large lecture hall, students can easily get bored, distracted, and disengaged. The frontal setting can also frustrate l...
https://arxiv.org/abs/2601.09842
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1eb8abe94c6dde75f9e232b8b44e3a03cfc5b7d5f104514e0714787ba8fda45b
2026-01-16T00:00:00-05:00
Strategies of cooperation and defection in five large language models
arXiv:2601.09849v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed to support human decision-making. This use of LLMs has concerning implications, especially when their prescriptions affect the welfare of others. To gauge how LLMs make social decisions, we explore whether five leadin...
https://arxiv.org/abs/2601.09849
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b5228cee1c8b52c1ff8dad90b691b0afa204ab8654255275345612b155c055af
2026-01-16T00:00:00-05:00
ViSIL: Unified Evaluation of Information Loss in Multimodal Video Captioning
arXiv:2601.09851v1 Announce Type: new Abstract: Multimodal video captioning condenses dense footage into a structured format of keyframes and natural language. By creating a cohesive multimodal summary, this approach anchors generative AI in rich semantic evidence and serves as a lightweight proxy for high-efficiency r...
https://arxiv.org/abs/2601.09851
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7512d88862c4453b5070dca3a4e5a0ef22f0ce791760e8b42c0965d9bc990080
2026-01-16T00:00:00-05:00
Bears, all bears, and some bears. Language Constraints on Language Models' Inductive Inferences
arXiv:2601.09852v1 Announce Type: new Abstract: Language places subtle constraints on how we make inductive inferences. Developmental evidence by Gelman et al. (2002) has shown children (4 years and older) to differentiate among generic statements ("Bears are daxable"), universally quantified NPs ("all bears are daxabl...
https://arxiv.org/abs/2601.09852
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c91d109e52f96035f5e8f5d54d838d6c3b6f1172828ffd449e02a7d0224e26cb
2026-01-16T00:00:00-05:00
MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication
arXiv:2601.09853v1 Announce Type: new Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises. In such cases, safe medical communication typically involves redirection: addressing the implicit misconception and then responding to the underlying patient context, rath...
https://arxiv.org/abs/2601.09853
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704246adde3cca462a8bbe2c33ff9f9e06d58353cf55460fb27d507d8b3d0ac1
2026-01-16T00:00:00-05:00
Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models
arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reas...
https://arxiv.org/abs/2601.09855
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b6d564e15edeec36ecc2e30d8b0376d42a4a88ea533b1451bbad025eb36e3ae5
2026-01-16T00:00:00-05:00
How Human Motion Prediction Quality Shapes Social Robot Navigation Performance in Constrained Spaces
arXiv:2601.09856v1 Announce Type: new Abstract: Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such setti...
https://arxiv.org/abs/2601.09856
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390fbb43b095709deda3641b4a72a9142922593c8c9272b9d075719c598cd5ae
2026-01-16T00:00:00-05:00
OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing
arXiv:2601.09858v1 Announce Type: new Abstract: Scientific paper generation requires document-level planning and factual grounding, but current large language models, despite their strong local fluency, often fail in global structure, input coverage, and citation consistency. We present a reinforcement learning framewo...
https://arxiv.org/abs/2601.09858
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5bc525c3d57f65d854aff13c6d8e2443455a1195789e24ca27b61a0df5052e67
2026-01-16T00:00:00-05:00
Breaking the Limits of Open-Weight CLIP: An Optimization Framework for Self-supervised Fine-tuning of CLIP
arXiv:2601.09859v1 Announce Type: new Abstract: CLIP has become a cornerstone of multimodal representation learning, yet improving its performance typically requires a prohibitively costly process of training from scratch on billions of samples. We ask a different question: Can we improve the performance of open-weight...
https://arxiv.org/abs/2601.09859
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