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24da37ca0b7f746b48fd806251fd764b5d3651e6ef23f70b3a1d8ddd765c9525
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2026-01-29T07:00:02+00:00
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The great government brain drain
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Science, Volume 391, Issue 6784, Page 428-429, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8893?af=R
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Academic Papers
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714da92e83f9c3aa915a4ccf916e6df78ce0582c7ed566bede289632bd4e079c
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2026-01-29T07:00:02+00:00
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Earthquake sensors buried in the quietest spot on Earth
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Science, Volume 391, Issue 6784, Page 430-431, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8894?af=R
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Academic Papers
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b3c8481dbe2238abb2e154170b4ee1b666045fe60c9ed8dd5b45ed8487cf62a0
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2026-01-29T07:00:02+00:00
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Leading preprint server clamps down on ‘AI slop’
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Science, Volume 391, Issue 6784, Page 432-433, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8896?af=R
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Academic Papers
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031f10656a8ad04bc691bd6f0aaa29f303bf59092d8090ed872f7f92fabd4938
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2026-01-29T07:00:02+00:00
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Magnetic fields cause fluorescent proteins to dim
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Science, Volume 391, Issue 6784, Page 434-435, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8898?af=R
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Academic Papers
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d7a3c33de78f7cf5a7567a20dc6973c12b5a1af3e5982502f1a48923893dadd1
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2026-01-29T07:00:02+00:00
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Oil helped build Venezuela’s science. Can oil now revive it?
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Science, Volume 391, Issue 6784, Page 431-432, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8895?af=R
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Academic Papers
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86c77332adf5a94f859e547309e5b94f1118e1ca2eb2f4b42251928028fb6a89
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2026-01-29T08:00:00+00:00
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The ‘undone science’ of opioid overdose deaths
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Science, Volume 391, Issue 6784, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aee8306?af=R
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Academic Papers
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f7f8695d9aa87a7612793a12aadfff4bb0968500762d9ab5eab292bc31678150
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2026-01-29T07:00:02+00:00
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China turns the tables in biotech
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Science, Volume 391, Issue 6784, Page 427-427, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef7757?af=R
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Academic Papers
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b7def46f96f21b07e57e24635aa84d12f91f1d7ab0617688ff9ea93d007ea93f
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2026-01-29T07:00:02+00:00
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Public access’s next frontier
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Science, Volume 391, Issue 6784, Page 522-524, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef7772?af=R
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Academic Papers
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c32d9c7a24b133d226236a636ee1fc566613f4027d4f7dbfb70e59cb7f9a411d
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2026-01-29T07:00:02+00:00
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Fossil energy minimum viable scale
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Science, Volume 391, Issue 6784, Page 449-452, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aea0972?af=R
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Academic Papers
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75fec4a8b26e61d4732b8efef07aba5a8361fb5945e75592017962be3c8895d3
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2026-01-29T07:00:02+00:00
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In Science Journals
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Science, Volume 391, Issue 6784, Page 466-468, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef8478?af=R
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Academic Papers
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427e9c1ae9893450a609c388869b7f57ed5be4952b4dca6ef70ac9ba2b368446
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2026-02-02T00:00:00-05:00
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Screen, Match, and Cache: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation
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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 ongoing actions, we propose FrameCache, a training-free three-stage framework consisting of Screen, Cache, and Match. In the Screen stage, a multi-dimensional, quality-aware mechanism with adaptive thresholds dynamically selects informative frames; the Cache stage maintains a reference pool using a dynamic replacement-hit strategy, preserving both diversity and relevance; and the Match stage extracts behavioral features to perform motion-consistent reference matching for coherent animation guidance. Extensive experiments on standard benchmarks demonstrate that FrameCache consistently improves temporal coherence and visual stability while integrating seamlessly with diverse baselines. Despite these encouraging results, further analysis reveals that its effectiveness depends on baseline temporal reasoning and real-synthetic consistency, motivating future work on compatibility conditions and adaptive cache mechanisms. Code will be made publicly available.
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https://arxiv.org/abs/2601.22160
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Academic Papers
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dce0f12b2384d50a25bc881f264ef8e6ec3b326494e7e22ac7d07fb752a52c9b
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2026-02-02T00:00:00-05:00
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Attention Isn't All You Need for Emotion Recognition:Domain Features Outperform Transformers on the EAV Dataset
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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 mechanisms (M2), and improved CNN baselines (M3). Our experiments show that sophisticated attention mechanisms consistently underperform on small datasets. M2 models achieved 5 to 13 percentage points below baselines due to overfitting and destruction of pretrained features. In contrast, simple domain-appropriate modifications proved effective: adding delta MFCCs to the audio CNN improved accuracy from 61.9\% to \textbf{65.56\%} (+3.66pp), while frequency-domain features for EEG achieved \textbf{67.62\%} (+7.62pp over the paper baseline). Our vision transformer baseline (M1) reached \textbf{75.30\%}, exceeding the paper's ViViT result (74.5\%) through domain-specific pretraining, and vision delta features achieved \textbf{72.68\%} (+1.28pp over the paper CNN). These findings demonstrate that for small-scale emotion recognition, domain knowledge and proper implementation outperform architectural complexity.
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https://arxiv.org/abs/2601.22161
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Academic Papers
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8adafd44f97bad762b999eca1aa2c458fe6a825095c07b553a529b7b4f8cef22
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2026-02-02T00:00:00-05:00
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Do Open-Vocabulary Detectors Transfer to Aerial Imagery? A Comparative Evaluation
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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 benchmark evaluating five state-of-the-art OVD models on the LAE-80C aerial dataset (3,592 images, 80 categories) under strict zero-shot conditions. Our experimental protocol isolates semantic confusion from visual localization through Global, Oracle, and Single-Category inference modes. Results reveal severe domain transfer failure: the best model (OWLv2) achieves only 27.6% F1-score with 69% false positive rate. Critically, reducing vocabulary size from 80 to 3.2 classes yields 15x improvement, demonstrating that semantic confusion is the primary bottleneck. Prompt engineering strategies such as domain-specific prefixing and synonym expansion, fail to provide meaningful performance gains. Performance varies dramatically across datasets (F1: 0.53 on DIOR, 0.12 on FAIR1M), exposing brittleness to imaging conditions. These findings establish baseline expectations and highlight the need for domain-adaptive approaches in aerial OVD.
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https://arxiv.org/abs/2601.22164
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Academic Papers
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361f8707a7845fd4d287eba79a24ea0db5eb86c64c68dee807cffb82d0c4f875
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2026-02-02T00:00:00-05:00
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In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
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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 three mechanisms for inducing drunk language in LLMs: persona-based prompting, causal fine-tuning, and reinforcement-based post-training. When evaluated on 5 LLMs, we observe a higher susceptibility to jailbreaking on JailbreakBench (even in the presence of defences) and privacy leaks on ConfAIde, where both benchmarks are in English, as compared to the base LLMs as well as previously reported approaches. Via a robust combination of manual evaluation and LLM-based evaluators and analysis of error categories, our findings highlight a correspondence between human-intoxicated behaviour, and anthropomorphism in LLMs induced with drunk language. The simplicity and efficiency of our drunk language inducement approaches position them as potential counters for LLM safety tuning, highlighting significant risks to LLM safety.
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https://arxiv.org/abs/2601.22169
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Academic Papers
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cfb467133f640d5cfb918de019344ff9edf2b100ef6410fe722f75b5de0882c6
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2026-02-02T00:00:00-05:00
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Large Language Models: A Mathematical Formulation
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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 by describing the encoding of text sequences into sequences of tokens, defining the architecture for next-token prediction models, explaining how these models are learned from data, and demonstrating how they are deployed to address a variety of tasks. The mathematical sophistication required to understand this material is not high, and relies on straightforward ideas from information theory, probability and optimization. Nonetheless, the combination of ideas resting on these different components from the mathematical sciences yields a complex algorithmic structure; and this algorithmic structure has demonstrated remarkable empirical successes. The mathematical framework established here provides a platform from which it is possible to formulate and address questions concerning the accuracy, efficiency and robustness of the algorithms that constitute LLMs. The framework also suggests directions for development of modified and new methodologies.
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https://arxiv.org/abs/2601.22170
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Academic Papers
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4cda64543c8e367cbe6d6bc78814cb86e2c5e464a88d6ac7be79ccc41742d288
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2026-02-02T00:00:00-05:00
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On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators
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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 $1\leq p<\infty$. To this end, we analyze the properties of sigmoidal functions and maximum-minimum operations, subsequently establishing the convergence of the proposed operator in pointwise, supremum, and $L^{p}$ norms. Furthermore, we derive quantitative estimates for the rates of convergence. In the applications section, numerical and graphical examples demonstrate that the proposed Durrmeyer-type operators provide smoother approximations compared to Kantorovich-type and standard max-min operators. Finally, we highlight the superior filtering performance of these operators in signal analysis, validating their effectiveness in both approximation and data processing tasks.
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https://arxiv.org/abs/2601.22174
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Academic Papers
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ab6d8aeb1bebe4e78c9c26ac3a49986398870426d84f6f2630c279a4ec42cc23
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2026-02-02T00:00:00-05:00
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An innovating approach to teaching applied to database design. Improvement of Action Learning in Lifelong Learning
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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 framework of the University College of Further Education of the University of Angers. Database design is a process initially reserved to the professional data processing specialists, coming from French Level-2 technological courses (2-year degrees) or Engineer Schools (Master). The pedagogical model of technological courses has integrated for more than 20 years transverse semester projects, in order to give the students the opportunity to apply newly acquired knowledge, coordinated by teachers. Action Learning requires teachers to assume the role of supervisors for the project management. The objective of Action Learning is to transmit not only knowledge from teachers, but also the experience of consultants to trainees having no competence in data processing, but who have the knowledge of their business process. The present paper shows that Action Learning puts together the factors for success of French technological courses, the adaptability of pedagogy provided to the vocational training, and finally the competence of service provider, Keeping the best parts of those three complementary approaches makes it possible for this kind of formation to achieve teaching and professional, assessable and long lasting goals. Action Learning belongs to the French policy that aims to improve the volume and the quality of the contracts between Universities and companies.
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https://arxiv.org/abs/2601.22175
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Academic Papers
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9ea95b3a9707e92baf43a70ea1f61d750be8e54b7c99f237ccd6963496cf3a02
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2026-02-02T00:00:00-05:00
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Discovering High-utility Sequential Rules with Increasing Utility Ratio
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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 information for decision-making because it uses confidence as an evaluation metric, as well as some algorithms like HUSRM and US-Rule. However, in current rule-growth mining methods, the linkage between HUSRs and their generation remains ambiguous. Specifically, it is unclear whether the addition of new items affects the utility or confidence of the former rule, leading to an increase or decrease in their values. Therefore, in this paper, we formulate the problem of mining HUSRs with an increasing utility ratio. To address this, we introduce a novel algorithm called SRIU for discovering all HUSRs with an increasing utility ratio using two distinct expansion methods, including left-right expansion and right-left expansion. SRIU also utilizes the item pair estimated utility pruning strategy (IPEUP) to reduce the search space. Moreover, for the two expansion methods, two sets of upper bounds and corresponding pruning strategies are introduced. To enhance the efficiency of SRIU, several optimizations are incorporated. These include utilizing the Bitmap to reduce memory consumption and designing a compact utility table for the mining procedure. Finally, extensive experimental results from both real-world and synthetic datasets demonstrate the effectiveness of the proposed method. Moreover, to better assess the quality of the generated sequential rules, metrics such as confidence and conviction are employed, which further demonstrate that SRIU can improve the relevance of mining results.
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https://arxiv.org/abs/2601.22178
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Academic Papers
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db66cfbf753353c412ea5520d352b8f43a101bd36fe842071123d97b1b53a485
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2026-02-02T00:00:00-05:00
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High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
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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 high-utility sequential rule mining algorithms suffer from redundant utility computations, as different rules may consist of the same sequence of items. When these items can form multiple distinct rules, additional utility calculations are required. To address this issue, this study proposes a sequential rule mining algorithm that utilizes segmentation guided by confidence (RSC), which employs confidence-guided segmentation to reduce redundant utility computation. It adopts a method that precomputes the confidence of segmented rules by leveraging the support of candidate subsequences in advance. Once the segmentation point is determined, all rules with different antecedents and consequents are generated simultaneously. RSC uses a utility-linked table to accelerate candidate sequence generation and introduces a stricter utility upper bound, called the reduced remaining utility of a sequence, to address sequences with duplicate items. Finally, the proposed RSC method was evaluated on multiple datasets, and the results demonstrate improvements over state-of-the-art approaches.
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https://arxiv.org/abs/2601.22179
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Academic Papers
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cbf3c8ea207b25430172b50d8789c87b37e735613e9af68ecc8b4523867e4d61
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2026-02-02T00:00:00-05:00
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MrRoPE: Mixed-radix Rotary Position Embedding
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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 theoretical foundation. In this paper, we propose MrRoPE (Mixed-radix RoPE), a generalized encoding formulation based on a radix system conversion perspective, which elegantly unifies various RoPE-extension approaches as distinct radix conversion strategies. Based on this theory, we introduce two training-free extensions, MrRoPE-Uni and MrRoPE-Pro, which leverage uniform and progressive radix conversion strategies, respectively, to achieve 'train short, test long' generalization. Without fine-tuning, MrRoPE-Pro sustains over 85% recall in the 128K-context Needle-in-a-Haystack test and achieves more than double YaRN's accuracy on Infinite-Bench retrieval and dialogue subsets. Theoretical analysis confirms that MrRoPE-Pro effectively raises the upper bound of RoPE's attainable encoding length, which further validates the reliability and utility of our theory and methodology.
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https://arxiv.org/abs/2601.22181
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Academic Papers
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aa42ad5d494c6b4fa4d78c63260ce8b30ebd0978fb8eb40b05b7b30df76ce807
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2026-02-02T00:00:00-05:00
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ShellForge: Adversarial Co-Evolution of Webshell Generation and Multi-View Detection for Robust Webshell Defense
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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 intent. Furthermore, many current defenses suffer from high false-alarm rates when encountering benign administrative scripts that employ heavy obfuscation for intellectual property protection. To address these challenges, we present ShellForge, an adversarial co-evolution framework that couples automated webshell generation with multi-view detection to continuously harden defensive boundaries. The framework operates through an iterative co-training loop where a generator and a detector mutually reinforce each other via the exchange of hard samples. The generator is optimized through supervised fine-tuning and preference-based reinforcement learning to synthesize functional, highly evasive variants. Simultaneously, we develop a multi-view fusion detector that integrates semantic features from long-string compression, structural features from pruned abstract syntax trees, and global statistical indicators such as Shannon entropy. To minimize false positives, ShellForge utilizes a LLM-based transformation to create de-malicious samples--scripts that retain complex obfuscation patterns but lack harmful payloads--serving as high-quality hard negatives during training. Evaluations on the public FWOID benchmark demonstrate that ShellForge significantly enhances defensive robustness. Upon convergence, the detector maintains a 0.981 F1-score while the generator achieves a 0.939 evasion rate against commercial engines on VirusTotal.
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https://arxiv.org/abs/2601.22182
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Academic Papers
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3601706d6c9fb14df3f5902d962d7f9a85aa226085af6b55a101282d1bab7d85
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2026-02-02T00:00:00-05:00
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COL-Trees: Efficient Hierarchical Object Search in Road Networks
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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 focus on retrieving nearby POIs for a single agent, these search heuristics do not translate to many other applications. For example, Aggregate k Nearest Neighbor (AkNN) queries require POIs that are close to multiple agents. k Farthest Neighbor (kFN) queries require POIs that are the antithesis of nearest. Such problems naturally benefit from a hierarchical approach, but existing methods rely on Euclidean-based heuristics, which have diminished effectiveness in graphs such as road networks. We propose a novel data structure, COL-Tree (Compacted Object-Landmark Tree), to address this gap by enabling efficient hierarchical graph traversal using a more accurate landmark-based heuristic. We then present query algorithms that utilize COL-Trees to efficiently answer AkNN, kFN, and other queries. In our experiments on real-world and synthetic datasets, we demonstrate that our techniques significantly outperform existing approaches, achieving up to 4 orders of magnitude improvement. Moreover, this comes at a small pre-processing overhead in both theory and practice.
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https://arxiv.org/abs/2601.22183
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Academic Papers
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10308b3ad67cf9b3e530bfa5f8fa58875223e5c0acc2acc0b261b61fda4213ad
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2026-02-02T00:00:00-05:00
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Tacit Coordination of Large Language Models
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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 or outcomes that naturally arise because they stand out in some way as salient or prominent to all players. This work studies Large Language Models (LLMs) as players in tacit coordination games, and addresses how, when, and why focal points emerge. We compare and quantify the coordination capabilities of LLMs in cooperative and competitive games for which human experiments are available. We also introduce several learning-free strategies to improve the coordination of LLMs, with themselves and with humans. On a selection of heterogeneous open-source models, including Llama, Qwen, and GPT-oss, we discover that LLMs have a remarkable capability to coordinate and often outperform humans, yet fail on common-sense coordination that involves numbers or nuanced cultural archetypes. This paper constitutes the first large-scale assessment of LLMs' tacit coordination within the theoretical and psychological framework of focal points.
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https://arxiv.org/abs/2601.22184
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Academic Papers
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2defbd2a75a778e9e3458dc00e3fdb6203cd3223f37a0da73cc496620032f78d
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2026-02-02T00:00:00-05:00
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MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis
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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 multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts required for comprehensive risk modeling. To address this gap, we introduce MemeChain, a large-scale, open-source, cross-chain dataset comprising 34,988 meme coins across Ethereum, BNB Smart Chain, Solana, and Base. MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts, enabling multimodal and forensic study of meme coin projects. Analysis of the dataset shows that visual branding is frequently omitted in low-effort deployments, and many projects lack a functional website. Moreover, we quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch. By providing unified cross-chain coverage and rich off-chain context, MemeChain serves as a foundational resource for research in financial forensics, multimodal anomaly detection, and automated scam prevention in the meme coin ecosystem.
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https://arxiv.org/abs/2601.22185
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Academic Papers
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88c69ee70b9beceab1ffdcb4a911dbe1a20aed8d139a7c0f3e6c52af5973ba70
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2026-02-02T00:00:00-05:00
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Partial Rewriting and Value Interpretation of Logically Constrained Terms (Full Version)
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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 (Takahata et al., 2025) has many advantages over the original approach of rewriting constrained terms. In this paper, we introduce partial constrained rewriting, a variant of rewriting existentially constrained terms whose underlying idea has already appeared implicitly in previous analyses and applications of LCTRSs. We examine the differences between these two notions of constrained rewriting. First, we establish a direct correspondence between them, leveraging subsumption and equivalence of constrained terms where appropriate. Then we give characterizations of each of them, using the interpretation of existentially constrained terms by instantiation. We further introduce the novel notion of value interpretation, that highlights subtle differences between partial and most general rewriting.
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https://arxiv.org/abs/2601.22191
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Academic Papers
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53ba17fff2fec5af1819b427d82409006c7ee24335db80aab6a9eae9a619e0af
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2026-02-02T00:00:00-05:00
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Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
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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 large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis.
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https://arxiv.org/abs/2601.22195
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Academic Papers
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92b445e1b372849a8d3f56f10f385461df14809aa673b7a80275050fb886700b
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2026-02-02T00:00:00-05:00
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Linux Kernel Recency Matters, CVE Severity Doesn't, and History Fades
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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 patch latency to understand what drives patching. Results show that severity and Common Vulnerability Scoring System (CVSS) metrics have a negligible association with patch latency, whereas kernel recency is a reasonable predictor in survival models. Kernel developers fix newer kernels sooner, while older ones retain unresolved CVEs. Commits introducing vulnerabilities are typically broader and more complex than their fixes, though often only approximate reconstructions of development history. The Linux kernel remains a unique open-source project -- its CVE process is no exception.
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https://arxiv.org/abs/2601.22196
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Academic Papers
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346674968d9bf4d72fc2084241f6fb5ef2bacdb0e14abf157bb48166462be18e
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2026-02-02T00:00:00-05:00
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Neural Signals Generate Clinical Notes in the Wild
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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 $11{,}000$ hours of EEG recordings from $9{,}048$ patients. We therefore develop CELM, the first clinical EEG-to-Language foundation model capable of summarizing long-duration, variable-length EEG recordings and performing end-to-end clinical report generation at multiple scales, including recording description, background activity, epileptiform abnormalities, events/seizures, and impressions. Experimental results show that, with patient history supervision, our method achieves $70\%$--$95\%$ average relative improvements in standard generation metrics (e.g., ROUGE-1 and METEOR) from $0.2$--$0.3$ to $0.4$--$0.6$. In the zero-shot setting without patient history, CELM attains generation scores in the range of $0.43$--$0.52$, compared to baselines of $0.17$--$0.26$. CELM integrates pretrained EEG foundation models with language models to enable scalable multimodal learning. We release our model and benchmark construction pipeline at [URL].
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https://arxiv.org/abs/2601.22197
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Academic Papers
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aeacaef0280b7774fe89a18c156fa35bc0a2a5da34da74952da8038f86030da2
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2026-02-02T00:00:00-05:00
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Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey
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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 technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art the latest deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this eld. This is the rst survey that focuses on LiDAR based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain.
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https://arxiv.org/abs/2601.22198
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Academic Papers
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9e3000538e71887df455198ae065ace9e969420b7e3d853c02b9099fb832bca9
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2026-02-02T00:00:00-05:00
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Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines
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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 first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.
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https://arxiv.org/abs/2601.22199
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Academic Papers
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a8261c45a2913e574670ec308256275df4ea247d6796ca4e39fe432e19712375
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2026-02-02T00:00:00-05:00
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The Benefit of Collective Intelligence in Community-Based Content Moderation is Limited by Overt Political Signalling
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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 Community Notes initiative. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesize that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance their overall quality. To test this idea, we conducted an online experiment in which participants jointly authored notes on political posts. Our results show that teams produce notes that are rated as more helpful than individually written notes. We also find that politically diverse teams perform better when evaluating Republican posts, while group composition does not affect perceived note quality for Democrat posts. However, the advantage of collaboration diminishes when team members are aware of one another's political affiliations. Taken together, these findings underscore the complexity of community-based content moderation and highlight the importance of understanding group dynamics and political diversity when designing more effective moderation systems.
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https://arxiv.org/abs/2601.22201
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Academic Papers
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21833f0a8525331f10147e37a07afc4c4aaf4f0114fa2d41f97c9b7ea09cadb9
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2026-02-02T00:00:00-05:00
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FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
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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 paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent stored updates from clients, even when they are absent from the current training round, thereby emulating their presence. Furthermore, we propose FedAdaVR-Quant, which stores client updates in quantised form, significantly reducing the memory requirements (by 50%, 75%, and 87.5%) of FedAdaVR while maintaining equivalent model performance. We analyse the convergence behaviour of FedAdaVR under general nonconvex conditions and prove that our proposed algorithm can eliminate partial client participation error. Extensive experiments conducted on multiple datasets, under both independent and identically distributed (IID) and non-IID settings, demonstrate that FedAdaVR consistently outperforms state-of-the-art baseline methods.
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https://arxiv.org/abs/2601.22204
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a05e8e38a5d4f2ac76ce6e95d3081b5dbd9678c13212b5c0f69aade85da6b595
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2026-02-02T00:00:00-05:00
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Causal Imitation Learning Under Measurement Error and Distribution Shift
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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 cloning (BC) -- whether conditioning on raw measurements or ignoring them -- can converge to systematically biased policies under distribution shift. We propose a general framework for IL under measurement error, inspired by explicitly modeling the causal relationships among the variables, yielding a target that retains a causal interpretation and is robust to distribution shift. Building on ideas from proximal causal inference, we introduce \texttt{CausIL}, which treats noisy state observations as proxy variables, and we provide identification conditions under which the target policy is recoverable from demonstrations without rewards or interactive expert queries. We develop estimators for both discrete and continuous state spaces; for continuous settings, we use an adversarial procedure over RKHS function classes to learn the required parameters. We evaluate \texttt{CausIL} on semi-simulated longitudinal data from the PhysioNet/Computing in Cardiology Challenge 2019 cohort and demonstrate improved robustness to distribution shift compared to BC baselines.
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https://arxiv.org/abs/2601.22206
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0886ce03d5acd3d2993f1d0a455ca4d31938dc994251af5fd8af751f1f0fe0dc
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2026-02-02T00:00:00-05:00
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Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis
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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 propagation, where symptoms appear far from their true causes. Recent advancements in Large Language Models (LLMs) present new opportunities to enhance automated RCA. However, their practical value for RCA depends on the fidelity of reasoning and decision-making. Existing work relies on historical incident corpora, operates directly on high-volume telemetry beyond current LLM capacity, or embeds reasoning inside complex multi-agent pipelines -- conditions that obscure whether failures arise from reasoning itself or from peripheral design choices. We present a focused empirical evaluation that isolates an LLM's reasoning behavior. We design a controlled experimental framework that foregrounds the LLM by using a simplified experimental setting. We evaluate six LLMs under two agentic workflows (ReAct and Plan-and-Execute) and a non-agentic baseline on two real-world case studies (GAIA and OpenRCA). In total, we executed 48,000 simulated failure scenarios, totaling 228 days of execution time. We measure both root-cause accuracy and the quality of intermediate reasoning traces. We produce a labeled taxonomy of 16 common RCA reasoning failures and use an LLM-as-a-Judge for annotation. Our results clarify where current open-source LLMs succeed and fail in multi-hop RCA, quantify sensitivity to input data modalities, and identify reasoning failures that predict final correctness. Together, these contributions provide transparent and reproducible empirical results and a failure taxonomy to guide future work on reasoning-driven system diagnosis.
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https://arxiv.org/abs/2601.22208
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Academic Papers
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2737cc3ea5fae2c0236f07c3af86e94f8e5d86517a055ee2b2bbcdc12454a63f
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2026-02-02T00:00:00-05:00
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Learning to Recommend Multi-Agent Subgraphs from Calling Trees
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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 filtering relevant agents, an orchestrator must choose options that are reliable, compatible with the current execution context, and able to cooperate with other selected agents. Existing recommender systems -- largely built for item-level ranking from flat user-item logs -- do not directly address the structured, sequential, and interaction-dependent nature of agent orchestration. We address this gap by \textbf{formulating agent recommendation in MAS as a constrained decision problem} and introducing a generic \textbf{constrained recommendation framework} that first uses retrieval to build a compact candidate set conditioned on the current subtask and context, and then performs \textbf{utility optimization} within this feasible set using a learned scorer that accounts for relevance, reliability, and interaction effects. We ground both the formulation and learning signals in \textbf{historical calling trees}, which capture the execution structure of MAS (parent-child calls, branching dependencies, and local cooperation patterns) beyond what flat logs provide. The framework supports two complementary settings: \textbf{agent-level recommendation} (select the next agent/tool) and \textbf{system-level recommendation} (select a small, connected agent team/subgraph for coordinated execution). To enable systematic evaluation, we construct a unified calling-tree benchmark by normalizing invocation logs from eight heterogeneous multi-agent corpora into a shared structured representation.
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https://arxiv.org/abs/2601.22209
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Academic Papers
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2f500ced42013670bfa9937755f74c8d04d4e6093f995b3d0ba033135da47c0e
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2026-02-02T00:00:00-05:00
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Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
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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-specific value functions into constrained optimization programs or learn deterministic structured policies, sacrificing generality and policy expressiveness. We propose a solver-induced \emph{latent spherical flow policy} that brings the expressiveness of modern generative policies to combinatorial RL while guaranteeing feasibility by design. Our method, LSFlow, learns a \emph{stochastic} policy in a compact continuous latent space via spherical flow matching, and delegates feasibility to a combinatorial optimization solver that maps each latent sample to a valid structured action. To improve efficiency, we train the value network directly in the latent space, avoiding repeated solver calls during policy optimization. To address the piecewise-constant and discontinuous value landscape induced by solver-based action selection, we introduce a smoothed Bellman operator that yields stable, well-defined learning targets. Empirically, our approach outperforms state-of-the-art baselines by an average of 20.6\% across a range of challenging combinatorial RL tasks.
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https://arxiv.org/abs/2601.22211
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Academic Papers
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2f76d141538cd5dfe73ed88a11f26ed0fbd8fc9c7cc3f73fed30d2f9662cd985
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2026-02-02T00:00:00-05:00
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What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets
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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 charts. Indeed, many VQA datasets that analyze charts do not contain the underlying data behind those charts or assume a 1-to-1 correspondence between chart marks and underlying data. In reality, charts are transformations (i.e. analysis, simplification, modification) of data. This distinction introduces a reasoning challenge in VQA that the current datasets do not capture. In this paper, we argue for a dedicated VQA benchmark for scientific charts where there is no 1-to-1 correspondence between chart marks and underlying data. To do so, we survey existing VQA datasets and highlight limitations of the current field. We then generate synthetic histogram charts based on ground truth data, and ask both humans and a large reasoning model questions where precise answers depend on access to the underlying data. We release the open-source dataset, including figures, underlying data, distribution parameters used to generate the data, and bounding boxes for all figure marks and text for future research.
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https://arxiv.org/abs/2601.22218
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93fdb93932cb1a4fb25fb63cd4800abbb33c195b976ec43a51a9fad167109804
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2026-02-02T00:00:00-05:00
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Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation
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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 relative camera translation and rotation from a pair of images. We introduce VRRPI-Bench, a benchmark derived from unlabeled egocentric videos with verbalized annotations of relative camera motion, reflecting realistic scenarios with simultaneous translation and rotation around a shared object. We further propose VRRPI-Diag, a diagnostic benchmark that isolates individual motion degrees of freedom. Despite the simplicity of RCPE, most VLMs fail to generalize beyond shallow 2D heuristics, particularly for depth changes and roll transformations along the optical axis. Even state-of-the-art models such as GPT-5 ($0.64$) fall short of classic geometric baselines ($0.97$) and human performance ($0.92$). Moreover, VLMs exhibit difficulty in multi-image reasoning, with inconsistent performance (best $59.7\%$) when integrating spatial cues across frames. Our findings reveal limitations in grounding VLMs in 3D and multi-view spatial reasoning.
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https://arxiv.org/abs/2601.22228
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3a9eb252288c8725438ad9511a653a5e56483c3007bad6e04058fa8ba8b9e590
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2026-02-02T00:00:00-05:00
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DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
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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 shifts, including imbalances between easy and hard problems, mismatches between training tasks and evaluation benchmarks, and trajectory mismatch arising from training data generated by cheaper models whose behavior differs from that of inference-time models. We propose DAJ, a reasoning-based LLM judge trained with verifiable rewards under a bi-level data-reweighted learning framework. The proposed framework learns data-importance weights (either domain-level or instance-level) to optimize generalization performance on a held-out meta set aligned with target benchmarks. To the best of our knowledge, this is the first application of data reweighting to LLM-as-a-Judge training for test-time scaling. Our approach automatically emphasizes hard problems, in-distribution samples, and trajectory-aligned data, without relying on hand-crafted heuristics. Empirically, DAJ achieves state-of-the-art performance on LiveCodeBench and BigCodeBench, outperforming strong test-time scaling baselines as well as leading proprietary models.
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https://arxiv.org/abs/2601.22230
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Academic Papers
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472d3c69e0a248a794f11fd401199def46e3b3053f56374937b4352e44d56bdf
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2026-02-02T00:00:00-05:00
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Geometry without Position? When Positional Embeddings Help and Hurt Spatial Reasoning
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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 introduce token-level diagnostics that measure how multi-view geometric consistency in ViT representation depends on consitent PEs. Through extensive experiments on 14 foundation ViT models, we reveal how PEs influence multi-view geometry and spatial reasoning. Our findings clarify the role of PEs as a causal mechanism that governs spatial structure in ViT representations. Our code is provided in https://github.com/shijianjian/vit-geometry-probes
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https://arxiv.org/abs/2601.22231
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4142841192c7255eef680c282b81f9fc0d9850ab38c7484b454e8b893db6eb74
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2026-02-02T00:00:00-05:00
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A Systematic Literature Review on LLM Defenses Against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy
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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. These maliciously crafted inputs can exploit LLMs, causing data leaks, unauthorized actions, or compromised outputs, for instance. As both offensive and defensive prompt injection techniques evolve quickly, a structured understanding of mitigation strategies becomes increasingly important. To address that, this work presents the first systematic literature review on prompt injection mitigation strategies, comprehending 88 studies. Building upon NIST's report on adversarial machine learning, this work contributes to the field through several avenues. First, it identifies studies beyond those documented in NIST's report and other academic reviews and surveys. Second, we propose an extension to NIST taxonomy by introducing additional categories of defenses. Third, by adopting NIST's established terminology and taxonomy as a foundation, we promote consistency and enable future researchers to build upon the standardized taxonomy proposed in this work. Finally, we provide a comprehensive catalog of the reviewed prompt injection defenses, documenting their reported quantitative effectiveness across specific LLMs and attack datasets, while also indicating which solutions are open-source and model-agnostic. This catalog, together with the guidelines presented herein, aims to serve as a practical resource for researchers advancing the field of adversarial machine learning and for developers seeking to implement effective defenses in production systems.
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https://arxiv.org/abs/2601.22240
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d35bda21175bd4f3f3358a1141888c062be4760d0fa4d061431d3b0605fa6bbd
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2026-02-02T00:00:00-05:00
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Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study
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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 success depends on two key choices: the geometric parameterization defining the search space and the optimizer exploring it. This study investigates this interplay through a compliance minimization problem for a cantilever beam subject to a connectivity constraint. We benchmark three geometric parameterizations, each combined with three representative BBO algorithms: differential evolution, covariance matrix adaptation evolution strategy, and heteroscedastic evolutionary Bayesian optimization, across 10D, 20D, and 50D design spaces. Results reveal that parameterization quality has a stronger influence on optimization performance than optimizer choice: a well-structured parameterization enables robust and competitive performance across algorithms, whereas weaker representations increase optimizer dependency. Overall, this study highlights the dominant role of geometric parameterization in practical BBO-based TO and shows that algorithm performance and selection cannot be fairly assessed without accounting for the induced design space.
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https://arxiv.org/abs/2601.22241
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0b7dfa41d998de4ef42431f00a6c15ac6e921b8d64f2671dee4f2df145799e12
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2026-02-02T00:00:00-05:00
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Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data
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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 imitation learning, which requires comprehensive naturalistic driving data capturing all states that influence a vehicle's decisions and corresponding actions, and (ii) reinforcement learning (RL), where the simulated driving environment either matches or is intentionally more challenging than real-world conditions. Both methods depend on high-quality observations of real-world driving behavior, which are often difficult and costly to obtain. State-of-the-art sensors on individual vehicles can gather microscopic data, but they lack context about the surrounding conditions. Conversely, roadside sensors can capture traffic flow and other macroscopic characteristics, but they cannot associate this information with individual vehicles on a microscopic level. Motivated by this complementarity, we propose a framework that reconstructs unobserved microscopic states from macroscopic observations, using microscopic data to anchor observed vehicle behaviors, and learns a shared policy whose behavior is microscopically consistent with the partially observed trajectories and actions and macroscopically aligned with target traffic statistics when deployed population-wide. Such constrained and regularized policies promote realistic flow patterns and safe coordination with human drivers at scale.
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https://arxiv.org/abs/2601.22242
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c9220bf8d977135620f6a33f7dd91e7998a5ac1994941bf13bbfe6ba9599642a
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2026-02-02T00:00:00-05:00
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Is Hierarchical Quantization Essential for Optimal Reconstruction?
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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 because they split global and local features across multiple levels. However, since higher levels derive all their information from lower levels, they should not carry additional reconstructive content beyond what the lower-level already encodes. Combined with recent advances in training objectives and quantization mechanisms, this leads us to ask whether a single-level VQ-VAE, with matched representational budget and no codebook collapse, can equal the reconstruction fidelity of its hierarchical counterpart. Although the multi-scale structure of hierarchical models may improve perceptual quality in downstream tasks, the effect of hierarchy on reconstruction accuracy, isolated from codebook utilization and overall representational capacity, remains empirically underexamined. We revisit this question by comparing a two-level VQ-VAE and a capacity-matched single-level model on high-resolution ImageNet images. Consistent with prior observations, we confirm that inadequate codebook utilization limits single-level VQ-VAEs and that overly high-dimensional embeddings destabilize quantization and increase codebook collapse. We show that lightweight interventions such as initialization from data, periodic reset of inactive codebook vectors, and systematic tuning of codebook hyperparameters significantly reduce collapse. Our results demonstrate that when representational budgets are matched, and codebook collapse is mitigated, single-level VQ-VAEs can match the reconstruction fidelity of hierarchical variants, challenging the assumption that hierarchical quantization is inherently superior for high-quality reconstructions.
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https://arxiv.org/abs/2601.22244
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07f5c0b22a48c3d503c99c2f1d5ab5ead646d1eae22edf530574e52b4d76d8ec
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2026-02-02T00:00:00-05:00
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MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models
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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 or distort the sampling distribution, degrading text quality; distortion-free approaches, in turn, often suffer from weak detectability or robustness. We propose MirrorMark, a multi-bit and distortion-free watermark for LLMs. By mirroring sampling randomness in a measure-preserving manner, MirrorMark embeds multi-bit messages without altering the token probability distribution, preserving text quality by design. To improve robustness, we introduce a context-based scheduler that balances token assignments across message positions while remaining resilient to insertions and deletions. We further provide a theoretical analysis of the equal error rate to interpret empirical performance. Experiments show that MirrorMark matches the text quality of non-watermarked generation while achieving substantially stronger detectability: with 54 bits embedded in 300 tokens, it improves bit accuracy by 8-12% and correctly identifies up to 11% more watermarked texts at 1% false positive rate.
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https://arxiv.org/abs/2601.22246
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fc0bbe96499e24f5e3d46cb9a3ead09fb92c45de21c7b87b67e61d69369a846d
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2026-02-02T00:00:00-05:00
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FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
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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 a promising way to improve performance. However, existing PRMs remain ineffective for code generation due to the lack of meaningful step decomposition in code and the noise of Monte Carlo-estimated partial-solution correctness scores (rewards). To address these challenges, we propose FunPRM. FunPRM prompts LLMs to encourage modular code generation organized into functions, with functions treated as PRM reasoning steps. Furthermore, FunPRM introduces a novel meta-learning-based reward correction mechanism that leverages clean final-solution rewards obtained via a unit-test-based evaluation system to purify noisy partial-solution rewards. Experiments on LiveCodeBench and BigCodeBench demonstrate that FunPRM consistently outperforms existing test-time scaling methods across five base LLMs, notably achieving state-of-the-art performance on LiveCodeBench when combined with O4-mini. Furthermore, FunPRM produces code that is more readable and reusable for developers.
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https://arxiv.org/abs/2601.22249
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42130961fa1cc7ff36e7341351cefe317977d93ea96132d7bde0ef15d01b6a22
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2026-02-02T00:00:00-05:00
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AI Narrative Breakdown. A Critical Assessment of Power and Promise
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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 as agency and decision-making, autonomy, truthfulness, knowledge processing, prediction, general purpose, neutrality and objectivity, apolitical optimization, sustainability game-changer, democratization, mass unemployment, and the dualistic portrayal of AI as either a harbinger of societal utopia or dystopia. Those narratives are analysed critically based on insights from critical computer science, critical data and algorithm studies, from STS, data protection theory, as well as from the philosophy of mind and semiotics. To properly analyse the narratives presented, the article first delves into a historical and technical contextualisation of the AI discourse itself. The article then introduces the notion of "Zeitgeist AI" to critique the imprecise and misleading application of the term "AI" across various societal sectors. Then, by discussing common narratives with nuance, the article contextualises and challenges often assumed socio-political implications of AI, uncovering in detail and with examples the inherent political, power infused and value-laden decisions within all AI applications. Concluding with a call for a more grounded engagement with AI, the article carves out acute problems ignored by the narratives discussed and proposes new narratives recognizing AI as a human-directed tool necessarily subject to societal governance.
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https://arxiv.org/abs/2601.22255
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f80ccb73249bae13011e4441c1f4b999ef6532070b501fa4b21efd3cb815c273
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2026-02-02T00:00:00-05:00
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SPARK: Real-Time Monitoring of Multi-Faceted Programming Exercises
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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 interdependencies, have no predictable completion order, or involve evaluation criteria that are difficult to summarize across many students (e.g., exercises building interactive web-based user interfaces). We introduce SPARK, a coding exercise monitoring dashboard designed to address these challenges. SPARK allows instructors to flexibly group substeps into checkpoints based on exercise requirements, suggests automated tests for these checkpoints, and generates visualizations to track progress across steps. SPARK also allows instructors to inspect intermediate outputs, providing deeper insights into solution variations. We also construct a dataset of 40-minute keystroke coding data from N=22 learners solving two web programming exercises and provide empirical insights into the perceived usefulness of SPARK through a within-subjects evaluation with 16 programming instructors.
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https://arxiv.org/abs/2601.22256
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9f5ba8e88902d13d9a98618c23efc6d73ae994c287126bc20d6e37ba5e87ad59
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2026-02-02T00:00:00-05:00
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Symmetry Breaking in Transformers for Efficient and Interpretable Training
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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 direction into this rotational space through batchwise-sampled, unlearned query and value biases. This modification has two theoretically motivated and empirically validated consequences. First, it can substantially improve the performance of simple, memory-efficient optimizers, narrowing -- and in some cases closing -- the gap to successful but more complex memory-intensive adaptive methods. We demonstrate this by pretraining 124M parameter transformer models with four optimization algorithms (AdamW, SOAP, SGDM, and Energy Conserving Descent(ECD)) and evaluating both validation loss and downstream logical reasoning. Second, it enables an interpretable use of otherwise redundant rotational degrees of freedom, selectively amplifying semantically meaningful token classes within individual attention heads. Overall, our results show that minimal, principled architectural changes can simultaneously improve performance and interpretability.
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https://arxiv.org/abs/2601.22257
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Academic Papers
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129766d0b5b67bfd0cba19ec424171a5c5a41d49ef61c6297557570203cb6473
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2026-02-02T00:00:00-05:00
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Tabular Foundation Models Can Do Survival Analysis
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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 develop a classification-based framework that reformulates both static and dynamic survival analysis as a series of binary classification problems by discretizing event times. Censored observations are naturally handled as examples with missing labels at certain time points. This classification formulation enables existing tabular foundation models to perform survival analysis through in-context learning without explicit training. We prove that under standard censoring assumptions, minimizing our binary classification loss recovers the true survival probabilities as the training set size increases. We demonstrate through evaluation across $53$ real-world datasets that off-the-shelf tabular foundation models with this classification formulation outperform classical and deep learning baselines on average over multiple survival metrics.
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https://arxiv.org/abs/2601.22259
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Academic Papers
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d30b226bef0b82c1d4f6d04975617976d545f49f9432ca9597b8046d546d2975
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2026-02-02T00:00:00-05:00
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Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
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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 exhaustion, and other reliability issues. These intermittent (flaky) job failures lead to substantial inefficiencies: wasted computational resources from repeated reruns and significant diagnosis time that distracts developers from core activities and often requires intervention from specialized teams. Prior work has proposed machine learning techniques to detect intermittent failures, but does not address the subsequent diagnosis challenge. To fill this gap, we introduce FlaXifyer, a few-shot learning approach for predicting intermittent job failure categories using pre-trained language models. FlaXifyer requires only job execution logs and achieves 84.3% Macro F1 and 92.0% Top-2 accuracy with just 12 labeled examples per category. We also propose LogSift, an interpretability technique that identifies influential log statements in under one second, reducing review effort by 74.4% while surfacing relevant failure information in 87% of cases. Evaluation on 2,458 job failures from TELUS demonstrates that FlaXifyer and LogSift enable effective automated triage, accelerate failure diagnosis, and pave the way towards the automated resolution of intermittent job failures.
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https://arxiv.org/abs/2601.22264
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Academic Papers
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d7a1daa48b4255f5e6bdae4471730a38a00e27618ccb42fbc7f6a6ebf721c08e
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2026-02-02T00:00:00-05:00
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Privacy-Preserving Sensor-Based Human Activity Recognition for Low-Resource Healthcare Using Classical Machine Learning
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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 activity recognition (HAR) framework based on wearable inertial sensors and machine learning. Activity data, including walking, walking upstairs, walking downstairs, sitting, standing, and lying, were collected using accelerometer and gyroscope measurements. Four classical classifiers, Logistic Regression, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN), were evaluated and compared with the proposed Support Tensor Machine (STM). Experimental results show that SVM achieved an accuracy of 93.33 percent, while Logistic Regression, Random Forest, and k-NN achieved 91.11 percent. In contrast, STM significantly outperformed these models, achieving a test accuracy of 96.67 percent and the highest cross-validation accuracy of 98.50 percent. Unlike conventional methods, STM leverages tensor representations to preserve spatio-temporal motion dynamics, resulting in robust classification across diverse activities. The proposed framework demonstrates strong potential for remote healthcare, elderly assistance, child activity monitoring, yoga feedback, and smart home wellness, offering a scalable solution for low-resource and rural healthcare settings.
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https://arxiv.org/abs/2601.22265
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Academic Papers
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f7c4bb9b5fd6209a565c7f22fd752f0f529c9db8f7cbfa46017619280f7b1340
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2026-02-02T00:00:00-05:00
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JAF: Judge Agent Forest
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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 query--response pairs generated by a primary agent, rather than evaluating each in isolation. This paradigm elevates the judge from a local evaluator to a holistic learner: by simultaneously assessing related responses, the judge discerns cross-instance patterns and inconsistencies, whose aggregate feedback enables the primary agent to improve by viewing its own outputs through the judge's collective perspective. Conceptually, JAF bridges belief propagation and ensemble-learning principles: overlapping in-context neighborhoods induce a knowledge-graph structure that facilitates propagation of critique, and repeated, randomized evaluations yield a robust ensemble of context-sensitive judgments. JAF can be instantiated entirely via ICL, with the judge prompted for each query using its associated primary-agent response plus a small, possibly noisy set of peer exemplars. While kNN in embedding space is a natural starting point for exemplars, this approach overlooks categorical structure, domain metadata, or nuanced distinctions accessible to modern LLMs. To overcome these limitations, we develop a flexible locality-sensitive hashing (LSH) algorithm that learns informative binary codes by integrating semantic embeddings, LLM-driven hash predicates, supervision from categorical labels, and relevant side information. These hash codes support efficient, interpretable, and relation-aware selection of diverse exemplars, and further optimize exploration of CoT reasoning paths. We validate JAF with an empirical study on the demanding task of cloud misconfigs triage in large-scale cloud environments.
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https://arxiv.org/abs/2601.22269
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Academic Papers
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728722fd8420a5436b609a7f1e1f5d656ebffd05705af22081cd9044385cca34
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2026-02-02T00:00:00-05:00
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Task-Uniform Convergence and Backward Transfer in Federated Domain-Incremental Learning with Partial Participation
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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 with shifting domains, yet (iii) the label space remains fixed. Two theoretical pillars remain missing for FDIL under realistic deployment: a guarantee of backward knowledge transfer (BKT) and a convergence rate that holds across the sequence of all tasks with partial participation. We introduce SPECIAL (Server-Proximal Efficient Continual Aggregation for Learning), a simple, memory-free FDIL algorithm that adds a single server-side ``anchor'' to vanilla FedAvg: in each round, the server nudges the uniformly sampled participated clients update toward the previous global model with a lightweight proximal term. This anchor curbs cumulative drift without replay buffers, synthetic data, or task-specific heads, keeping communication and model size unchanged. Our theory shows that SPECIAL (i) preserves earlier tasks: a BKT bound caps any increase in prior-task loss by a drift-controlled term that shrinks with more rounds, local epochs, and participating clients; and (ii) learns efficiently across all tasks: the first communication-efficient non-convex convergence rate for FDIL with partial participation, O((E/NT)^(1/2)), with E local epochs, T communication rounds, and N participated clients per round, matching single-task FedAvg while explicitly separating optimization variance from inter-task drift. Experimental results further demonstrate the effectiveness of SPECIAL.
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https://arxiv.org/abs/2601.22274
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Academic Papers
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f00702a4e710386ab742028d151044d8c397223552a00b9b43a090657037f8cb
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2026-02-02T00:00:00-05:00
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VMonarch: Efficient Video Diffusion Transformers with Structured Attention
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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. It is a class of structured matrices with flexible sparsity, enabling sub-quadratic attention via an alternating minimization algorithm. Accordingly, we propose VMonarch, a novel attention mechanism for Video DiTs that enables efficient computation over the dynamic sparse patterns with structured Monarch matrices. First, we adapt spatio-temporal Monarch factorization to explicitly capture the intra-frame and inter-frame correlations of the video data. Second, we introduce a recomputation strategy to mitigate artifacts arising from instabilities during alternating minimization of Monarch matrices. Third, we propose a novel online entropy algorithm fused into FlashAttention, enabling fast Monarch matrix updates for long sequences. Extensive experiments demonstrate that VMonarch achieves comparable or superior generation quality to full attention on VBench after minimal tuning. It overcomes the attention bottleneck in Video DiTs, reduces attention FLOPs by a factor of 17.5, and achieves a speedup of over 5x in attention computation for long videos, surpassing state-of-the-art sparse attention methods at 90% sparsity.
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https://arxiv.org/abs/2601.22275
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Academic Papers
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bef3410060c7303249ba86f66621a3555c5993bbec6ae1e628de53f9bda12506
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2026-02-02T00:00:00-05:00
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SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
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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 offers a theoretically grounded approach to attribution, it faces a dual computational bottleneck: (i) the prohibitive cost of exhaustive model retraining for each sampled subset of players (i.e., data contributors) and (ii) the combinatorial number of subsets needed to estimate marginal contributions due to contributor interactions. To this end, we propose SurrogateSHAP, a retraining-free framework that approximates the expensive retraining game through inference from a pretrained model. To further improve efficiency, we employ a gradient-boosted tree to approximate the utility function and derive Shapley values analytically from the tree-based model. We evaluate SurrogateSHAP across three diverse attribution tasks: (i) image quality for DDPM-CFG on CIFAR-20, (ii) aesthetics for Stable Diffusion on Post-Impressionist artworks, and (iii) product diversity for FLUX.1 on Fashion-Product data. Across settings, SurrogateSHAP outperforms prior methods while substantially reducing computational overhead, consistently identifying influential contributors across multiple utility metrics. Finally, we demonstrate that SurrogateSHAP effectively localizes data sources responsible for spurious correlations in clinical images, providing a scalable path toward auditing safety-critical generative models.
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https://arxiv.org/abs/2601.22276
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Academic Papers
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25e8b27671c1298cacf15f03607ce7ccc93599d3a76acd48f22bd5ed622d16d2
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2026-02-02T00:00:00-05:00
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Riemannian Lyapunov Optimizer: A Unified Framework for Optimization
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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 framework that reinterprets optimization as an extended state discrete-time controlled dynamical system on a Riemannian parameter manifold. Central to this framework is the identification of a Normally Attracting Invariant Manifold (NAIM), which organizes training dynamics into two distinct stages: rapid alignment of the speed state to a target graph, followed by controlled evolution within it. We formalize this by constructing a strict Lyapunov function that certifies convergence to a target manifold. This perspective yields a constructive ``optimizer generator" that not only recovers classic algorithms but enables the principled design of RLOs. We validate our theory via geometric diagnostics and demonstrate that grounding optimizer design in control theory yields state-of-the-art performance in large-scale benchmarks. Overall, RLOs bridge control theory and modern machine learning optimization, providing a unified language and a systematic toolkit for designing stable, effective optimizers.
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https://arxiv.org/abs/2601.22284
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Academic Papers
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46ff39c24fb6792b27d29ff1a4f3db55126837b2225a8f8b9bcc63cea2a92b1a
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2026-02-02T00:00:00-05:00
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Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success
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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 merging method and the partner tasks. Using linear optimization over a set of interpretable pairwise metrics (e.g., gradient L2 distance), we uncover properties correlating with post-merge performance across four merging methods. We find substantial variation in success drivers (46.7% metric overlap; 55.3% sign agreement), revealing method-specific "fingerprints". Crucially, however, subspace overlap and gradient alignment metrics consistently emerge as foundational, method-agnostic prerequisites for compatibility. These findings provide a diagnostic foundation for understanding mergeability and motivate future fine-tuning strategies that explicitly encourage these properties.
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https://arxiv.org/abs/2601.22285
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Academic Papers
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9039c3f3e90113128894a7d226631cc70f61cb6d4e5f3f42c2f2a239a8d7a3db
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2026-02-02T00:00:00-05:00
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PersonaCite: VoC-Grounded Interviewable Agentic Synthetic AI Personas for Verifiable User and Design Research
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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 agentic system that reframes AI personas as evidence-bounded research instruments through retrieval-augmented interaction. Unlike prior approaches that rely on prompt-based roleplaying, PersonaCite retrieves actual voice-of-customer artifacts during each conversation turn, constrains responses to retrieved evidence, explicitly abstains when evidence is missing, and provides response-level source attribution. Through semi-structured interviews and deployment study with 14 industry experts, we identify preliminary findings on perceived benefits, validity concerns, and design tensions, and propose Persona Provenance Cards as a documentation pattern for responsible AI persona use in human-centered design workflows.
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https://arxiv.org/abs/2601.22288
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Academic Papers
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7db2c6471505134bcbf26fda2ba1612d853629439747049547af74b52b4472bc
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2026-02-02T00:00:00-05:00
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ReloPush-BOSS: Optimization-guided Nonmonotone Rearrangement Planning for a Car-like Robot Pusher
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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 breaking each manipulation action into multiple parts to achieve a desired object rearrangement. Prior work tackles such instances by planning prerelocations, temporary object displacements that enable constraint satisfaction, but deciding where to prerelocate remains difficult due to local minima leading to infeasible or high-cost paths. Our key insight is that these minima can be avoided by steering a prerelocation optimization toward low-cost regions informed by Dubins path classification. These optimized prerelocations are integrated into an object traversability graph that encodes kinematic, geometric, and pushing constraints. Searching this graph in a depth-first fashion results in efficient, feasible rearrangement sequences. Across a series of densely cluttered scenarios with up to 13 objects, our framework, ReloPush-BOSS, exhibits consistently highest success rates and shortest pushing paths compared to state-of-the-art baselines. Hardware experiments on a 1/10 car-like pusher demonstrate the robustness of our approach. Code and footage from our experiments can be found at: https://fluentrobotics.com/relopushboss.
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https://arxiv.org/abs/2601.22289
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Academic Papers
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35aa3b478e456d3cebf8e85f21ac52207a16d9a4cdbb99286104fbca371e02df
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2026-02-02T00:00:00-05:00
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The Six Sigma Agent: Achieving Enterprise-Grade Reliability in LLM Systems Through Consensus-Driven Decomposed Execution
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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 through three synergistic components: (1) task decomposition into a dependency tree of atomic actions; (2) micro-agent sampling where each task is executed n times in parallel across diverse LLMs to generate independent outputs; and (3) consensus voting with dynamic scaling, clustering outputs and selecting the answer from the winning cluster with maximum votes. We prove that sampling n independent outputs with error rate p achieves system error O(p^{ceil(n/2)}), enabling exponential reliability gains. Even using cheaper models with 5% per-action error, consensus voting with 5 agents reduces error to 0.11%; dynamic scaling to 13 agents achieves 3.4 DPMO (Defects Per Million Opportunities), the Six Sigma standard. Evaluation across three enterprise use cases demonstrates a 14,700x reliability improvement over single-agent execution while reducing costs by 80%. Our work establishes that reliability in AI systems emerges from principled redundancy and consensus rather than model scaling alone.
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https://arxiv.org/abs/2601.22290
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Academic Papers
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93947737c00493fc2a356098ef858a63daadca71e140b2471f6147ab5c9e54d7
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2026-02-02T00:00:00-05:00
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Learning Reward Functions for Cooperative Resilience in Multi-Agent Systems
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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 cooperative resilience, the ability of agents to anticipate, resist, recover, and transform in the face of disruptions, a critical yet underexplored property in Multi-Agent Reinforcement Learning. We study how reward function design influences resilience in mixed-motive settings and introduce a novel framework that learns reward functions from ranked trajectories, guided by a cooperative resilience metric. Agents are trained in a suite of social dilemma environments using three reward strategies: i) traditional individual reward; ii) resilience-inferred reward; and iii) hybrid that balance both. We explore three reward parameterizations-linear models, hand-crafted features, and neural networks, and employ two preference-based learning algorithms to infer rewards from behavioral rankings. Our results demonstrate that hybrid strategy significantly improve robustness under disruptions without degrading task performance and reduce catastrophic outcomes like resource overuse. These findings underscore the importance of reward design in fostering resilient cooperation, and represent a step toward developing robust multi-agent systems capable of sustaining cooperation in uncertain environments.
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https://arxiv.org/abs/2601.22292
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Academic Papers
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0a5ed452ab8ed28ded2fa1c40e6a7411f9a92710b6938e4e38f374b5311133f6
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2026-02-02T00:00:00-05:00
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ParalESN: Enabling parallel information processing in Reservoir Computing
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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-dimensional reservoirs. In this work, we revisit RC through the lens of structured operators and state space modeling to address these limitations, introducing Parallel Echo State Network (ParalESN). ParalESN enables the construction of high-dimensional and efficient reservoirs based on diagonal linear recurrence in the complex space, enabling parallel processing of temporal data. We provide a theoretical analysis demonstrating that ParalESN preserves the Echo State Property and the universality guarantees of traditional Echo State Networks while admitting an equivalent representation of arbitrary linear reservoirs in the complex diagonal form. Empirically, ParalESN matches the predictive accuracy of traditional RC on time series benchmarks, while delivering substantial computational savings. On 1-D pixel-level classification tasks, ParalESN achieves competitive accuracy with fully trainable neural networks while reducing computational costs and energy consumption by orders of magnitude. Overall, ParalESN offers a promising, scalable, and principled pathway for integrating RC within the deep learning landscape.
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https://arxiv.org/abs/2601.22296
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Academic Papers
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a6b3eaa32fbcb68087e2cc21e12b473a23fccbfc85e3f45b0301862476418721
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2026-02-02T00:00:00-05:00
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Prepare Reasoning Language Models for Multi-Agent Debate with Self-Debate Reinforcement Learning
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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 performance. However, current RLVR methods typically train LLMs to solve problems in isolation, without explicitly preparing them to synthesize and benefit from different rationales that arise during debate. In this work, we propose Self-Debate Reinforcement Learning (SDRL), a training framework that equips a single LLM with strong standalone problem-solving ability and the capability to learn from diverse reasoning trajectories in MAD. Given a prompt, SDRL first samples multiple candidate solutions, then constructs a debate context with diverse reasoning paths and generates second-turn responses conditioned on this context. Finally, SDRL jointly optimizes both the initial and debate-conditioned responses, yielding a model that is effective as both a standalone solver and a debate participant. Experiments across multiple base models and reasoning benchmarks show that SDRL improves overall MAD performance while simultaneously strengthening single model reasoning.
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https://arxiv.org/abs/2601.22297
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Academic Papers
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bacf7c9ee5129d1033c32eb20632d08596c6c7332edd9ed675021afff03f6d68
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2026-02-02T00:00:00-05:00
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Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation
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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 individual outputs for high-stakes applications. To address this issue, we propose a systematic conformal prediction approach tailored to conditional generative models, leveraging density estimation on model-generated samples. We introduce a novel method called CP4Gen, which utilizes clustering-based density estimation to construct prediction sets that are less sensitive to outliers, more interpretable, and of lower structural complexity than existing methods. Extensive experiments on synthetic datasets and real-world applications, including climate emulation tasks, demonstrate that CP4Gen consistently achieves superior performance in terms of prediction set volume and structural simplicity. Our approach offers practitioners a powerful tool for uncertainty estimation associated with conditional generative models, particularly in scenarios demanding rigorous and interpretable prediction sets.
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https://arxiv.org/abs/2601.22298
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Academic Papers
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7539a1e5a63caca312760d14a3d3be9feae34d915ec4ebb46101a03dbe1f7513
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2026-02-02T00:00:00-05:00
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Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes
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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-Real), a generative rendering framework that synthesizes real-style urban crowd videos from coarse 3D simulations. Our approach uses coarse 3D renderings to explicitly control scene layout, camera motion, and human trajectories, while a learned neural renderer generates realistic appearance, lighting, and fine-scale dynamics guided by text prompts. To overcome the lack of paired training data between coarse simulations and real videos, we adopt a two-phase mixed CG-real training strategy that learns a strong generative prior from large-scale real footage and introduces controllability through shared implicit spatio-temporal features across domains. The resulting system supports coarse-to-fine control, generalizes across diverse CG and game inputs, and produces temporally consistent, controllable, and realistic urban scene videos from minimal 3D input. We will release the model and project webpage at https://gonzalognogales.github.io/coarse2real/.
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https://arxiv.org/abs/2601.22301
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Academic Papers
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1344c9d6a925fd40f7a5995ce90195b0bd3e72dbf60995b8cde126f666c4d92e
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2026-02-02T00:00:00-05:00
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ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning
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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 integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
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https://arxiv.org/abs/2601.22302
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Academic Papers
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af2006fe4e69ab41e4ce591edb8d5c212db922fe6c592bba81b71989171cd5ba
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2026-02-02T00:00:00-05:00
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BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation
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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 propose to cast workflow generation as Bayesian inference over a posterior distribution on workflows, and introduce \textbf{Bayesian Workflow Generation (BWG)}, a sampling framework that builds workflows step-by-step using parallel look-ahead rollouts for importance weighting and a sequential in-loop refiner for pool-wide improvements. We prove that, without the refiner, the weighted empirical distribution converges to the target posterior. We instantiate BWG as \textbf{BayesFlow}, a training-free algorithm for workflow construction. Across six benchmark datasets, BayesFlow improves accuracy by up to 9 percentage points over SOTA workflow generation baselines and by up to 65 percentage points over zero-shot prompting, establishing BWG as a principled upgrade to search-based workflow design. Code will be available on https://github.com/BoYuanVisionary/BayesFlow.
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https://arxiv.org/abs/2601.22305
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Academic Papers
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54a518b88dc2bcd128fb5698b72ddf8a31d8787cf94d68e9b90dabc279c66fe0
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2026-02-02T00:00:00-05:00
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Exact closed-form Gaussian moments of residual layers
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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, ReLU (as a limit of GeLU), Heaviside (as a limit of probit), and sine activation functions; for both feedforward and generalized residual layers. On random networks, we find orders-of-magnitude improvements in the KL divergence error metric, up to a millionfold, over popular alternatives. On real data, we find competitive statistical calibration for inference under epistemic uncertainty in the input. On a variational Bayes network, we show that our method attains hundredfold improvements in KL divergence from Monte Carlo ground truth over a state-of-the-art deterministic inference method. We also give an a priori error bound and a preliminary analysis of stochastic feedforward neurons, which have recently attracted general interest.
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https://arxiv.org/abs/2601.22307
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Academic Papers
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3f1bdbb238f07bc654f57662b351ce6fafd3a8f0e098e8b444e372f3b7308773
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2026-02-02T00:00:00-05:00
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Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
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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 this paper, we propose a novel optimal stealthy attack formulation that considers different degrees of detectability and show that it bypasses state-of-the-art defenses. We further propose a new methodology based on normalization of objectives to evaluate different trade-offs between effectiveness and detectability. Finally, we develop a novel defense (BayesClean) against stealthy attacks. BayesClean improves on previous defenses when attacks are stealthy and the number of poisoning points is significant.
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https://arxiv.org/abs/2601.22308
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Academic Papers
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e87cb95ea76c5e450dcdecd263f194760e521bdd71b067991e98931c20f0ea11
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2026-02-02T00:00:00-05:00
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Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
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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 a form of step-wise greedy policy that is adequate for short horizons but fails in long-horizon planning, where early actions must account for delayed consequences. From this planning-centric perspective, we study LLM-based agents in deterministic, fully structured environments with explicit state transitions and evaluation signals. Our analysis reveals a core failure mode of reasoning-based policies: locally optimal choices induced by step-wise scoring lead to early myopic commitments that are systematically amplified over time and difficult to recover from. We introduce FLARE (Future-aware Lookahead with Reward Estimation) as a minimal instantiation of future-aware planning to enforce explicit lookahead, value propagation, and limited commitment in a single model, allowing downstream outcomes to influence early decisions. Across multiple benchmarks, agent frameworks, and LLM backbones, FLARE consistently improves task performance and planning-level behavior, frequently allowing LLaMA-8B with FLARE to outperform GPT-4o with standard step-by-step reasoning. These results establish a clear distinction between reasoning and planning.
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https://arxiv.org/abs/2601.22311
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Academic Papers
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38dc3ec35645c4502ebf8d72b5a142eaa761720bceb2317d0b2eabf3fef336e6
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2026-02-02T00:00:00-05:00
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SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
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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 geometric scale generalization and its connection to structural hallucination, consistency, and reasoning in materials foundation models. Given canonical crystal representations, models must reason about derived nanoparticle structures obtained through supercell expansion and geometric truncation across length scales spanning a few atoms to over 18,000 atoms, totaling $\approx$100,000 structures from DFT-validated unit cells. SCALAR defines three tasks. (i) CIF to property prediction. (ii) A Chain-of-Thought variant with explicit physics-grounded reasoning. (iii) Inverse retrieval identifying crystals from candidates given target properties. Outputs are evaluated via structured metrics capturing numeric error, hallucination, cross-prompt consistency, monotonic reasoning, output validity, and retrieval regret. Experiments across diverse foundation models reveal large, model-dependent shifts under explicit reasoning, often reducing hallucination and error, but frequently destabilizing consistency or validity. These results demonstrate that geometric scale generalization cannot be inferred from accuracy alone. Supplementary materials are available at https://github.com/KurbanIntelligenceLab/SCALAR.
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https://arxiv.org/abs/2601.22312
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Academic Papers
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f0ae673b1ff54fdfa52bdb53cd4a1c2a998defa47328ba4b5026ab237bab0988
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2026-02-02T00:00:00-05:00
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Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment
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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 re-surfacing knowledge that was intended to be forgotten. These works typically assume that the initial model is aligned based on static black-box evaluation, i.e., the absence of undesired responses to a fixed set of queries. In contrast, we formalize model alignment in both the static and post-update settings and uncover a fundamental limitation of black-box evaluation. We theoretically show that, due to overparameterization, static alignment provides no guarantee of post-update alignment for any update dataset. Moreover, we prove that static black-box probing cannot distinguish a model that is genuinely post-update robust from one that conceals an arbitrary amount of adversarial behavior which can be activated by even a single benign gradient update. We further validate these findings empirically in LLMs across three core alignment domains: privacy, jailbreak safety, and behavioral honesty. We demonstrate the existence of LLMs that pass all standard black-box alignment tests, yet become severely misaligned after a single benign update. Finally, we show that the capacity to hide such latent adversarial behavior increases with model scale, confirming our theoretical prediction that post-update misalignment grows with the number of parameters. Together, our results highlight the inadequacy of static evaluation protocols and emphasize the urgent need for post-update-robust alignment evaluation.
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https://arxiv.org/abs/2601.22313
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Academic Papers
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a6f1a588f82a6bc4b2864962098d8a930e33a3178b4cd727354d2ed9273c31e0
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2026-02-02T00:00:00-05:00
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Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to Hypothesis Generation
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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 and predictions) are often available and can be used to pretrain powerful predictive models, as well as to provide an informative prior. We propose Prediction-Augmented Gaussian Process Upper Confidence Bound (PA-GP-UCB), a novel Bayesian optimization algorithm that leverages both oracles and offline data to achieve provable gains in sample efficiency for the ground-truth oracle queries. PA-GP-UCB employs a control-variates estimator derived from a joint Gaussian process posterior to correct prediction bias and reduce uncertainty. We prove that PA-GP-UCB preserves the standard regret rate of GP-UCB while achieving a strictly smaller leading constant that is explicitly controlled by prediction quality and offline data coverage. Empirically, PA-GP-UCB converges faster than Vanilla GP-UCB and naive prediction-augmented GP-UCB baselines on synthetic benchmarks and on a real-world hypothesis evaluation task grounded in human behavioral data, where predictions are provided by large language models. These results establish PA-GP-UCB as a general and sample-efficient framework for hypothesis generation under expensive feedback.
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https://arxiv.org/abs/2601.22315
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Academic Papers
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9216f1b8ccbb09a0e2dd2e856700edd00cc531ff34a35ba3843e27c5f73f83cd
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2026-02-02T00:00:00-05:00
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FlowSymm: Physics Aware, Symmetry Preserving Graph Attention for Network Flow Completion
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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 group-action on divergence-free flows, (ii) a graph-attention encoder to learn feature-conditioned weights over these symmetry-preserving actions, and (iii) a lightweight Tikhonov refinement solved via implicit bilevel optimization. The method first anchors the given observation on a minimum-norm divergence-free completion. We then compute an orthonormal basis for all admissible group actions that leave the observed flows invariant and parameterize the valid solution subspace, which shows an Abelian group structure under vector addition. A stack of GATv2 layers then encodes the graph and its edge features into per-edge embeddings, which are pooled over the missing edges and produce per-basis attention weights. This attention-guided process selects a set of physics-aware group actions that preserve the observed flows. Finally, a scalar Tikhonov penalty refines the missing entries via a convex least-squares solver, with gradients propagated implicitly through Cholesky factorization. Across three real-world flow benchmarks (traffic, power, bike), FlowSymm outperforms state-of-the-art baselines in RMSE, MAE and correlation metrics.
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https://arxiv.org/abs/2601.22317
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c3a15a207e9fce1ece3267ad9cea08ae6ff5656f148c2b551e45ad44ebcee517
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2026-02-02T00:00:00-05:00
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Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations
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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 is essential. Existing router approaches assume access to centralized query-model evaluation data. However, these data are often fragmented across clients, such as end users and organizations, and are privacy-sensitive, which makes centralizing data infeasible. Additionally, per-client router training is ineffective since local evaluation data is limited and covers only a restricted query distribution and a biased subset of model evaluations. We introduce the first federated framework for LLM routing, enabling clients to learn a shared routing policy from local offline query-model evaluation data. Our framework supports both parametric multilayer perceptron router and nonparametric K-means router under heterogeneous client query distributions and non-uniform model coverage. Across two benchmarks, federated collaboration improves the accuracy-cost frontier over client-local routers, both via increased effective model coverage and better query generalization. Our theoretical results also validate that federated training reduces routing suboptimality.
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https://arxiv.org/abs/2601.22318
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Academic Papers
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60459891bafb171653010b984ba879469d60a5e1eef063071761e6268976de9c
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2026-02-02T00:00:00-05:00
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Matrix Factorization for Practical Continual Mean Estimation Under User-Level Differential Privacy
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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 contribute multiple data points. Previous work on this problem has focused on pure differential privacy. While important, this approach limits applicability, as it leads to overly noisy estimates. In contrast, we analyze the problem under approximate differential privacy, adopting recent advances in the Matrix Factorization mechanism. We introduce a novel mean estimation specific factorization, which is both efficient and accurate, achieving asymptotically lower mean-squared error bounds in continual mean estimation under user-level differential privacy.
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https://arxiv.org/abs/2601.22320
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Academic Papers
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bc371fcee1f1f37e241308bba8d12450f222604e05f253918df80624a8dcf8cc
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2026-02-02T00:00:00-05:00
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Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks
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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) Access Points (APs) and devices, offering finer localization granularity, but fall short in quantifying prediction uncertainty, a key requirement for real-world deployment. In this paper, we propose Spatially-Adaptive Conformal Graph Transformer (SAC-GT), a framework for accurate and reliable indoor localization. SAC-GT integrates a Graph Transformer (GT) model that captures network's spatial topology and signal strength dynamics, with a novel Spatially-Adaptive Conformal Prediction (SACP) method that provides region-specific uncertainty estimates. This allows SAC-GT to produce not only precise two-dimensional (2D) location predictions but also statistically valid confidence regions tailored to varying environmental conditions. Extensive evaluations on a large-scale real-world dataset demonstrate that the proposed SAC-GT solution achieves state-of-the-art localization accuracy while delivering robust and spatially adaptive reliability guarantees.
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https://arxiv.org/abs/2601.22322
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Academic Papers
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f9442fec91ef440c79d3f538f8f9dfdbdfe8930622a978b33bb1719581f3162b
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2026-02-02T00:00:00-05:00
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Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
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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 choice among a small set of models, which makes them hard to adapt to new models or changing budget constraints. In this paper, we propose SCOPE (Scalable and Controllable Outcome Performance Estimator), a routing framework that goes beyond model selection by predicting their cost and performance. Trained with reinforcement learning, SCOPE makes reasoning-based predictions by retrieving how models behave on similar problems, rather than relying on fixed model names, enabling it to work with new, unseen models. Moreover, by explicitly predicting how accurate and how expensive a model will be, it turns routing into a dynamic decision problem, allowing users to easily control the trade-off between accuracy and cost. Experiments show that SCOPE is more than just a cost-saving tool. It flexibly adapts to user needs: it can boost accuracy by up to 25.7% when performance is the priority, or cut costs by up to 95.1% when efficiency matters most.
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https://arxiv.org/abs/2601.22323
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Academic Papers
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7efc0126bc871f9f5a56b0182fe1e4c7cb4404a21453add8e68569b1b0819c54
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2026-02-02T00:00:00-05:00
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AgentScore: Autoformulation of Deployable Clinical Scoring Systems
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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 to misalignment with workflow constraints such as memorability, auditability, and bedside execution. We argue that this gap arises not from insufficient predictive power, but from optimizing over model classes that are incompatible with guideline deployment. Deployable guidelines often take the form of unit-weighted clinical checklists, formed by thresholding the sum of binary rules, but learning such scores requires searching an exponentially large discrete space of possible rule sets. We introduce AgentScore, which performs semantically guided optimization in this space by using LLMs to propose candidate rules and a deterministic, data-grounded verification-and-selection loop to enforce statistical validity and deployability constraints. Across eight clinical prediction tasks, AgentScore outperforms existing score-generation methods and achieves AUC comparable to more flexible interpretable models despite operating under stronger structural constraints. On two additional externally validated tasks, AgentScore achieves higher discrimination than established guideline-based scores.
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https://arxiv.org/abs/2601.22324
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Academic Papers
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15f714f8813f4d83eabf0018d251ef84bd8e5bbef0fa61ad53823871caeb1744
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2026-02-02T00:00:00-05:00
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Label-Efficient Monitoring of Classification Models via Stratified Importance Sampling
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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) that directly addresses these constraints in model monitoring. While SIS has previously been applied in specialized domains, our theoretical analysis establishes its broad applicability to the monitoring of classification models. Under mild conditions, SIS yields unbiased estimators with strict finite-sample mean squared error (MSE) improvements over both importance sampling (IS) and stratified random sampling (SRS). The framework does not rely on optimally defined proposal distributions or strata: even with noisy proxies and sub-optimal stratification, SIS can improve estimator efficiency compared to IS or SRS individually, though extreme proposal mismatch may limit these gains. Experiments across binary and multiclass tasks demonstrate consistent efficiency improvements under fixed label budgets, underscoring SIS as a principled, label-efficient, and operationally lightweight methodology for post-deployment model monitoring.
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https://arxiv.org/abs/2601.22326
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Academic Papers
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3163e46935264ef2324dc059cb3fc26d01953d0d27ba7a8e1aeae6a6e28d1662
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2026-02-02T00:00:00-05:00
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Molecular Representations in Implicit Functional Space via Hyper-Networks
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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-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields. To ensure physical consistency, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network to model a shared prior over molecular fields. We evaluate MolField on molecular dynamics and property prediction. Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks and yields downstream behavior that is stable to how molecules are discretized or queried.
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https://arxiv.org/abs/2601.22327
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Academic Papers
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94a9c1e036a752548834ec2eb18b130ccb07858bd1ced7d55eb3c3f17ad491fb
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2026-02-02T00:00:00-05:00
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Knowledge-Informed Kernel State Reconstruction for Interpretable Dynamical System Discovery
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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), a framework for symbolic discovery built on knowledge-informed Kernel State Reconstruction. MAAT formulates state reconstruction in a reproducing kernel Hilbert space and directly incorporates structural and semantic priors such as non-negativity, conservation laws, and domain-specific observation models into the reconstruction objective, while accommodating heterogeneous sampling and measurement granularity. This yields smooth, physically consistent state estimates with analytic time derivatives, providing a principled interface between fragmented sensor data and symbolic regression. Across twelve diverse scientific benchmarks and multiple noise regimes, MAAT substantially reduces state-estimation MSE for trajectories and derivatives used by downstream symbolic regression relative to strong baselines.
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https://arxiv.org/abs/2601.22328
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Academic Papers
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e9483d607735d43b6f62f4e4e86cf3efba7545aedaab0739a87d85321babad29
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2026-02-02T00:00:00-05:00
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Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice?
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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 decisions or serve as models of human behavior, it is critical to assess whether they exhibit analogous patterns of (ir)rationalities and biases. To this end, we evaluate multiple LLM families on (i) benchmarks testing core axioms of rational choice and (ii) classic decision domains from behavioral economics and social norms where emotions are known to shape judgment and choice. Across settings, we show that deliberate "thinking" reliably improves rationality and pushes models toward expected-value maximization. To probe human-like affective distortions and their interaction with reasoning, we use two emotion-steering methods: in-context priming (ICP) and representation-level steering (RLS). ICP induces strong directional shifts that are often extreme and difficult to calibrate, whereas RLS produces more psychologically plausible patterns but with lower reliability. Our results suggest that the same mechanisms that improve rationality also amplify sensitivity to affective interventions, and that different steering methods trade off controllability against human-aligned behavior. Overall, this points to a tension between reasoning and affective steering, with implications for both human simulation and the safe deployment of LLM-based decision systems.
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https://arxiv.org/abs/2601.22329
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Academic Papers
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f735432089671b0e410d26217a01cb282148c5a589e197988128d725dcb9aa2b
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2026-02-02T00:00:00-05:00
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Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling
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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 batch effects arising from differences in laboratories, instruments, and protocols, which can obscure biological signal. We present BALANS (Batch Alignment via Local Affinities and Subsampling), a scalable batch-correction method that aligns samples across batches by constructing a smoothed affinity matrix from pairwise distances. Given $n$ data points, BALANS builds a sparse affinity matrix $A \in \mathbb{R}^{n \times n}$ using two ideas. (i) For points $i$ and $j$, it sets a local scale using the distance from $i$ to its $k$-th nearest neighbor within the batch of $j$, then computes $A_{ij}$ via a Gaussian kernel calibrated by these batch-aware local scales. (ii) Rather than forming all $n^2$ entries, BALANS uses an adaptive sampling procedure that prioritizes rows with low cumulative neighbor coverage and retains only the strongest affinities per row, yielding a sparse but informative approximation of $A$. We prove that this sampling strategy is order-optimal in sample complexity and provides an approximation guarantee, and we show that BALANS runs in nearly linear time in $n$. Experiments on diverse real-world Cell Painting datasets and controlled large-scale synthetic benchmarks demonstrate that BALANS scales to large collections while improving runtime over native implementations of widely used batch-correction methods, without sacrificing correction quality.
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https://arxiv.org/abs/2601.22331
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Academic Papers
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413af986448f9da2dea36c5cfc0fa131faee1d093fe92c2f406dcef981c9f377
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2026-02-02T00:00:00-05:00
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DP-$\lambda$CGD: Efficient Noise Correlation for Differentially Private Model Training
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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. Matrix factorization mechanisms are a prominent example, but they correlate noise across many iterations and require storing previously added noise vectors, leading to substantial memory overhead in some settings. In this work, we propose a new noise correlation strategy that correlates noise only with the immediately preceding iteration and cancels a controlled portion of it. Our method relies on noise regeneration using a pseudorandom noise generator, eliminating the need to store past noise. As a result, it requires no additional memory beyond standard DP-SGD. We show that the computational overhead is minimal and empirically demonstrate improved accuracy over DP-SGD.
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https://arxiv.org/abs/2601.22334
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Academic Papers
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7f15453212e681d72c1143233a22c169d3c5738aab5b66450afe4bda53b0589f
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2026-02-02T00:00:00-05:00
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Knowledge Gradient for Preference Learning
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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 direct function evaluations are unavailable. Extending the knowledge gradient to preferential BO is hindered by its computational challenge. At its core, the look-ahead step in the preferential setting requires computing a non-Gaussian posterior, which was previously considered intractable. In this paper, we address this challenge by deriving an exact and analytical knowledge gradient for preferential BO. We show that the exact knowledge gradient performs strongly on a suite of benchmark problems, often outperforming existing acquisition functions. In addition, we also present a case study illustrating the limitation of the knowledge gradient in certain scenarios.
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https://arxiv.org/abs/2601.22335
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Academic Papers
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dd59193b3db55906e6340edb54b7832acf7a953cc949d867c58ca3b77dbbc4e3
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2026-02-02T00:00:00-05:00
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From Retrieving Information to Reasoning with AI: Exploring Different Interaction Modalities to Support Human-AI Coordination in Clinical Decision-Making
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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-support systems (CDSS) restricts designing novel mechanisms that overcome existing tool limitations and enhance performance and experience. This qualitative study examines how clinicians (n=12) perceive different interaction modalities (text-based conversation with LLMs, interactive and static UI, and voice) for decision-support. In open-ended use of LLM-based tools, our participants took a tool-centric approach using them for information retrieval and confirmation with simple prompts instead of use as active deliberation partners that can handle complex questions. Critical engagement emerged with changes to the interaction setup. Engagement also differed with individual cognitive styles. Lastly, benefits and drawbacks of interaction with text, voice and traditional UIs for clinical decision-support show the lack of a one-size-fits-all interaction modality.
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https://arxiv.org/abs/2601.22338
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aa08b3bb291f4f2f591cdcbab58a0dd028a7bb3705ace34bf2cd8fd52b017a01
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2026-02-02T00:00:00-05:00
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Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems
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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 overlook crucial sustainability goals and cyber vulnerabilities, leaving systems susceptible to both ecological harm and malicious attacks. To tackle these challenges simultaneously, this work integrates a quantum-inspired reinforcement learning framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures. We design a quantum-inspired reinforcement learning framework that couples a controllable spin-chain analogy with real-time AIoT signals and optimizes a multi-objective reward unifying fidelity, security, and carbon costs. The approach learns robust policies with stabilized training via value-based and ensemble updates, supported by window-normalized reward components to ensure commensurate scaling. In simulation, the method exhibits smooth convergence, strong late-episode performance, and graceful degradation under representative noise channels, outperforming standard learned and model-based references, highlighting its robust handling of real-time sustainability and risk demands. These findings reinforce the potential for quantum-inspired AIoT frameworks to drive secure, eco-conscious supply chain operations at scale, laying the groundwork for globally connected infrastructures that responsibly meet both consumer and environmental needs.
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https://arxiv.org/abs/2601.22339
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a373049d21356e37f62d6bd435c9dd4c251d46c37b71a516687d1d0b5ff7187d
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2026-02-02T00:00:00-05:00
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Convergence Analysis of the Discrete Constrained Saddle Dynamics and Their Momentum Variants
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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 the convergence rate depends on the condition number of the Riemannian Hessian. To mitigate this dependence, we introduce a momentum-based constrained saddle dynamics and prove local convergence of the continuous-time dynamics as well as the corresponding discrete scheme, which further demonstrates that momentum accelerates convergence, particularly in ill-conditioned settings. In addition, we show that a single-step eigenvector update is sufficient to guarantee local convergence; thus, the assumption of exact unstable eigenvectors is not necessary, which substantially reduces the computational cost. Finally, numerical experiments, including applications to the Thomson problem, the Rayleigh quotient on the Stiefel manifold, and the energy functional of Bose-Einstein condensates, are presented to complement the theoretical analysis.
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https://arxiv.org/abs/2601.22341
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e1bd38ddaf86a85a6ab795df760e6fedd63d8002ee7d10925cf7a30a93554109
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2026-02-02T00:00:00-05:00
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Low-Rank Approximation by Randomly Pivoted LU
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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 expectation for matrices with rapidly decaying singular values. RPLU outperforms existing low-rank approximation algorithms in two settings: first, when memory is limited, RPLU can be implemented with $\mathcal{O}(k^2 + m + n)$ storage and $\mathcal{O}( k(m + n)+ k\mathcal{M}(\mat{A}) + k^3)$ operations, where $\mathcal{M}(\mat{A})$ is the cost of a matvec with $\mat{A}\in\mathbb{C}^{n\times m}$ or its adjoint, for a rank-$k$ approximation. Second, when the matrix and its Schur complements share exploitable structure, such as for Cauchy-like matrices. The efficacy of RPLU is illustrated with several examples, including applications in rational approximation and solving large linear systems on GPUs.
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https://arxiv.org/abs/2601.22344
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a57df35de743e9a22c5f4e1ccb7dfd1287939adf3bc92aac1a07e8ae4589f240
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2026-02-02T00:00:00-05:00
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Failing to Explore: Language Models on Interactive Tasks
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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, we find systematic under-exploration and suboptimal solutions, with performance often significantly worse than simple explore--exploit heuristic baselines and scaling weakly as the budget increases. Finally, we study two lightweight interventions: splitting a fixed budget into parallel executions, which surprisingly improves performance despite a no-gain theoretical result for our tasks, and periodically summarizing the interaction history, which preserves key discoveries and further improves exploration.
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https://arxiv.org/abs/2601.22345
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Academic Papers
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9887092f3ccf0ac36b11c3ccdd5cddd96769d7c8fec7a482083913fbdb65ae07
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2026-02-02T00:00:00-05:00
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FAIRFORMER: A transformer architecture for discrete fair division
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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-equivariant two-tower transformer that encodes items and agents as unordered token sets, applies self-attention within each set, and uses item-to-agent cross-attention to produce per-item assignment distributions in a single forward pass. FairFormer is trained end-to-end to maximize expected log-Nash welfare on sampled instances, requiring no solver supervision, unrolled allocation procedures, or fairness labels. At test time, we discretize by row-wise $\arg\max$ and apply a lightweight post-processing routine that transfers items to eliminate violations of envy-freeness up to one item while prioritizing improvements in Nash welfare. Our approach generalizes beyond its training regime and achieves near-optimal welfare (e.g., for uniformly sampled valuations, $96$--$97\%$ for Nash welfare; $95$--$96\%$ for utilitarian welfare), outperforming strong baselines in solution quality and/or runtime.
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https://arxiv.org/abs/2601.22346
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Academic Papers
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da2694bd536d97a3358ac7ab5b9e1c81fd7564a90d0ac72379c8d81fecdecd6f
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2026-02-02T00:00:00-05:00
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MixQuant: Pushing the Limits of Block Rotations in Post-Training Quantization
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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, we present the first systematic, non-asymptotic analysis of outlier suppression for block Hadamard rotations. Our analysis reveals that outlier suppression is fundamentally limited by the geometry of the input vector. In particular, post-rotation outliers are deterministically minimized when the pre-rotation $\ell_1$ norm mass is evenly distributed across blocks. Guided by these insights, we introduce MixQuant, a block rotation-aware PTQ framework that redistributes activation mass via permutations prior to rotation. We propose a greedy mass diffusion algorithm to calibrate permutations by equalizing the expected blockwise $\ell_1$ norms. To avoid adding inference overhead, we identify permutation-equivariant regions in transformer architectures to merge the resulting permutations into model weights before deployment. Experiments show that MixQuant consistently improves accuracy across all block sizes, recovering up to 90% of the full-vector rotation perplexity when quantizing Llama3 1B to INT4 with block size 16, compared to 46% without permutations.
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https://arxiv.org/abs/2601.22347
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Academic Papers
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735e87b63bfbe557a9a93acb53499d25f7ec3a8f3cd4427b0c2469d709d00acc
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2026-02-02T00:00:00-05:00
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Forward-KL Convergence of Time-Inhomogeneous Langevin Diffusions
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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 provide a convergence analysis that includes non-asymptotic bounds for the continuous-time diffusion and its Euler--Maruyama discretization in the forward-Kullback--Leibler divergence under a single set of abstract conditions on the time-dependent drift. The results apply to many practically-relevant annealing schemes, including geometric tempering and annealed Langevin sampling. In addition, we provide numerical experiments comparing the annealing schemes covered by our theory in low- and high-dimensional settings.
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https://arxiv.org/abs/2601.22349
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Academic Papers
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4a464cb0f9011b97c6ff749ae486f8bb19c5ee30e1c3a10ba5155bfabbdbb925
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2026-02-02T00:00:00-05:00
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Learning Policy Representations for Steerable Behavior Synthesis
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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 expectations of state-action feature maps with respect to occupancy measures. We show that these representations can be approximated uniformly for a range of policies using a set-based architecture. Our model encodes a set of state-action samples into a latent embedding, from which we decode both the policy and its value functions corresponding to multiple rewards. We use variational generative approach to induce a smooth latent space, and further shape it with contrastive learning so that latent distances align with differences in value functions. This geometry permits gradient-based optimization directly in the latent space. Leveraging this capability, we solve a novel behavior synthesis task, where policies are steered to satisfy previously unseen value function constraints without additional training.
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https://arxiv.org/abs/2601.22350
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Academic Papers
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b7c77563261f55a757a4d7926b9da0839f30a1af0eabe5fa43e0e86740791e07
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2026-02-02T00:00:00-05:00
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Recoverability Has a Law: The ERR Measure for Tool-Augmented Agents
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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 formalize recoverability through Expected Recovery Regret (ERR), which quantifies the deviation of a recovery policy from the optimal one under stochastic execution noise, and derive a first-order relationship between ERR and an empirical observable quantity, the Efficiency Score (ES). This yields a falsifiable first-order quantitative law of recovery dynamics in tool-using agents. We empirically validate the law across five tool-use benchmarks spanning controlled perturbations, diagnostic reasoning, and real-world APIs. Across model scales, perturbation regimes, and recovery horizons, predicted regret under the ERR-ES law closely matched observed post-failure regret measured from Monte Carlo rollouts, within delta less than or equal to 0.05. Our results reveal that recoverability is not an artifact of model scale or architecture, but a governed property of interaction dynamics, providing a theoretical foundation for execution-level robustness in language agents.
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https://arxiv.org/abs/2601.22352
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Academic Papers
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8a861e9902cd9c6c881ad5c4fd2ab2ef09d778986ef5d012ce16a0ce872c12db
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2026-02-02T00:00:00-05:00
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Relative Wasserstein Angle and the Problem of the $W_2$-Nearest Gaussian Distribution
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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 quantities, the relative Wasserstein angle and the orthogonal projection distance, which provide meaningful measures of non-Gaussianity. We prove that the filling cone generated by any two rays in this space is flat, ensuring that angles, projections, and inner products are rigorously well-defined. This geometric viewpoint recasts Gaussian approximation as a projection problem onto the Gaussian cone and reveals that the commonly used moment-matching Gaussian can \emph{not} be the \(W_2\)-nearest Gaussian for a given empirical distribution. In one dimension, we derive closed-form expressions for the proposed quantities and extend them to several classical distribution families, including uniform, Laplace, and logistic distributions; while in high dimensions, we develop an efficient stochastic manifold optimization algorithm based on a semi-discrete dual formulation. Experiments on synthetic data and real-world feature distributions demonstrate that the relative Wasserstein angle is more robust than the Wasserstein distance and that the proposed nearest Gaussian provides a better approximation than moment matching in the evaluation of Fr\'echet Inception Distance (FID) scores.
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https://arxiv.org/abs/2601.22355
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Academic Papers
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d5d5423699d6af682943f96d0f60364a3b75f22d9ea871672b389e7501d40d48
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2026-02-02T00:00:00-05:00
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PoSafeNet: Safe Learning with Poset-Structured Neural Nets
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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, safety requirements are heterogeneous and admit only partial priority relations, where some constraints are comparable while others are inherently incomparable. We formalize this setting as poset-structured safety, modeling safety constraints as a partially ordered set and treating safety composition as a structural property of the policy class. Building on this formulation, we propose PoSafeNet, a differentiable neural safety layer that enforces safety via sequential closed-form projection under poset-consistent constraint orderings, enabling adaptive selection or mixing of valid safety executions while preserving priority semantics by construction. Experiments on multi-obstacle navigation, constrained robot manipulation, and vision-based autonomous driving demonstrate improved feasibility, robustness, and scalability over unstructured and differentiable quadratic program-based safety layers.
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https://arxiv.org/abs/2601.22356
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Academic Papers
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svg
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88bff0920af5e558366789c6f71d5c78f2c7ca2bff4e83bf205ee1f92b3a3589
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2026-02-02T00:00:00-05:00
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Small Talk, Big Impact: The Energy Cost of Thanking AI
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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 energy measurements, we quantify how input length, output length and model size affect energy use. While politeness is our motivating example, it also serves as a controlled and reproducible proxy for measuring the energy footprint of a typical LLM interaction. Our findings provide actionable insights for building more sustainable and efficient LLM applications, especially in increasingly widespread real-world contexts like chat. As user adoption grows and billions of prompts are processed daily, understanding and mitigating this cost becomes crucial - not just for efficiency, but for sustainable AI deployment.
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https://arxiv.org/abs/2601.22357
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Academic Papers
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