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24da37ca0b7f746b48fd806251fd764b5d3651e6ef23f70b3a1d8ddd765c9525 | 2026-01-29T07:00:02+00:00 | The great government brain drain | Science, Volume 391, Issue 6784, Page 428-429, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8893?af=R | Academic Papers | svg |
714da92e83f9c3aa915a4ccf916e6df78ce0582c7ed566bede289632bd4e079c | 2026-01-29T07:00:02+00:00 | Earthquake sensors buried in the quietest spot on Earth | Science, Volume 391, Issue 6784, Page 430-431, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8894?af=R | Academic Papers | svg |
b3c8481dbe2238abb2e154170b4ee1b666045fe60c9ed8dd5b45ed8487cf62a0 | 2026-01-29T07:00:02+00:00 | Leading preprint server clamps down on ‘AI slop’ | Science, Volume 391, Issue 6784, Page 432-433, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8896?af=R | Academic Papers | svg |
031f10656a8ad04bc691bd6f0aaa29f303bf59092d8090ed872f7f92fabd4938 | 2026-01-29T07:00:02+00:00 | Magnetic fields cause fluorescent proteins to dim | Science, Volume 391, Issue 6784, Page 434-435, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8898?af=R | Academic Papers | svg |
d7a3c33de78f7cf5a7567a20dc6973c12b5a1af3e5982502f1a48923893dadd1 | 2026-01-29T07:00:02+00:00 | Oil helped build Venezuela’s science. Can oil now revive it? | Science, Volume 391, Issue 6784, Page 431-432, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8895?af=R | Academic Papers | svg |
86c77332adf5a94f859e547309e5b94f1118e1ca2eb2f4b42251928028fb6a89 | 2026-01-29T08:00:00+00:00 | The ‘undone science’ of opioid overdose deaths | Science, Volume 391, Issue 6784, January 2026. | https://www.science.org/doi/abs/10.1126/science.aee8306?af=R | Academic Papers | svg |
f7f8695d9aa87a7612793a12aadfff4bb0968500762d9ab5eab292bc31678150 | 2026-01-29T07:00:02+00:00 | China turns the tables in biotech | Science, Volume 391, Issue 6784, Page 427-427, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef7757?af=R | Academic Papers | svg |
b7def46f96f21b07e57e24635aa84d12f91f1d7ab0617688ff9ea93d007ea93f | 2026-01-29T07:00:02+00:00 | Public access’s next frontier | Science, Volume 391, Issue 6784, Page 522-524, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef7772?af=R | Academic Papers | svg |
c32d9c7a24b133d226236a636ee1fc566613f4027d4f7dbfb70e59cb7f9a411d | 2026-01-29T07:00:02+00:00 | Fossil energy minimum viable scale | Science, Volume 391, Issue 6784, Page 449-452, January 2026. | https://www.science.org/doi/abs/10.1126/science.aea0972?af=R | Academic Papers | svg |
75fec4a8b26e61d4732b8efef07aba5a8361fb5945e75592017962be3c8895d3 | 2026-01-29T07:00:02+00:00 | In Science Journals | Science, Volume 391, Issue 6784, Page 466-468, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef8478?af=R | Academic Papers | svg |
427e9c1ae9893450a609c388869b7f57ed5be4952b4dca6ef70ac9ba2b368446 | 2026-02-02T00:00:00-05:00 | Screen, Match, and Cache: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation | arXiv:2601.22160v1 Announce Type: new Abstract: Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies while preserving frame quality remains challenging. Inspired by the human ability to leverage past observations for interpreting o... | https://arxiv.org/abs/2601.22160 | Academic Papers | svg |
dce0f12b2384d50a25bc881f264ef8e6ec3b326494e7e22ac7d07fb752a52c9b | 2026-02-02T00:00:00-05:00 | Attention Isn't All You Need for Emotion Recognition:Domain Features Outperform Transformers on the EAV Dataset | arXiv:2601.22161v1 Announce Type: new Abstract: We present a systematic study of multimodal emotion recognition using the EAV dataset, investigating whether complex attention mechanisms improve performance on small datasets. We implement three model categories: baseline transformers (M1), novel factorized attention mec... | https://arxiv.org/abs/2601.22161 | Academic Papers | svg |
8adafd44f97bad762b999eca1aa2c458fe6a825095c07b553a529b7b4f8cef22 | 2026-02-02T00:00:00-05:00 | Do Open-Vocabulary Detectors Transfer to Aerial Imagery? A Comparative Evaluation | arXiv:2601.22164v1 Announce Type: new Abstract: Open-vocabulary object detection (OVD) enables zero-shot recognition of novel categories through vision-language models, achieving strong performance on natural images. However, transferability to aerial imagery remains unexplored. We present the first systematic benchmar... | https://arxiv.org/abs/2601.22164 | Academic Papers | svg |
361f8707a7845fd4d287eba79a24ea0db5eb86c64c68dee807cffb82d0c4f875 | 2026-02-02T00:00:00-05:00 | In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement | arXiv:2601.22169v1 Announce Type: new Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e., text written under the influence of alcohol, as a driver for safety failures in large language models (LLMs). We investigate th... | https://arxiv.org/abs/2601.22169 | Academic Papers | svg |
cfb467133f640d5cfb918de019344ff9edf2b100ef6410fe722f75b5de0882c6 | 2026-02-02T00:00:00-05:00 | Large Language Models: A Mathematical Formulation | arXiv:2601.22170v1 Announce Type: new Abstract: Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summarization, providing recommendations, writing software and solving quantitative problems. We provide a mathematical framework for LLMs ... | https://arxiv.org/abs/2601.22170 | Academic Papers | svg |
4cda64543c8e367cbe6d6bc78814cb86e2c5e464a88d6ac7be79ccc41742d288 | 2026-02-02T00:00:00-05:00 | On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators | arXiv:2601.22174v1 Announce Type: new Abstract: In this paper, we investigate Durrmeyer-type generalizations of maximum-minimum neural network operators. The primary objective of this study is to establish the convergence of these operators in the $L^{p}$ norm for functions $f\in L^{p}([a,b],[0,1])$ with $1\leq p<\inft... | https://arxiv.org/abs/2601.22174 | Academic Papers | svg |
ab6d8aeb1bebe4e78c9c26ac3a49986398870426d84f6f2630c279a4ec42cc23 | 2026-02-02T00:00:00-05:00 | An innovating approach to teaching applied to database design. Improvement of Action Learning in Lifelong Learning | arXiv:2601.22175v1 Announce Type: new Abstract: For now 10 years, the Action Learning has allowed employees of University of Angers, private and public Companies to be initiated with the design of database, on projects financed by professional structures. These innovating training periods are carried out within the fra... | https://arxiv.org/abs/2601.22175 | Academic Papers | svg |
9ea95b3a9707e92baf43a70ea1f61d750be8e54b7c99f237ccd6963496cf3a02 | 2026-02-02T00:00:00-05:00 | Discovering High-utility Sequential Rules with Increasing Utility Ratio | arXiv:2601.22178v1 Announce Type: new Abstract: Utility-driven mining is an essential task in data science, as it can provide deeper insight into the real world. High-utility sequential rule mining (HUSRM) aims at discovering sequential rules with high utility and high confidence. It can certainly provide reliable info... | https://arxiv.org/abs/2601.22178 | Academic Papers | svg |
db66cfbf753353c412ea5520d352b8f43a101bd36fe842071123d97b1b53a485 | 2026-02-02T00:00:00-05:00 | High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence | arXiv:2601.22179v1 Announce Type: new Abstract: Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high utility and strong confidence, which are valuable in real-world applications. However, existing... | https://arxiv.org/abs/2601.22179 | Academic Papers | svg |
cbf3c8ea207b25430172b50d8789c87b37e735613e9af68ecc8b4523867e4d61 | 2026-02-02T00:00:00-05:00 | MrRoPE: Mixed-radix Rotary Position Embedding | arXiv:2601.22181v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE)-extension refers to modifying or generalizing the Rotary Position Embedding scheme to handle longer sequences than those encountered during pre-training. However, current extension strategies are highly diverse and lack a unified theoretic... | https://arxiv.org/abs/2601.22181 | Academic Papers | svg |
aa42ad5d494c6b4fa4d78c63260ce8b30ebd0978fb8eb40b05b7b30df76ce807 | 2026-02-02T00:00:00-05:00 | ShellForge: Adversarial Co-Evolution of Webshell Generation and Multi-View Detection for Robust Webshell Defense | arXiv:2601.22182v1 Announce Type: new Abstract: Webshells remain a primary foothold for attackers to compromise servers, particularly within PHP ecosystems. However, existing detection mechanisms often struggle to keep pace with rapid variant evolution and sophisticated obfuscation techniques that camouflage malicious ... | https://arxiv.org/abs/2601.22182 | Academic Papers | svg |
3601706d6c9fb14df3f5902d962d7f9a85aa226085af6b55a101282d1bab7d85 | 2026-02-02T00:00:00-05:00 | COL-Trees: Efficient Hierarchical Object Search in Road Networks | arXiv:2601.22183v1 Announce Type: new Abstract: Location-based services rely heavily on efficient methods that search for relevant points-of-interest (POIs) near a given location. A k Nearest Neighbor (kNN) query is one such example that finds the k closest POIs from an agent's location. While most existing techniques ... | https://arxiv.org/abs/2601.22183 | Academic Papers | svg |
10308b3ad67cf9b3e530bfa5f8fa58875223e5c0acc2acc0b261b61fda4213ad | 2026-02-02T00:00:00-05:00 | Tacit Coordination of Large Language Models | arXiv:2601.22184v1 Announce Type: new Abstract: In tacit coordination games with multiple outcomes, purely rational solution concepts, such as Nash equilibria, provide no guidance for which equilibrium to choose. Shelling's theory explains how, in these settings, humans coordinate by relying on focal points: solutions ... | https://arxiv.org/abs/2601.22184 | Academic Papers | svg |
2defbd2a75a778e9e3458dc00e3fdb6203cd3223f37a0da73cc496620032f78d | 2026-02-02T00:00:00-05:00 | MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis | arXiv:2601.22185v1 Announce Type: new Abstract: The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multipl... | https://arxiv.org/abs/2601.22185 | Academic Papers | svg |
88c69ee70b9beceab1ffdcb4a911dbe1a20aed8d139a7c0f3e6c52af5973ba70 | 2026-02-02T00:00:00-05:00 | Partial Rewriting and Value Interpretation of Logically Constrained Terms (Full Version) | arXiv:2601.22191v1 Announce Type: new Abstract: Logically constrained term rewrite systems (LCTRSs) are a rewriting formalism that naturally supports built-in data structures, including integers and bit-vectors. The recent framework of existentially constrained terms and most general constrained rewriting on them (Taka... | https://arxiv.org/abs/2601.22191 | Academic Papers | svg |
53ba17fff2fec5af1819b427d82409006c7ee24335db80aab6a9eae9a619e0af | 2026-02-02T00:00:00-05:00 | Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network | arXiv:2601.22195v1 Announce Type: new Abstract: Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing larg... | https://arxiv.org/abs/2601.22195 | Academic Papers | svg |
92b445e1b372849a8d3f56f10f385461df14809aa673b7a80275050fb886700b | 2026-02-02T00:00:00-05:00 | Linux Kernel Recency Matters, CVE Severity Doesn't, and History Fades | arXiv:2601.22196v1 Announce Type: new Abstract: In 2024, the Linux kernel became its own Common Vulnerabilities and Exposures (CVE) Numbering Authority (CNA), formalizing how kernel vulnerabilities are identified and tracked. We analyze the anatomy and dynamics of kernel CVEs using metadata, associated commits, and pat... | https://arxiv.org/abs/2601.22196 | Academic Papers | svg |
346674968d9bf4d72fc2084241f6fb5ef2bacdb0e14abf157bb48166462be18e | 2026-02-02T00:00:00-05:00 | Neural Signals Generate Clinical Notes in the Wild | arXiv:2601.22197v1 Announce Type: new Abstract: Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We curate a large-scale clinical EEG dataset with $9{,}922$ reports paired with approximately $11{,}000$ ... | https://arxiv.org/abs/2601.22197 | Academic Papers | svg |
aeacaef0280b7774fe89a18c156fa35bc0a2a5da34da74952da8038f86030da2 | 2026-02-02T00:00:00-05:00 | Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey | arXiv:2601.22198v1 Announce Type: new Abstract: An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds that can bene t most from these systems. Although LiDAR place recognition is a widely used tech... | https://arxiv.org/abs/2601.22198 | Academic Papers | svg |
9e3000538e71887df455198ae065ace9e969420b7e3d853c02b9099fb832bca9 | 2026-02-02T00:00:00-05:00 | Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines | arXiv:2601.22199v1 Announce Type: new Abstract: Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the firs... | https://arxiv.org/abs/2601.22199 | Academic Papers | svg |
a8261c45a2913e574670ec308256275df4ea247d6796ca4e39fe432e19712375 | 2026-02-02T00:00:00-05:00 | The Benefit of Collective Intelligence in Community-Based Content Moderation is Limited by Overt Political Signalling | arXiv:2601.22201v1 Announce Type: new Abstract: Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Footnotes on TikTok, and Facebook's ... | https://arxiv.org/abs/2601.22201 | Academic Papers | svg |
21833f0a8525331f10147e37a07afc4c4aaf4f0114fa2d41f97c9b7ea09cadb9 | 2026-02-02T00:00:00-05:00 | FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation | arXiv:2601.22204v1 Announce Type: new Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this pa... | https://arxiv.org/abs/2601.22204 | Academic Papers | svg |
a05e8e38a5d4f2ac76ce6e95d3081b5dbd9678c13212b5c0f69aade85da6b595 | 2026-02-02T00:00:00-05:00 | Causal Imitation Learning Under Measurement Error and Distribution Shift | arXiv:2601.22206v1 Announce Type: new Abstract: We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and deployment. Such settings induce spurious state-action correlations, so standard behavioral c... | https://arxiv.org/abs/2601.22206 | Academic Papers | svg |
0886ce03d5acd3d2993f1d0a455ca4d31938dc994251af5fd8af751f1f0fe0dc | 2026-02-02T00:00:00-05:00 | Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis | arXiv:2601.22208v1 Announce Type: new Abstract: Root cause analysis (RCA) is essential for diagnosing failures within complex software systems to ensure system reliability. The highly distributed and interdependent nature of modern cloud-based systems often complicates RCA efforts, particularly for multi-hop fault prop... | https://arxiv.org/abs/2601.22208 | Academic Papers | svg |
2737cc3ea5fae2c0236f07c3af86e94f8e5d86517a055ee2b2bbcdc12454a63f | 2026-02-02T00:00:00-05:00 | Learning to Recommend Multi-Agent Subgraphs from Calling Trees | arXiv:2601.22209v1 Announce Type: new Abstract: Multi-agent systems (MAS) increasingly solve complex tasks by orchestrating agents and tools selected from rapidly growing marketplaces. As these marketplaces expand, many candidates become functionally overlapping, making selection not just a retrieval problem: beyond fi... | https://arxiv.org/abs/2601.22209 | Academic Papers | svg |
2f500ced42013670bfa9937755f74c8d04d4e6093f995b3d0ba033135da47c0e | 2026-02-02T00:00:00-05:00 | Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions | arXiv:2601.22211v1 Announce Type: new Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical. Existing approaches embed task-specifi... | https://arxiv.org/abs/2601.22211 | Academic Papers | svg |
2f76d141538cd5dfe73ed88a11f26ed0fbd8fc9c7cc3f73fed30d2f9662cd985 | 2026-02-02T00:00:00-05:00 | What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets | arXiv:2601.22218v1 Announce Type: new Abstract: Visual Question Answering (VQA) has become an important benchmark for assessing how large multimodal models (LMMs) interpret images. However, most VQA datasets focus on real-world images or simple diagrammatic analysis, with few focused on interpreting complex scientific ... | https://arxiv.org/abs/2601.22218 | Academic Papers | svg |
93fdb93932cb1a4fb25fb63cd4800abbb33c195b976ec43a51a9fad167109804 | 2026-02-02T00:00:00-05:00 | Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation | arXiv:2601.22228v1 Announce Type: new Abstract: Vision-Language Models (VLMs) perform well in 2D perception and semantic reasoning compared to their limited understanding of 3D spatial structure. We investigate this gap using relative camera pose estimation (RCPE), a fundamental vision task that requires inferring rela... | https://arxiv.org/abs/2601.22228 | Academic Papers | svg |
3a9eb252288c8725438ad9511a653a5e56483c3007bad6e04058fa8ba8b9e590 | 2026-02-02T00:00:00-05:00 | DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation | arXiv:2601.22230v1 Announce Type: new Abstract: Test-time scaling for code generation commonly relies on Best-of-N selection, in which multiple candidate solutions are sampled from a base model, and the best one is selected by an LLM judge. However, training reliable LLM judges is challenging due to severe distribution... | https://arxiv.org/abs/2601.22230 | Academic Papers | svg |
472d3c69e0a248a794f11fd401199def46e3b3053f56374937b4352e44d56bdf | 2026-02-02T00:00:00-05:00 | Geometry without Position? When Positional Embeddings Help and Hurt Spatial Reasoning | arXiv:2601.22231v1 Announce Type: new Abstract: This paper revisits the role of positional embeddings (PEs) within vision transformers (ViTs) from a geometric perspective. We show that PEs are not mere token indices but effectively function as geometric priors that shape the spatial structure of the representation. We ... | https://arxiv.org/abs/2601.22231 | Academic Papers | svg |
4142841192c7255eef680c282b81f9fc0d9850ab38c7484b454e8b893db6eb74 | 2026-02-02T00:00:00-05:00 | A Systematic Literature Review on LLM Defenses Against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy | arXiv:2601.22240v1 Announce Type: new Abstract: The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) and large language models (LLMs) has been accompanied by the emergence of new security vulnerabilities and challenges, such as jailbreaking and other prompt injection attacks. Thes... | https://arxiv.org/abs/2601.22240 | Academic Papers | svg |
d35bda21175bd4f3f3358a1141888c062be4760d0fa4d061431d3b0605fa6bbd | 2026-02-02T00:00:00-05:00 | Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study | arXiv:2601.22241v1 Announce Type: new Abstract: Gradient-free black-box optimization (BBO) is widely used in engineering design and provides a flexible framework for topology optimization (TO), enabling the discovery of high-performing structural designs without requiring gradient information from simulations. Yet, its... | https://arxiv.org/abs/2601.22241 | Academic Papers | svg |
0b7dfa41d998de4ef42431f00a6c15ac6e921b8d64f2671dee4f2df145799e12 | 2026-02-02T00:00:00-05:00 | Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data | arXiv:2601.22242v1 Announce Type: new Abstract: A driving algorithm that aligns with good human driving practices, or at the very least collaborates effectively with human drivers, is crucial for developing safe and efficient autonomous vehicles. In practice, two main approaches are commonly adopted: (i) supervised or ... | https://arxiv.org/abs/2601.22242 | Academic Papers | svg |
c9220bf8d977135620f6a33f7dd91e7998a5ac1994941bf13bbfe6ba9599642a | 2026-02-02T00:00:00-05:00 | Is Hierarchical Quantization Essential for Optimal Reconstruction? | arXiv:2601.22244v1 Announce Type: new Abstract: Vector-quantized variational autoencoders (VQ-VAEs) are central to models that rely on high reconstruction fidelity, from neural compression to generative pipelines. Hierarchical extensions, such as VQ-VAE2, are often credited with superior reconstruction performance beca... | https://arxiv.org/abs/2601.22244 | Academic Papers | svg |
07f5c0b22a48c3d503c99c2f1d5ab5ead646d1eae22edf530574e52b4d76d8ec | 2026-02-02T00:00:00-05:00 | MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models | arXiv:2601.22246v1 Announce Type: new Abstract: As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution has become increasingly important. Watermarking is a promising approach, but existing methods either provide only binary signals o... | https://arxiv.org/abs/2601.22246 | Academic Papers | svg |
fc0bbe96499e24f5e3d46cb9a3ead09fb92c45de21c7b87b67e61d69369a846d | 2026-02-02T00:00:00-05:00 | FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation | arXiv:2601.22249v1 Announce Type: new Abstract: Code generation is a core application of large language models (LLMs), yet LLMs still frequently fail on complex programming tasks. Given its success in mathematical reasoning, test-time scaling approaches such as Process Reward Model (PRM)-based Best-of-N selection offer... | https://arxiv.org/abs/2601.22249 | Academic Papers | svg |
42130961fa1cc7ff36e7341351cefe317977d93ea96132d7bde0ef15d01b6a22 | 2026-02-02T00:00:00-05:00 | AI Narrative Breakdown. A Critical Assessment of Power and Promise | arXiv:2601.22255v1 Announce Type: new Abstract: This article sets off for an exploration of the still evolving discourse surrounding artificial intelligence (AI) in the wake of the release of ChatGPT. It scrutinizes the pervasive narratives that are shaping the societal engagement with AI, spotlighting key themes such ... | https://arxiv.org/abs/2601.22255 | Academic Papers | svg |
f80ccb73249bae13011e4441c1f4b999ef6532070b501fa4b21efd3cb815c273 | 2026-02-02T00:00:00-05:00 | SPARK: Real-Time Monitoring of Multi-Faceted Programming Exercises | arXiv:2601.22256v1 Announce Type: new Abstract: Monitoring in-class programming exercises can help instructors identify struggling students and common challenges. However, understanding students' progress can be prohibitively difficult, particularly for multi-faceted problems that include multiple steps with complex in... | https://arxiv.org/abs/2601.22256 | Academic Papers | svg |
9f5ba8e88902d13d9a98618c23efc6d73ae994c287126bc20d6e37ba5e87ad59 | 2026-02-02T00:00:00-05:00 | Symmetry Breaking in Transformers for Efficient and Interpretable Training | arXiv:2601.22257v1 Announce Type: new Abstract: The attention mechanism in its standard implementation contains extraneous rotational degrees of freedom that are carried through computation but do not affect model activations or outputs. We introduce a simple symmetry-breaking protocol that inserts a preferred directio... | https://arxiv.org/abs/2601.22257 | Academic Papers | svg |
129766d0b5b67bfd0cba19ec424171a5c5a41d49ef61c6297557570203cb6473 | 2026-02-02T00:00:00-05:00 | Tabular Foundation Models Can Do Survival Analysis | arXiv:2601.22259v1 Announce Type: new Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event occurs. We develo... | https://arxiv.org/abs/2601.22259 | Academic Papers | svg |
d30b226bef0b82c1d4f6d04975617976d545f49f9432ca9597b8046d546d2975 | 2026-02-02T00:00:00-05:00 | Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models | arXiv:2601.22264v1 Announce Type: new Abstract: In principle, Continuous Integration (CI) pipeline failures provide valuable feedback to developers on code-related errors. In practice, however, pipeline jobs often fail intermittently due to non-deterministic tests, network outages, infrastructure failures, resource exh... | https://arxiv.org/abs/2601.22264 | Academic Papers | svg |
d7a1daa48b4255f5e6bdae4471730a38a00e27618ccb42fbc7f6a6ebf721c08e | 2026-02-02T00:00:00-05:00 | Privacy-Preserving Sensor-Based Human Activity Recognition for Low-Resource Healthcare Using Classical Machine Learning | arXiv:2601.22265v1 Announce Type: new Abstract: Limited access to medical infrastructure forces elderly and vulnerable patients to rely on home-based care, often leading to neglect and poor adherence to therapeutic exercises such as yoga or physiotherapy. To address this gap, we propose a low-cost and automated human a... | https://arxiv.org/abs/2601.22265 | Academic Papers | svg |
f7c4bb9b5fd6209a565c7f22fd752f0f529c9db8f7cbfa46017619280f7b1340 | 2026-02-02T00:00:00-05:00 | JAF: Judge Agent Forest | arXiv:2601.22269v1 Announce Type: new Abstract: Judge agents are fundamental to agentic AI frameworks: they provide automated evaluation, and enable iterative self-refinement of reasoning processes. We introduce JAF: Judge Agent Forest, a framework in which the judge agent conducts joint inference across a cohort of qu... | https://arxiv.org/abs/2601.22269 | Academic Papers | svg |
728722fd8420a5436b609a7f1e1f5d656ebffd05705af22081cd9044385cca34 | 2026-02-02T00:00:00-05:00 | Task-Uniform Convergence and Backward Transfer in Federated Domain-Incremental Learning with Partial Participation | arXiv:2601.22274v1 Announce Type: new Abstract: Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this setting as Federated Domain-Incremental Learning (FDIL), where (i) clients are heterogeneous, (ii) tasks arrive sequentially wi... | https://arxiv.org/abs/2601.22274 | Academic Papers | svg |
f00702a4e710386ab742028d151044d8c397223552a00b9b43a090657037f8cb | 2026-02-02T00:00:00-05:00 | VMonarch: Efficient Video Diffusion Transformers with Structured Attention | arXiv:2601.22275v1 Announce Type: new Abstract: The quadratic complexity of the attention mechanism severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the highly sparse spatio-temporal attention patterns exhibited in Video DiTs can be naturally represented by the Monarch matrix... | https://arxiv.org/abs/2601.22275 | Academic Papers | svg |
bef3410060c7303249ba86f66621a3555c5993bbec6ae1e628de53f9bda12506 | 2026-02-02T00:00:00-05:00 | SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models | arXiv:2601.22276v1 Announce Type: new Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is essential for fair compensation and sustainable data marketplaces. While the Shapley value o... | https://arxiv.org/abs/2601.22276 | Academic Papers | svg |
25e8b27671c1298cacf15f03607ce7ccc93599d3a76acd48f22bd5ed622d16d2 | 2026-02-02T00:00:00-05:00 | Riemannian Lyapunov Optimizer: A Unified Framework for Optimization | arXiv:2601.22284v1 Announce Type: new Abstract: We introduce Riemannian Lyapunov Optimizers (RLOs), a family of optimization algorithms that unifies classic optimizers within one geometric framework. Unlike heuristic improvements to existing optimizers, RLOs are systematically derived from a novel control-theoretic fra... | https://arxiv.org/abs/2601.22284 | Academic Papers | svg |
46ff39c24fb6792b27d29ff1a4f3db55126837b2225a8f8b9bcc63cea2a92b1a | 2026-02-02T00:00:00-05:00 | Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success | arXiv:2601.22285v1 Announce Type: new Abstract: Model merging combines knowledge from separately fine-tuned models, yet success factors remain poorly understood. While recent work treats mergeability as an intrinsic property, we show with an architecture-agnostic framework that it fundamentally depends on both the merg... | https://arxiv.org/abs/2601.22285 | Academic Papers | svg |
9039c3f3e90113128894a7d226631cc70f61cb6d4e5f3f42c2f2a239a8d7a3db | 2026-02-02T00:00:00-05:00 | PersonaCite: VoC-Grounded Interviewable Agentic Synthetic AI Personas for Verifiable User and Design Research | arXiv:2601.22288v1 Announce Type: new Abstract: LLM-based and agent-based synthetic personas are increasingly used in design and product decision-making, yet prior work shows that prompt-based personas often produce persuasive but unverifiable responses that obscure their evidentiary basis. We present PersonaCite, an a... | https://arxiv.org/abs/2601.22288 | Academic Papers | svg |
7db2c6471505134bcbf26fda2ba1612d853629439747049547af74b52b4472bc | 2026-02-02T00:00:00-05:00 | ReloPush-BOSS: Optimization-guided Nonmonotone Rearrangement Planning for a Car-like Robot Pusher | arXiv:2601.22289v1 Announce Type: new Abstract: We focus on multi-object rearrangement planning in densely cluttered environments using a car-like robot pusher. The combination of kinematic, geometric and physics constraints underlying this domain results in challenging nonmonotone problem instances which demand breaki... | https://arxiv.org/abs/2601.22289 | Academic Papers | svg |
35aa3b478e456d3cebf8e85f21ac52207a16d9a4cdbb99286104fbca371e02df | 2026-02-02T00:00:00-05:00 | The Six Sigma Agent: Achieving Enterprise-Grade Reliability in LLM Systems Through Consensus-Driven Decomposed Execution | arXiv:2601.22290v1 Announce Type: new Abstract: Large Language Models demonstrate remarkable capabilities yet remain fundamentally probabilistic, presenting critical reliability challenges for enterprise deployment. We introduce the Six Sigma Agent, a novel architecture that achieves enterprise-grade reliability throug... | https://arxiv.org/abs/2601.22290 | Academic Papers | svg |
93947737c00493fc2a356098ef858a63daadca71e140b2471f6147ab5c9e54d7 | 2026-02-02T00:00:00-05:00 | Learning Reward Functions for Cooperative Resilience in Multi-Agent Systems | arXiv:2601.22292v1 Announce Type: new Abstract: Multi-agent systems often operate in dynamic and uncertain environments, where agents must not only pursue individual goals but also safeguard collective functionality. This challenge is especially acute in mixed-motive multi-agent systems. This work focuses on cooperativ... | https://arxiv.org/abs/2601.22292 | Academic Papers | svg |
0a5ed452ab8ed28ded2fa1c40e6a7411f9a92710b6938e4e38f374b5311133f6 | 2026-02-02T00:00:00-05:00 | ParalESN: Enabling parallel information processing in Reservoir Computing | arXiv:2601.22296v1 Announce Type: new Abstract: Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely constrained by (i) the necessity of processing temporal data sequentially and (ii) the prohibitive memory footprint of high-dimensio... | https://arxiv.org/abs/2601.22296 | Academic Papers | svg |
a6b3eaa32fbcb68087e2cc21e12b473a23fccbfc85e3f45b0301862476418721 | 2026-02-02T00:00:00-05:00 | Prepare Reasoning Language Models for Multi-Agent Debate with Self-Debate Reinforcement Learning | arXiv:2601.22297v1 Announce Type: new Abstract: The reasoning abilities of large language models (LLMs) have been substantially improved by reinforcement learning with verifiable rewards (RLVR). At test time, collaborative reasoning through Multi-Agent Debate (MAD) has emerged as a promising approach for enhancing LLM ... | https://arxiv.org/abs/2601.22297 | Academic Papers | svg |
bacf7c9ee5129d1033c32eb20632d08596c6c7332edd9ed675021afff03f6d68 | 2026-02-02T00:00:00-05:00 | Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation | arXiv:2601.22298v1 Announce Type: new Abstract: Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains. A critical challenge with these models is the absence of calibrated uncertainty, which undermines trust in indiv... | https://arxiv.org/abs/2601.22298 | Academic Papers | svg |
7539a1e5a63caca312760d14a3d3be9feae34d915ec4ebb46101a03dbe1f7513 | 2026-02-02T00:00:00-05:00 | Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes | arXiv:2601.22301v1 Announce Type: new Abstract: Traditional rendering pipelines rely on complex assets, accurate materials and lighting, and substantial computational resources to produce realistic imagery, yet they still face challenges in scalability and realism for populated dynamic scenes. We present C2R (Coarse-to... | https://arxiv.org/abs/2601.22301 | Academic Papers | svg |
1344c9d6a925fd40f7a5995ce90195b0bd3e72dbf60995b8cde126f666c4d92e | 2026-02-02T00:00:00-05:00 | ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning | arXiv:2601.22302v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that ... | https://arxiv.org/abs/2601.22302 | Academic Papers | svg |
af2006fe4e69ab41e4ce591edb8d5c212db922fe6c592bba81b71989171cd5ba | 2026-02-02T00:00:00-05:00 | BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation | arXiv:2601.22305v1 Announce Type: new Abstract: Automatic workflow generation is the process of automatically synthesizing sequences of LLM calls, tool invocations, and post-processing steps for complex end-to-end tasks. Most prior methods cast this task as an optimization problem with limited theoretical grounding. We... | https://arxiv.org/abs/2601.22305 | Academic Papers | svg |
54a518b88dc2bcd128fb5698b72ddf8a31d8787cf94d68e9b90dabc279c66fe0 | 2026-02-02T00:00:00-05:00 | Exact closed-form Gaussian moments of residual layers | arXiv:2601.22307v1 Announce Type: new Abstract: We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment matching. We close a longstanding gap by deriving exact moment matching for the probit, GeLU, R... | https://arxiv.org/abs/2601.22307 | Academic Papers | svg |
3f1bdbb238f07bc654f57662b351ce6fafd3a8f0e098e8b444e372f3b7308773 | 2026-02-02T00:00:00-05:00 | Stealthy Poisoning Attacks Bypass Defenses in Regression Settings | arXiv:2601.22308v1 Announce Type: new Abstract: Regression models are widely used in industrial processes, engineering and in natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice. In... | https://arxiv.org/abs/2601.22308 | Academic Papers | svg |
e87cb95ea76c5e450dcdecd263f194760e521bdd71b067991e98931c20f0ea11 | 2026-02-02T00:00:00-05:00 | Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents | arXiv:2601.22311v1 Announce Type: new Abstract: Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horizons. We argue that this failure reflects a fundamental mismatch: step-wise reasoning induces... | https://arxiv.org/abs/2601.22311 | Academic Papers | svg |
38dc3ec35645c4502ebf8d72b5a142eaa761720bceb2317d0b2eabf3fef336e6 | 2026-02-02T00:00:00-05:00 | SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models | arXiv:2601.22312v1 Announce Type: new Abstract: Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We introduce SCALAR (Structural Consistency And Logic Across Regimes), a benchmark for evaluating g... | https://arxiv.org/abs/2601.22312 | Academic Papers | svg |
f0ae673b1ff54fdfa52bdb53cd4a1c2a998defa47328ba4b5026ab237bab0988 | 2026-02-02T00:00:00-05:00 | Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment | arXiv:2601.22313v1 Announce Type: new Abstract: Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has shown that models initially deemed "aligned" can exhibit misaligned behavior after fine-tuning, such as forgetting jailbreak safety features or ... | https://arxiv.org/abs/2601.22313 | Academic Papers | svg |
a6f1a588f82a6bc4b2864962098d8a930e33a3178b4cd727354d2ed9273c31e0 | 2026-02-02T00:00:00-05:00 | Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to Hypothesis Generation | arXiv:2601.22315v1 Announce Type: new Abstract: Many real-world optimization problems involve an expensive ground-truth oracle (e.g., human evaluation, physical experiments) and a cheap, low-fidelity prediction oracle (e.g., machine learning models, simulations). Meanwhile, abundant offline data (e.g., past experiments... | https://arxiv.org/abs/2601.22315 | Academic Papers | svg |
9216f1b8ccbb09a0e2dd2e856700edd00cc531ff34a35ba3843e27c5f73f83cd | 2026-02-02T00:00:00-05:00 | FlowSymm: Physics Aware, Symmetry Preserving Graph Attention for Network Flow Completion | arXiv:2601.22317v1 Announce Type: new Abstract: Recovering missing flows on the edges of a network, while exactly respecting local conservation laws, is a fundamental inverse problem that arises in many systems such as transportation, energy, and mobility. We introduce FlowSymm, a novel architecture that combines (i) a... | https://arxiv.org/abs/2601.22317 | Academic Papers | svg |
c3a15a207e9fce1ece3267ad9cea08ae6ff5656f148c2b551e45ad44ebcee517 | 2026-02-02T00:00:00-05:00 | Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations | arXiv:2601.22318v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly accessed as remotely hosted services by edge and enterprise clients that cannot run frontier models locally. Since models vary widely in capability and price, routing queries to models that balance quality and inference cost i... | https://arxiv.org/abs/2601.22318 | Academic Papers | svg |
60459891bafb171653010b984ba879469d60a5e1eef063071761e6268976de9c | 2026-02-02T00:00:00-05:00 | Matrix Factorization for Practical Continual Mean Estimation Under User-Level Differential Privacy | arXiv:2601.22320v1 Announce Type: new Abstract: We study continual mean estimation, where data vectors arrive sequentially and the goal is to maintain accurate estimates of the running mean. We address this problem under user-level differential privacy, which protects each user's entire dataset even when they contribut... | https://arxiv.org/abs/2601.22320 | Academic Papers | svg |
bc371fcee1f1f37e241308bba8d12450f222604e05f253918df80624a8dcf8cc | 2026-02-02T00:00:00-05:00 | Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks | arXiv:2601.22322v1 Announce Type: new Abstract: Indoor localization is a critical enabler for a wide range of location-based services in smart environments, including navigation, asset tracking, and safety-critical applications. Recent graph-based models leverage spatial relationships between Wire-less Fidelity (Wi-Fi)... | https://arxiv.org/abs/2601.22322 | Academic Papers | svg |
f9442fec91ef440c79d3f538f8f9dfdbdfe8930622a978b33bb1719581f3162b | 2026-02-02T00:00:00-05:00 | Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning | arXiv:2601.22323v1 Announce Type: new Abstract: Model routing chooses which language model to use for each query. By sending easy queries to cheaper models and hard queries to stronger ones, it can significantly reduce inference cost while maintaining high accuracy. However, most existing routers treat this as a fixed ... | https://arxiv.org/abs/2601.22323 | Academic Papers | svg |
7efc0126bc871f9f5a56b0182fe1e4c7cb4404a21453add8e68569b1b0819c54 | 2026-02-02T00:00:00-05:00 | AgentScore: Autoformulation of Deployable Clinical Scoring Systems | arXiv:2601.22324v1 Announce Type: new Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules. While machine-learning models achieve strong performance, many fail to translate into routine clinical use due t... | https://arxiv.org/abs/2601.22324 | Academic Papers | svg |
15f714f8813f4d83eabf0018d251ef84bd8e5bbef0fa61ad53823871caeb1744 | 2026-02-02T00:00:00-05:00 | Label-Efficient Monitoring of Classification Models via Stratified Importance Sampling | arXiv:2601.22326v1 Announce Type: new Abstract: Monitoring the performance of classification models in production is critical yet challenging due to strict labeling budgets, one-shot batch acquisition of labels and extremely low error rates. We propose a general framework based on Stratified Importance Sampling (SIS) t... | https://arxiv.org/abs/2601.22326 | Academic Papers | svg |
3163e46935264ef2324dc059cb3fc26d01953d0d27ba7a8e1aeae6a6e28d1662 | 2026-02-02T00:00:00-05:00 | Molecular Representations in Implicit Functional Space via Hyper-Networks | arXiv:2601.22327v1 Announce Type: new Abstract: Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensiona... | https://arxiv.org/abs/2601.22327 | Academic Papers | svg |
94a9c1e036a752548834ec2eb18b130ccb07858bd1ced7d55eb3c3f17ad491fb | 2026-02-02T00:00:00-05:00 | Knowledge-Informed Kernel State Reconstruction for Interpretable Dynamical System Discovery | arXiv:2601.22328v1 Announce Type: new Abstract: Recovering governing equations from data is central to scientific discovery, yet existing methods often break down under noisy, partial observations, or rely on black-box latent dynamics that obscure mechanism. We introduce MAAT (Model Aware Approximation of Trajectories)... | https://arxiv.org/abs/2601.22328 | Academic Papers | svg |
e9483d607735d43b6f62f4e4e86cf3efba7545aedaab0739a87d85321babad29 | 2026-02-02T00:00:00-05:00 | Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice? | arXiv:2601.22329v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly positioned as decision engines for hiring, healthcare, and economic judgment, yet real-world human judgment reflects a balance between rational deliberation and emotion-driven bias. If LLMs are to participate in high-stakes de... | https://arxiv.org/abs/2601.22329 | Academic Papers | svg |
f735432089671b0e410d26217a01cb282148c5a589e197988128d725dcb9aa2b | 2026-02-02T00:00:00-05:00 | Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling | arXiv:2601.22331v1 Announce Type: new Abstract: Cell Painting is a microscopy-based, high-content imaging assay that produces rich morphological profiles of cells and can support drug discovery by quantifying cellular responses to chemical perturbations. At scale, however, Cell Painting data is strongly affected by bat... | https://arxiv.org/abs/2601.22331 | Academic Papers | svg |
413af986448f9da2dea36c5cfc0fa131faee1d093fe92c2f406dcef981c9f377 | 2026-02-02T00:00:00-05:00 | DP-$\lambda$CGD: Efficient Noise Correlation for Differentially Private Model Training | arXiv:2601.22334v1 Announce Type: new Abstract: Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees. Several recent extensions improve its accuracy by introducing correlated noise across training iterations. Ma... | https://arxiv.org/abs/2601.22334 | Academic Papers | svg |
7f15453212e681d72c1143233a22c169d3c5738aab5b66450afe4bda53b0589f | 2026-02-02T00:00:00-05:00 | Knowledge Gradient for Preference Learning | arXiv:2601.22335v1 Announce Type: new Abstract: The knowledge gradient is a popular acquisition function in Bayesian optimization (BO) for optimizing black-box objectives with noisy function evaluations. Many practical settings, however, allow only pairwise comparison queries, yielding a preferential BO problem where d... | https://arxiv.org/abs/2601.22335 | Academic Papers | svg |
dd59193b3db55906e6340edb54b7832acf7a953cc949d867c58ca3b77dbbc4e3 | 2026-02-02T00:00:00-05:00 | From Retrieving Information to Reasoning with AI: Exploring Different Interaction Modalities to Support Human-AI Coordination in Clinical Decision-Making | arXiv:2601.22338v1 Announce Type: new Abstract: LLMs are popular among clinicians for decision-support because of simple text-based interaction. However, their impact on clinicians' performance is ambiguous. Not knowing how clinicians use this new technology and how they compare it to traditional clinical decision-supp... | https://arxiv.org/abs/2601.22338 | Academic Papers | svg |
aa08b3bb291f4f2f591cdcbab58a0dd028a7bb3705ace34bf2cd8fd52b017a01 | 2026-02-02T00:00:00-05:00 | Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems | arXiv:2601.22339v1 Announce Type: new Abstract: Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled Internet of Things (AIoT) solutions for global commerce. However, conventional supply chain optimization models often ove... | https://arxiv.org/abs/2601.22339 | Academic Papers | svg |
a373049d21356e37f62d6bd435c9dd4c251d46c37b71a516687d1d0b5ff7187d | 2026-02-02T00:00:00-05:00 | Convergence Analysis of the Discrete Constrained Saddle Dynamics and Their Momentum Variants | arXiv:2601.22341v1 Announce Type: new Abstract: We study the discrete constrained saddle dynamics and their momentum variants for locating saddle points on manifolds. Under the assumption of exact unstable eigenvectors, we establish a local linear convergence of the discrete constrained saddle dynamics and show that th... | https://arxiv.org/abs/2601.22341 | Academic Papers | svg |
e1bd38ddaf86a85a6ab795df760e6fedd63d8002ee7d10925cf7a30a93554109 | 2026-02-02T00:00:00-05:00 | Low-Rank Approximation by Randomly Pivoted LU | arXiv:2601.22344v1 Announce Type: new Abstract: The low-rank approximation properties of Randomly Pivoted LU (RPLU), a variant of Gaussian elimination where pivots are sampled proportional to the squared entries of the Schur complement, are analyzed. It is shown that the RPLU iterates converge geometrically in expectat... | https://arxiv.org/abs/2601.22344 | Academic Papers | svg |
a57df35de743e9a22c5f4e1ccb7dfd1287939adf3bc92aac1a07e8ae4589f240 | 2026-02-02T00:00:00-05:00 | Failing to Explore: Language Models on Interactive Tasks | arXiv:2601.22345v1 Announce Type: new Abstract: We evaluate language models on their ability to explore interactive environments under a limited interaction budget. We introduce three parametric tasks with controllable exploration difficulty, spanning continuous and discrete environments. Across state-of-the-art models... | https://arxiv.org/abs/2601.22345 | Academic Papers | svg |
9887092f3ccf0ac36b11c3ccdd5cddd96769d7c8fec7a482083913fbdb65ae07 | 2026-02-02T00:00:00-05:00 | FAIRFORMER: A transformer architecture for discrete fair division | arXiv:2601.22346v1 Announce Type: new Abstract: We propose a deep neural network-based solution to the problem of allocating indivisible goods under additive subjective valuations without monetary transfers, trading off economic efficiency with envy-based fairness. We introduce FairFormer, an amortized, permutation-equ... | https://arxiv.org/abs/2601.22346 | Academic Papers | svg |
da2694bd536d97a3358ac7ab5b9e1c81fd7564a90d0ac72379c8d81fecdecd6f | 2026-02-02T00:00:00-05:00 | MixQuant: Pushing the Limits of Block Rotations in Post-Training Quantization | arXiv:2601.22347v1 Announce Type: new Abstract: Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of full-vector rotations, the effect of block structure on outlier suppression remains poorly understood. To fill this gap, ... | https://arxiv.org/abs/2601.22347 | Academic Papers | svg |
735e87b63bfbe557a9a93acb53499d25f7ec3a8f3cd4427b0c2469d709d00acc | 2026-02-02T00:00:00-05:00 | Forward-KL Convergence of Time-Inhomogeneous Langevin Diffusions | arXiv:2601.22349v1 Announce Type: new Abstract: Many practical samplers rely on time-dependent drifts -- often induced by annealing or tempering schedules -- to improve exploration and stability. This motivates a unified non-asymptotic analysis of the corresponding Langevin diffusions and their discretizations. We prov... | https://arxiv.org/abs/2601.22349 | Academic Papers | svg |
4a464cb0f9011b97c6ff749ae486f8bb19c5ee30e1c3a10ba5155bfabbdbb925 | 2026-02-02T00:00:00-05:00 | Learning Policy Representations for Steerable Behavior Synthesis | arXiv:2601.22350v1 Announce Type: new Abstract: Given a Markov decision process (MDP), we seek to learn representations for a range of policies to facilitate behavior steering at test time. As policies of an MDP are uniquely determined by their occupancy measures, we propose modeling policy representations as expectati... | https://arxiv.org/abs/2601.22350 | Academic Papers | svg |
b7c77563261f55a757a4d7926b9da0839f30a1af0eabe5fa43e0e86740791e07 | 2026-02-02T00:00:00-05:00 | Recoverability Has a Law: The ERR Measure for Tool-Augmented Agents | arXiv:2601.22352v1 Announce Type: new Abstract: Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive theory that resolves this gap by showing that recoverability follows a measurable law. To elaborate, we fo... | https://arxiv.org/abs/2601.22352 | Academic Papers | svg |
8a861e9902cd9c6c881ad5c4fd2ab2ef09d778986ef5d012ce16a0ce872c12db | 2026-02-02T00:00:00-05:00 | Relative Wasserstein Angle and the Problem of the $W_2$-Nearest Gaussian Distribution | arXiv:2601.22355v1 Announce Type: new Abstract: We study the problem of quantifying how far an empirical distribution deviates from Gaussianity under the framework of optimal transport. By exploiting the cone geometry of the relative translation invariant quadratic Wasserstein space, we introduce two novel geometric qu... | https://arxiv.org/abs/2601.22355 | Academic Papers | svg |
d5d5423699d6af682943f96d0f60364a3b75f22d9ea871672b389e7501d40d48 | 2026-02-02T00:00:00-05:00 | PoSafeNet: Safe Learning with Poset-Structured Neural Nets | arXiv:2601.22356v1 Announce Type: new Abstract: Safe learning is essential for deploying learningbased controllers in safety-critical robotic systems, yet existing approaches often enforce multiple safety constraints uniformly or via fixed priority orders, leading to infeasibility and brittle behavior. In practice, saf... | https://arxiv.org/abs/2601.22356 | Academic Papers | svg |
88bff0920af5e558366789c6f71d5c78f2c7ca2bff4e83bf205ee1f92b3a3589 | 2026-02-02T00:00:00-05:00 | Small Talk, Big Impact: The Energy Cost of Thanking AI | arXiv:2601.22357v1 Announce Type: new Abstract: Being polite is free - or is it? In this paper, we quantify the energy cost of seemingly innocuous messages such as ``thank you'' when interacting with large language models, often used by users to convey politeness. Using real-world conversation traces and fine-grained e... | https://arxiv.org/abs/2601.22357 | Academic Papers | svg |
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