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ff6b1008ef0fc0293e087c16567dbc96164421c150c862c53998a78f9f90d41a
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2026-01-22T07:00:22+00:00
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In Science Journals
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Science, Volume 391, Issue 6783, Page 363-365, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aef5688?af=R
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Academic Papers
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654bb6d5317a419342b292fd1dd51fa1df307c1160a34eba3286cd521de6fb14
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2026-01-22T07:00:22+00:00
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Editorial Expression of Concern for the Report “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances”
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Science, Volume 391, Issue 6783, Page 360-360, January 2026.
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https://www.science.org/doi/abs/10.1126/science.aee6983?af=R
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Academic Papers
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svg
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7beb35b17a6ed254b7d8a48ead5fe03b7e7d2f78c9470973c0ae44174acfd00e
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2026-01-23T00:00:00-05:00
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Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarios
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arXiv:2601.15290v1 Announce Type: new Abstract: Testing conversational AI systems at scale across diverse domains necessitates realistic and diverse user interactions capturing a wide array of behavioral patterns. We present a novel multi-agent framework for realistic, explainable human user simulation in interactive scenarios, using persona control and task state tracking to mirror human cognitive processes during goal-oriented conversations. Our system employs three specialized AI agents: (1) a User Agent to orchestrate the overall interaction, (2) a State Tracking Agent to maintain structured task state, and (3) a Message Attributes Generation Agent that controls conversational attributes based on task progress and assigned persona. To validate our approach, we implement and evaluate the framework for guest ordering at a restaurant with scenarios rich in task complexity, behavioral diversity, and conversational ambiguity. Through systematic ablations, we evaluate the contributory efficacy of each agentic component to overall simulation quality in terms of persona adherence, task completion accuracy, explainability, and realism. Our experiments demonstrate that the complete multi-agent system achieves superior simulation quality compared to single-LLM baselines, with significant gains across all evaluation metrics. This framework establishes a powerful environment for orchestrating agents to simulate human users with cognitive plausibility, decomposing the simulation into specialized sub-agents that reflect distinct aspects of human thought processes applicable across interactive domains.
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https://arxiv.org/abs/2601.15290
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Academic Papers
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6c84da605489ce8c72981561b9706b61d2e9ef442dde09e9b7abec34dba39553
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2026-01-23T00:00:00-05:00
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Public transport challenges and technology-assisted accessibility for visually impaired elderly residents in urban environments
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arXiv:2601.15291v1 Announce Type: new Abstract: Independent navigation is a core aspect of maintaining social participation and individual health for vulnerable populations. While historic cities such as Edinburgh, as the capital of Scotland, often feature well-established public transport systems, urban accessibility challenges remain and are exacerbated by a complex landscape, especially for groups with multiple vulnerabilities such as the blind elderly. With limited research examining how real-time data feeds and developments in artificial intelligence can enhance navigation aids, we address this gap through a mixed-methods approach. Our work combines statistical and machine learning techniques, with a focus on spatial analysis to investigate network coverage, service patterns, and density through live Transport for Edinburgh data, with a qualitative thematic analysis of semi-structured interviews with the mentioned target group. The results demonstrate the highly centralised nature of the city's transport system, the significance of memory-based navigation, and the lack of travel information in usable formats. We also find that participants already use navigation technology to varying degrees and express a willingness to adopt artificial intelligence. Our analysis highlights the importance of dynamic tools in terms of sensory and cognitive needs to meaningfully improve independent travel.
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https://arxiv.org/abs/2601.15291
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Academic Papers
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0ba16c6761ad7aff12b1d1163d042cfd07cf36d591da0310b4c396200fd4036d
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2026-01-23T00:00:00-05:00
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A Mobile Application Front-End for Presenting Explainable AI Results in Diabetes Risk Estimation
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arXiv:2601.15292v1 Announce Type: new Abstract: Diabetes is a significant and continuously rising health challenge in Indonesia. Although many artificial intelligence (AI)-based health applications have been developed for early detection, most function as "black boxes," lacking transparency in their predictions. Explainable AI (XAI) methods offer a solution, yet their technical outputs are often incomprehensible to non-expert users. This research aims to develop a mobile application front-end that presents XAI-driven diabetes risk analysis in an intuitive, understandable format. Development followed the waterfall methodology, comprising requirements analysis, interface design, implementation, and evaluation. Based on user preference surveys, the application adopts two primary visualization types - bar charts and pie charts - to convey the contribution of each risk factor. These are complemented by personalized textual narratives generated via integration with GPT-4o. The application was developed natively for Android using Kotlin and Jetpack Compose. The resulting prototype interprets SHAP (SHapley Additive exPlanations), a key XAI approach, into accessible graphical visualizations and narratives. Evaluation through user comprehension testing (Likert scale and interviews) and technical functionality testing confirmed the research objectives were met. The combination of visualization and textual narrative effectively enhanced user understanding (average score 4.31/5) and empowered preventive action, supported by a 100% technical testing success rate.
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https://arxiv.org/abs/2601.15292
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Academic Papers
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05def5a25790a92f13410a80e10b5b8135a73dcf53f70493916ddc879d11eb14
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2026-01-23T00:00:00-05:00
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Social Robotics for Disabled Students: An Empirical Investigation of Embodiment, Roles and Interaction
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arXiv:2601.15293v1 Announce Type: new Abstract: Institutional and social barriers in higher education often prevent students with disabilities from effectively accessing support, including lengthy procedures, insufficient information, and high social-emotional demands. This study empirically explores how disabled students perceive robot-based support, comparing two interaction roles, one information based (signposting) and one disclosure based (sounding board), and two embodiment types (physical robot/disembodied voice agent). Participants assessed these systems across five dimensions: perceived understanding, social energy demands, information access/clarity, task difficulty, and data privacy concerns. The main findings of the study reveal that the physical robot was perceived as more understanding than the voice-only agent, with embodiment significantly shaping perceptions of sociability, animacy, and privacy. We also analyse differences between disability types. These results provide critical insights into the potential of social robots to mitigate accessibility barriers in higher education, while highlighting ethical, social and technical challenges.
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https://arxiv.org/abs/2601.15293
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Academic Papers
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210a1fe6fac038851b6d49eaf47268315f73399c5e0cc043c973e37ef73b4271
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2026-01-23T00:00:00-05:00
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KnowTeX: Visualizing Mathematical Dependencies
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arXiv:2601.15294v1 Announce Type: new Abstract: Mathematical knowledge exists in many forms, ranging from informal textbooks and lecture notes to large formal proof libraries, yet moving between these representations remains difficult. Informal texts hide dependencies, while formal systems expose every detail in ways that are not always human-readable. Dependency graphs offer a middle ground by making visible the structure of results, definitions, and proofs. We present KnowTeX, a standalone, user-friendly tool that extends the ideas of Lean's Blueprints, enabling the visualization of conceptual dependencies directly from LaTeX sources. Using a simple "uses" command, KnowTeX extracts relationships among statements and generates previewable graphs in DOT and TikZ formats. Applied to mathematical texts, such graphs clarify core results, support education and formalization, and provide a resource for aligning informal and formal mathematical representations. We argue that dependency graphs should become a standard feature of mathematical writing, benefiting both human readers and automated systems.
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https://arxiv.org/abs/2601.15294
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Academic Papers
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ef8330f089220902622e5d2b6d34c84f927e2c1175458c880110aa9dcd95b32d
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2026-01-23T00:00:00-05:00
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Elsewise: Authoring AI-Based Interactive Narrative with Possibility Space Visualization
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arXiv:2601.15295v1 Announce Type: new Abstract: Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determining which narrative possibilities they actually experience. Generative AI can enable new forms of IN by improvisationally expanding on pre-authored content in response to open-ended player input. However, this extrapolation risks widening the gap between author-envisioned and player-experienced stories, potentially limiting the strength of plot progression and the communication of the author's narrative intent. To bridge the gap, we introduce Elsewise: an authoring tool for AI-based INs that implements a novel Bundled Storyline concept to enhance author's perception and understanding of the narrative possibility space, allowing authors to explore similarities and differences between possible playthroughs of their IN in terms of open-ended, user-configurable narrative dimensions. A user study (n=12) shows that our approach improves author anticipation of player-experienced narrative, leading to more effective control and exploration of the narrative possibility spaces.
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https://arxiv.org/abs/2601.15295
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Academic Papers
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svg
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0d5e3b9393a5c70cf8beaf8c7bae7405420ef0fa120a9e5bf0f9712f168d162d
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2026-01-23T00:00:00-05:00
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Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration
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arXiv:2601.15296v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance, yet existing decoding strategies either explore blindly (random sampling) or redundantly (independent multi-sampling). We propose Entropy-Tree, a tree-based decoding method that exploits entropy as a signal for branching decisions--expanding the search tree only at positions where the model exhibits genuine uncertainty. Entropy-Tree shows superior accuracy and calibration in reasoning tasks: it achieves better pass@k than Multi-chain across multiple models and datasets, and its predictive entropy demonstrates better AUROC compared to several traditional metrics. Entropy-Tree unifies efficient structured exploration and reliable uncertainty estimation within a single decoding procedure.
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https://arxiv.org/abs/2601.15296
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Academic Papers
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svg
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883883b3079df370054df44ecea51f46bbabdecd9781734f6e78b9445e320169
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2026-01-23T00:00:00-05:00
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AfriEconQA: A Benchmark Dataset for African Economic Analysis based on World Bank Reports
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arXiv:2601.15297v1 Announce Type: new Abstract: We introduce AfriEconQA, a specialized benchmark dataset for African economic analysis grounded in a comprehensive corpus of 236 World Bank reports. The task of AfriEconQA is to answer complex economic queries that require high-precision numerical reasoning and temporal disambiguation from specialized institutional documents. The dataset consists of 8,937 curated QA instances, rigorously filtered from a pool of 10018 synthetic questions to ensure high-quality evidence-answer alignment. Each instance is composed of: (1) a question requiring reasoning over economic indicators, (2) the corresponding evidence retrieved from the corpus, (3) a verified ground-truth answer, and (4) source metadata (e.g., URL and publication date) to ensure temporal provenance. AfriEconQA is the first benchmark focused specifically on African economic analysis, providing a unique challenge for Information Retrieval (IR) systems, as the data is largely absent from the pretraining corpora of current Large Language Models (LLMs). We operationalize this dataset through an 11-experiment matrix, benchmarking a zero-shot baseline (GPT-5 Mini) against RAG configurations using GPT-4o and Qwen 32B across five distinct embedding and ranking strategies. Our results demonstrate a severe parametric knowledge gap, where zero-shot models fail to answer over 90 percent of queries, and even state-of-the-art RAG pipelines struggle to achieve high precision. This confirms AfriEconQA as a robust and challenging benchmark for the next generation of domain-specific IR and RAG systems. The AfriEconQA dataset and code will be made publicly available upon publication.
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https://arxiv.org/abs/2601.15297
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Academic Papers
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9520ddf70893676efc3d393155ff8f9ddcc78e02868a2fd14e08be5e264d030f
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2026-01-23T00:00:00-05:00
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Embedding Retrofitting: Data Engineering for better RAG
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arXiv:2601.15298v1 Announce Type: new Abstract: Embedding retrofitting adjusts pre-trained word vectors using knowledge graph constraints to improve domain-specific retrieval. However, the effectiveness of retrofitting depends critically on knowledge graph quality, which in turn depends on text preprocessing. This paper presents a data engineering framework that addresses data quality degradation from annotation artifacts in real-world corpora. The analysis shows that hashtag annotations inflate knowledge graph density, leading to creating spurious edges that corrupt the retrofitting objective. On noisy graphs, all retrofitting techniques produce statistically significant degradation ($-3.5\%$ to $-5.2\%$, $p<0.05$). After preprocessing, \acrshort{ewma} retrofitting achieves $+6.2\%$ improvement ($p=0.0348$) with benefits concentrated in quantitative synthesis questions ($+33.8\%$ average). The gap between clean and noisy preprocessing (10\%+ swing) exceeds the gap between algorithms (3\%), establishing preprocessing quality as the primary determinant of retrofitting success.
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https://arxiv.org/abs/2601.15298
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Academic Papers
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svg
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26e7e1e82e0d107da5528098e516275f64880d4917fc2b6f095a864fd5273fa7
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2026-01-23T00:00:00-05:00
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MALTopic: Multi-Agent LLM Topic Modeling Framework
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arXiv:2601.15299v1 Announce Type: new Abstract: Topic modeling is a crucial technique for extracting latent themes from unstructured text data, particularly valuable in analyzing survey responses. However, traditional methods often only consider free-text responses and do not natively incorporate structured or categorical survey responses for topic modeling. And they produce abstract topics, requiring extensive human interpretation. To address these limitations, we propose the Multi-Agent LLM Topic Modeling Framework (MALTopic). This framework decomposes topic modeling into specialized tasks executed by individual LLM agents: an enrichment agent leverages structured data to enhance textual responses, a topic modeling agent extracts latent themes, and a deduplication agent refines the results. Comparative analysis on a survey dataset demonstrates that MALTopic significantly improves topic coherence, diversity, and interpretability compared to LDA and BERTopic. By integrating structured data and employing a multi-agent approach, MALTopic generates human-readable topics with enhanced contextual relevance, offering a more effective solution for analyzing complex survey data.
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https://arxiv.org/abs/2601.15299
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Academic Papers
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602c98e4c59755b6b255bc8573b811dfc2035a251285826dd52ed15e143977ea
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2026-01-23T00:00:00-05:00
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Intelligence Degradation in Long-Context LLMs: Critical Threshold Determination via Natural Length Distribution Analysis
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arXiv:2601.15300v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit catastrophic performance degradation when processing contexts approaching certain critical thresholds, even when information remains relevant. This intelligence degradation-defined as over 30% drop in task performance-severely limits long-context applications. This degradation shows a common pattern: models maintain strong performance up to a critical threshold, then collapse catastrophically. We term this shallow long-context adaptation-models adapt for short to medium contexts but fail beyond critical thresholds. This paper presents three contributions: (1) Natural Length Distribution Analysis: We use each sample's natural token length without truncation or padding, providing stronger causal evidence that degradation results from context length itself. (2) Critical Threshold Determination: Through experiments on a mixed dataset (1,000 samples covering 5%-95% of context length), we identify the critical threshold for Qwen2.5-7B at 40-50% of maximum context length, where F1 scores drop from 0.55-0.56 to 0.3 (45.5% degradation), using five-method cross-validation. (3) Unified Framework: We consolidate shallow adaptation, explaining degradation patterns and providing a foundation for mitigation strategies. This work provides the first systematic characterization of intelligence degradation in open-source Qwen models, offering practical guidance for deploying LLMs in long-context scenarios.
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https://arxiv.org/abs/2601.15300
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Academic Papers
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svg
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6a8372dc691bcb98060a91ff07a9b70b51eb17872322fc79eef7b95109c9150a
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2026-01-23T00:00:00-05:00
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Can We Trust LLM Detectors?
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arXiv:2601.15301v1 Announce Type: new Abstract: The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training-free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in-domain, they degrade sharply out-of-domain, and training-free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain-agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI
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https://arxiv.org/abs/2601.15301
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Academic Papers
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eb43dab26339fe9d32cd531584137f9d378194d947135457dee1a59067169c3c
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2026-01-23T00:00:00-05:00
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Gated Sparse Attention: Combining Computational Efficiency with Training Stability for Long-Context Language Models
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arXiv:2601.15305v1 Announce Type: new Abstract: The computational burden of attention in long-context language models has motivated two largely independent lines of work: sparse attention mechanisms that reduce complexity by attending to selected tokens, and gated attention variants that improve training sta-bility while mitigating the attention sink phenomenon. We observe that these approaches address complementary weaknesses and propose Gated Sparse Attention (GSA), an architecture that realizes the benefits of both. GSA incorporates a gated lightning indexer with sigmoid activations that produce bounded, interpretable selection scores, an adaptive sparsity controller that modulates the number of attended tokens based on local uncertainty, and dual gating at the value and output stages. We establish theoretical foundations for the approach, including complexity analysis, expressiveness results, and convergence guarantees. In experiments with 1.7B parameter models trained on 400B tokens, GSA matches the efficiency of sparse-only baselines (12-16x speedup at 128K context) while achieving the quality gains associated with gated attention: perplexity improves from 6.03 to 5.70, RULER scores at 128K context nearly double, and attention to the first token, a proxy for attention sinks, drops from 47% to under 4%. Training stability improves markedly, with loss spikes reduced by 98%.
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https://arxiv.org/abs/2601.15305
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Academic Papers
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906607b17f20ad534584176a81016c398bb5c96b96d8010fa7fe4bb4f9467a21
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2026-01-23T00:00:00-05:00
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Uncovering Latent Bias in LLM-Based Emergency Department Triage Through Proxy Variables
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arXiv:2601.15306v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled their integration into clinical decision-making; however, hidden biases against patients across racial, social, economic, and clinical backgrounds persist. In this study, we investigate bias in LLM-based medical AI systems applied to emergency department (ED) triage. We employ 32 patient-level proxy variables, each represented by paired positive and negative qualifiers, and evaluate their effects using both public (MIMIC-IV-ED Demo, MIMIC-IV Demo) and restricted-access credentialed (MIMIC-IV-ED and MIMIC-IV) datasets as appropriate~\cite{mimiciv_ed_demo,mimiciv_ed,mimiciv}. Our results reveal discriminatory behavior mediated through proxy variables in ED triage scenarios, as well as a systematic tendency for LLMs to modify perceived patient severity when specific tokens appear in the input context, regardless of whether they are framed positively or negatively. These findings indicate that AI systems is still imperfectly trained on noisy, sometimes non-causal signals that do not reliably reflect true patient acuity. Consequently, more needs to be done to ensure the safe and responsible deployment of AI technologies in clinical settings.
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https://arxiv.org/abs/2601.15306
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Academic Papers
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149330be11ba97ce3d25b73a72a4f5c2febc4f47929a8595c414192ade5e4023
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2026-01-23T00:00:00-05:00
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DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Survey
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arXiv:2601.15307v1 Announce Type: new Abstract: The rapid development of automated scientific survey generation technology has made it increasingly important to establish a comprehensive benchmark to evaluate the quality of generated surveys.Nearly all existing evaluation benchmarks rely on flawed selection criteria such as citation counts and structural coherence to select human-written surveys as the ground truth survey datasets, and then use surface-level metrics such as structural quality and reference relevance to evaluate generated surveys.However, these benchmarks have two key issues: (1) the ground truth survey datasets are unreliable because of a lack academic dimension annotations; (2) the evaluation metrics only focus on the surface quality of the survey such as logical coherence. Both issues lead to existing benchmarks cannot assess to evaluate their deep "academic value", such as the core research objectives and the critical analysis of different studies. To address the above problems, we propose DeepSurvey-Bench, a novel benchmark designed to comprehensively evaluate the academic value of generated surveys. Specifically, our benchmark propose a comprehensive academic value evaluation criteria covering three dimensions: informational value, scholarly communication value, and research guidance value. Based on this criteria, we construct a reliable dataset with academic value annotations, and evaluate the deep academic value of the generated surveys. Extensive experimental results demonstrate that our benchmark is highly consistent with human performance in assessing the academic value of generated surveys.
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https://arxiv.org/abs/2601.15307
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Academic Papers
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617c581d5e249e89558e52da1e42778479c02e8dd963bab8d11db7b7bb89e55b
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2026-01-23T00:00:00-05:00
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When Generative AI Meets Extended Reality: Enabling Scalable and Natural Interactions
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arXiv:2601.15308v1 Announce Type: new Abstract: Extended Reality (XR), including virtual, augmented, and mixed reality, provides immersive and interactive experiences across diverse applications, from VR-based education to AR-based assistance and MR-based training. However, widespread XR adoption remains limited due to two key challenges: 1) the high cost and complexity of authoring 3D content, especially for large-scale environments or complex interactions; and 2) the steep learning curve associated with non-intuitive interaction methods like handheld controllers or scripted gestures. Generative AI (GenAI) presents a promising solution by enabling intuitive, language-driven interaction and automating content generation. Leveraging vision-language models and diffusion-based generation, GenAI can interpret ambiguous instructions, understand physical scenes, and generate or manipulate 3D content, significantly lowering barriers to XR adoption. This paper explores the integration of XR and GenAI through three concrete use cases, showing how they address key obstacles in scalability and natural interaction, and identifying technical challenges that must be resolved to enable broader adoption.
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https://arxiv.org/abs/2601.15308
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Academic Papers
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c31543d45882981f06711a82ab5e08dfc30e43e4154bd52e2f856d21a7e3a279
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2026-01-23T00:00:00-05:00
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Designing Persuasive Social Robots for Health Behavior Change: A Systematic Review of Behavior Change Strategies and Evaluation Methods
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arXiv:2601.15309v1 Announce Type: new Abstract: Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited. This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes. Relevant literature was identified through systematic database searches and hand searches. Analysis of 39 studies revealed four overarching categories of behavior change strategies: coaching strategies, counseling strategies, social influence strategies, and persuasion-enhancing strategies. These strategies highlight the unique affordances of social robots as behavior change interventions and offer valuable design heuristics. The review also identified key characteristics of current evaluation practices, including study designs, settings, durations, and outcome measures, on the basis of which we propose several directions for future HRI research.
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https://arxiv.org/abs/2601.15309
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Academic Papers
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svg
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71da0366fcdbf2aa212cfc0acb4c29a2fcd4c2fa61748688ae176e986e4f9e30
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2026-01-23T00:00:00-05:00
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Aeon: High-Performance Neuro-Symbolic Memory Management for Long-Horizon LLM Agents
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arXiv:2601.15311v1 Announce Type: new Abstract: Large Language Models (LLMs) are fundamentally constrained by the quadratic computational cost of self-attention and the "Lost in the Middle" phenomenon, where reasoning capabilities degrade as context windows expand. Existing solutions, primarily "Flat RAG" architectures relying on vector databases, treat memory as an unstructured bag of embeddings. This approach fails to capture the hierarchical and temporal structure of long-horizon interactions, leading to "Vector Haze", the retrieval of disjointed facts lacking episodic continuity. We propose Aeon, a Neuro-Symbolic Cognitive Operating System that redefines memory not as a static store, but as a managed OS resource. Aeon structures memory into a Memory Palace (a spatial index implemented via Atlas, a SIMD-accelerated Page-Clustered Vector Index that combines small-world graph navigation with B+ Tree-style disk locality to minimize read amplification) and a Trace (a neuro-symbolic episodic graph). We introduce the Semantic Lookaside Buffer (SLB), a predictive caching mechanism that exploits conversational locality to achieve sub-millisecond retrieval latencies. Benchmarks demonstrate that Aeon achieves < 1ms retrieval latency on conversational workloads while ensuring state consistency via a zero-copy C++/Python bridge, effectively enabling persistent, structured memory for autonomous agents.
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https://arxiv.org/abs/2601.15311
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Academic Papers
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1903278c8c0a11b96fa0f8708a5521b3be3f99a1e8b85eda4594e29f28573820
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2026-01-23T00:00:00-05:00
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Do people expect different behavior from large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games
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arXiv:2601.15312v1 Announce Type: new Abstract: While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component.
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https://arxiv.org/abs/2601.15312
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Academic Papers
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4183d86febb059786e1755d7e7a6d0a32ae9a1d16e1871b476e0d80a4e3e32f3
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2026-01-23T00:00:00-05:00
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The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
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arXiv:2601.15316v1 Announce Type: new Abstract: In recent years, the rapid evolution of large vision-language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional feature-engineering approaches to unified, end-to-end multimodal reasoning frameworks. Early methods primarily relied on shallow fusion techniques to capture correlations between text and images, but they struggled with high-level semantic understanding and complex cross-modal interactions. The emergence of LVLMs has fundamentally changed this landscape by enabling joint modeling of vision and language with powerful representation learning, thereby enhancing the ability to detect misinformation that leverages both textual narratives and visual content. Despite these advances, the field lacks a systematic survey that traces this transition and consolidates recent developments. To address this gap, this paper provides a comprehensive review of MFND through the lens of LVLMs. We first present a historical perspective, mapping the evolution from conventional multimodal detection pipelines to foundation model-driven paradigms. Next, we establish a structured taxonomy covering model architectures, datasets, and performance benchmarks. Furthermore, we analyze the remaining technical challenges, including interpretability, temporal reasoning, and domain generalization. Finally, we outline future research directions to guide the next stage of this paradigm shift. To the best of our knowledge, this is the first comprehensive survey to systematically document and analyze the transformative role of LVLMs in combating multimodal fake news. The summary of existing methods mentioned is in our Github: \href{https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection}{https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection}.
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https://arxiv.org/abs/2601.15316
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Academic Papers
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96694704c46d05cb3d96c82126abffa69f2b668cc29712f988b7e35dfc9bac5e
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2026-01-23T00:00:00-05:00
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On the closest balanced game
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arXiv:2601.15318v1 Announce Type: new Abstract: Cooperative games with nonempty core are called balanced, and the set of balanced games is a polyhedron. Given a game with empty core, we look for the closest balanced game, in the sense of the (weighted) Euclidean distance, i.e., the orthogonal projection of the game on the set of balanced games. Besides an analytical approach which becomes rapidly intractable, we propose a fast algorithm to find the closest balanced game, avoiding exponential complexity for the optimization problem, and being able to run up to 20 players. We show experimentally that the probability that the closest game has a core reduced to a singleton tends to 1 when the number of players grow. We provide a mathematical proof that the proportion of facets whose games have a non-singleton core tends to 0 when the number of players grow, by finding an expression of the aymptotic growth of the number of minimal balanced collections. This permits to prove mathematically the experimental result. Consequently, taking the core of the projected game defines a new solution concept, which we call least square core due to its analogy with the least core, and our result shows that the probability that this is a point solution tends to 1 when the number of players grow.
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https://arxiv.org/abs/2601.15318
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Academic Papers
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f8c138ab4b4fd2746582977d05da7912feee9ee34d19e11181d430e5e3e6af50
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2026-01-23T00:00:00-05:00
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Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents
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arXiv:2601.15322v1 Announce Type: new Abstract: LLM agents struggle with regulatory audit replay: when asked to reproduce a flagged transaction decision with identical inputs, most deployments fail to return consistent results. This paper introduces the Determinism-Faithfulness Assurance Harness (DFAH), a framework for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using agents deployed in financial services. Across 74 configurations (12 models, 4 providers, 8-24 runs each at T=0.0) in non-agentic baseline experiments, 7-20B parameter models achieved 100% determinism, while 120B+ models required 3.7x larger validation samples to achieve equivalent statistical reliability. Agentic tool-use introduces additional variance (see Tables 4-7). Contrary to the assumed reliability-capability trade-off, a positive Pearson correlation emerged (r = 0.45, p < 0.01, n = 51 at T=0.0) between determinism and faithfulness; models producing consistent outputs also tended to be more evidence-aligned. Three financial benchmarks are provided (compliance triage, portfolio constraints, DataOps exceptions; 50 cases each) along with an open-source stress-test harness. In these benchmarks and under DFAH evaluation settings, Tier 1 models with schema-first architectures achieved determinism levels consistent with audit replay requirements.
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https://arxiv.org/abs/2601.15322
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Academic Papers
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b80f1095036962bbd0a62df69096cab2550a6ac2be6f2a473bceecfbb2be90bb
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2026-01-23T00:00:00-05:00
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Prometheus Mind: Retrofitting Memory to Frozen Language Models
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arXiv:2601.15324v1 Announce Type: new Abstract: Adding memory to pretrained language models typically requires architectural changes or weight modification. We present Prometheus Mind, which retrofits memory to a frozen Qwen3-4B using 11 modular adapters (530MB, 7% overhead) -- fully reversible by removing the adapters. Building this system required solving four problems: (1) Extraction -- we develop Contrastive Direction Discovery (CDD), which finds semantic directions via minimal pairs without labeled data. (2) Training -- end-to-end optimization collapses; stage-wise training of each adapter on simple proxy tasks succeeds. (3) Injection -- learned encoders fail to generalize; we find that lm_head.weight rows already provide the mapping we need, requiring no training. (4) Hidden state collapse -- transformers make ``wife'' and ``brother'' 0.98+ similar; we train projections to recover distinction (0.98 $\rightarrow$ 0.09). On PrometheusExtract-132 (132 cases), the system achieves 94.4% retrieval on clean inputs (n=54, 95% CI: [84.9%, 98.1%]), degrading to 19.4% on informal inputs with ellipsis, filler words, or implicit subjects (n=36). The primary bottleneck is relation classification (47.3% accuracy), responsible for most extraction errors.
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https://arxiv.org/abs/2601.15324
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Academic Papers
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03a2ebd666cf56f351d2b437793a3cf5ded65c8f65eac73bafd7953fc7b28fa0
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2026-01-23T00:00:00-05:00
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MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection
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arXiv:2601.15325v1 Announce Type: new Abstract: Dynamic community detection plays a crucial role in understanding the temporal evolution of community structures in complex networks. Existing methods based on nonnegative tensor RESCAL decomposition typically require the decomposition rank to equal the number of communities, which limits model flexibility. This paper proposes an improved MLP-enhanced nonnegative tensor decomposition model (MLP-NTD) that incorporates a multilayer perceptron (MLP) after RESCAL decomposition for community mapping, thereby decoupling the decomposition rank from the number of communities. The framework optimizes model parameters through a reconstruction loss function, which preserves the ability to capture dynamic community evolution while significantly improving the accuracy and robustness of community partitioning. Experimental results on multiple real-world dynamic network datasets demonstrate that MLP-NTD outperforms state-of-the-art methods in terms of modularity, validating the effectiveness of the proposed approach.
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https://arxiv.org/abs/2601.15325
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Academic Papers
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a72ef1ad2dabee5a6d42e8e0d799b6e39c53b64fae455a5a9744432450f89add
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2026-01-23T00:00:00-05:00
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Rules Create Unequal Rewards: Elite Tennis Players Allocate Resources Efficiently
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arXiv:2601.15327v1 Announce Type: new Abstract: In many competitive settings, from education to politics, rules do not reward effort evenly, and thresholds (e.g., grade cutoffs or electoral majorities) make some moments disproportionately important. Success thus depends on efficiently allocating limited resources. However, empirical demonstration has been difficult because effort allocation is rarely observable and feedback is often delayed, limiting our understanding of expertise. Professional tennis provides an ideal natural experiment. Because each game resets after a player wins four points and points in a lost game are wasted, the value of a point varies sharply across scores. Efficient allocation should therefore win games without wasting points, conserving resources for future games. Such allocation manifests in score-dependent point-winning probabilities, from which we derive each player's Pareto frontier-the theoretical limit of the trade-off between game-winning probability and the expected points per game. Here, we show that top players operate closer to this frontier, converting points to game wins more efficiently. Optimal strategies reduce the probability of winning points when the player is far behind (e.g.,0-2, 0-3). This behavior is psychologically difficult-letting go of the current game-but represents a rational energy conservation strategy. Top players exhibit this pattern especially in return games, where winning points is harder than in service games, requiring them to drastically vary their efforts, consistent with game-theoretic predictions. These findings suggest that elite performance reflects efficient adaptation to rule-created value structures; knowing when to give up may be as fundamental to expertise as knowing when to compete.
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https://arxiv.org/abs/2601.15327
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Academic Papers
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09d14d0f215d39ecef298035cce23f21ff15ed5cd35b1c93da34ca9fdaf53a9f
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2026-01-23T00:00:00-05:00
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ICPO: Illocution-Calibrated Policy Optimization for Multi-Turn Conversation
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arXiv:2601.15330v1 Announce Type: new Abstract: Large Language Models (LLMs) in multi-turn conversations often suffer from a ``lost-in-conversation'' phenomenon, where they struggle to recover from early incorrect assumptions, particularly when users provide ambiguous initial instructions. We find that standard post-training techniques like Reinforcement Learning with Verifiable Rewards (RLVR) exacerbate this issue by rewarding confident, direct answers, thereby inducing overconfidence and discouraging the model from seeking clarification. To address this, we propose Illocution-Calibrated Policy Optimization (ICPO), a novel training framework that sensitizes the model to instruction ambiguity. ICPO augments the training corpus with underspecified prompts and conditions the reward signal on the user's illocutionary intent, rewarding the model for expressing uncertainty or asking for clarification when faced with ambiguity. Experiments demonstrate that ICPO fosters appropriate humility, yielding a substantial average improvement of 75\% in multi-turn conversation, while preserving robust performance on single-turn benchmarks. Our work presents a practical path toward more robust and collaborative conversational AI that can better navigate the nuances of human interaction.
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https://arxiv.org/abs/2601.15330
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Academic Papers
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7001214685803a0227c01cb884dadf0b5ad668c62f0b12bab37717a70bb694e6
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2026-01-23T00:00:00-05:00
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RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models
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arXiv:2601.15331v1 Announce Type: new Abstract: The deployment of large language models (LLMs) has raised security concerns due to their susceptibility to producing harmful or policy-violating outputs when exposed to adversarial prompts. While alignment and guardrails mitigate common misuse, they remain vulnerable to automated jailbreaking methods such as GCG, PEZ, and GBDA, which generate adversarial suffixes via training and gradient-based search. Although effective, these methods particularly GCG are computationally expensive, limiting their practicality for organisations with constrained resources. This paper introduces a resource-efficient adversarial prompting approach that eliminates the need for retraining by matching new prompts to a database of pre-trained adversarial prompts. A dataset of 1,000 prompts was classified into seven harm-related categories, and GCG, PEZ, and GBDA were evaluated on a Llama 3 8B model to identify the most effective attack method per category. Results reveal a correlation between prompt type and algorithm effectiveness. By retrieving semantically similar successful adversarial prompts, the proposed method achieves competitive attack success rates with significantly reduced computational cost. This work provides a practical framework for scalable red-teaming and security evaluation of aligned LLMs, including in settings where model internals are inaccessible.
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https://arxiv.org/abs/2601.15331
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Academic Papers
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6a700598af08f458aeb6963282ea3dcc0e405703223b9611338c87f4a9ae621b
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2026-01-23T00:00:00-05:00
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Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference
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arXiv:2601.15333v1 Announce Type: new Abstract: Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient understanding of protein structures and unpredictable molecular generation. To address these challenges, we propose Exploration-Augmented Latent Inference for LLMs (ELILLM), a framework that reinterprets the LLM generation process as an encoding, latent space exploration, and decoding workflow. ELILLM explicitly explores portions of the design problem beyond the model's current knowledge while using a decoding module to handle familiar regions, generating chemically valid and synthetically reasonable molecules. In our implementation, Bayesian optimization guides the systematic exploration of latent embeddings, and a position-aware surrogate model efficiently predicts binding affinity distributions to inform the search. Knowledge-guided decoding further reduces randomness and effectively imposes chemical validity constraints. We demonstrate ELILLM on the CrossDocked2020 benchmark, showing strong controlled exploration and high binding affinity scores compared with seven baseline methods. These results demonstrate that ELILLM can effectively enhance LLMs capabilities for SBDD.
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https://arxiv.org/abs/2601.15333
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Academic Papers
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d2eca52a81cf002c49d30d99665a8e65699ce6912748e84ea4a0da9350d5c571
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2026-01-23T00:00:00-05:00
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No Reliable Evidence of Self-Reported Sentience in Small Large Language Models
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arXiv:2601.15334v1 Announce Type: new Abstract: Whether language models possess sentience has no empirical answer. But whether they believe themselves to be sentient can, in principle, be tested. We do so by querying several open-weights models about their own consciousness, and then verifying their responses using classifiers trained on internal activations. We draw upon three model families (Qwen, Llama, GPT-OSS) ranging from 0.6 billion to 70 billion parameters, approximately 50 questions about consciousness and subjective experience, and three classification methods from the interpretability literature. First, we find that models consistently deny being sentient: they attribute consciousness to humans but not to themselves. Second, classifiers trained to detect underlying beliefs - rather than mere outputs - provide no clear evidence that these denials are untruthful. Third, within the Qwen family, larger models deny sentience more confidently than smaller ones. These findings contrast with recent work suggesting that models harbour latent beliefs in their own consciousness.
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https://arxiv.org/abs/2601.15334
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Academic Papers
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5146d3fa2cd5aad5caefdccc26807878e12ef4f9253d1f6a7ef569bcdc2d7b56
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2026-01-23T00:00:00-05:00
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ToolCaching: Towards Efficient Caching for LLM Tool-calling
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arXiv:2601.15335v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have revolutionized web applications, enabling intelligent search, recommendation, and assistant services with natural language interfaces. Tool-calling extends LLMs with the ability to interact with external APIs, greatly enhancing their practical utility. While prior research has improved tool-calling performance by adopting traditional computer systems techniques, such as parallel and asynchronous execution, the challenge of redundant or repeated tool-calling requests remains largely unaddressed. Caching is a classic solution to this problem, but applying it to LLM tool-calling introduces new difficulties due to heterogeneous request semantics, dynamic workloads, and varying freshness requirements, which render conventional cache policies ineffective. To address these issues, we propose ToolCaching, an efficient feature-driven and adaptive caching framework for LLM tool-calling systems. ToolCaching systematically integrates semantic and system-level features to evaluate request cacheability and estimate caching value. At its core, the VAAC algorithm integrates bandit-based admission with value-driven, multi-factor eviction, jointly accounting for request frequency, recency, and caching value. Extensive experiments on synthetic and public tool-calling workloads demonstrate that ToolCaching with VAAC achieves up to 11% higher cache hit ratios and 34% lower latency compared to standard policies, effectively accelerating LLM tool-calling in practical applications.
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https://arxiv.org/abs/2601.15335
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Academic Papers
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3db707e6c188e3c0e2e977fb18423604f17a0f24e2a44290d5f5429ae1988600
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2026-01-23T00:00:00-05:00
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Language Models Entangle Language and Culture
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arXiv:2601.15337v1 Announce Type: new Abstract: Users should not be systemically disadvantaged by the language they use for interacting with LLMs; i.e. users across languages should get responses of similar quality irrespective of language used. In this work, we create a set of real-world open-ended questions based on our analysis of the WildChat dataset and use it to evaluate whether responses vary by language, specifically, whether answer quality depends on the language used to query the model. We also investigate how language and culture are entangled in LLMs such that choice of language changes the cultural information and context used in the response by using LLM-as-a-Judge to identify the cultural context present in responses. To further investigate this, we evaluate LLMs on a translated subset of the CulturalBench benchmark across multiple languages. Our evaluations reveal that LLMs consistently provide lower quality answers to open-ended questions in low resource languages. We find that language significantly impacts the cultural context used by the model. This difference in context impacts the quality of the downstream answer.
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https://arxiv.org/abs/2601.15337
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Academic Papers
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f0a1c01863430c263e20cdbd2f0a02e9e8dfa03ac2f8b1aa197a6ae2158dbb83
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2026-01-23T00:00:00-05:00
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From Quotes to Concepts: Axial Coding of Political Debates with Ensemble LMs
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arXiv:2601.15338v1 Announce Type: new Abstract: Axial coding is a commonly used qualitative analysis method that enhances document understanding by organizing sentence-level open codes into broader categories. In this paper, we operationalize axial coding with large language models (LLMs). Extending an ensemble-based open coding approach with an LLM moderator, we add an axial coding step that groups open codes into higher-order categories, transforming raw debate transcripts into concise, hierarchical representations. We compare two strategies: (i) clustering embeddings of code-utterance pairs using density-based and partitioning algorithms followed by LLM labeling, and (ii) direct LLM-based grouping of codes and utterances into categories. We apply our method to Dutch parliamentary debates, converting lengthy transcripts into compact, hierarchically structured codes and categories. We evaluate our method using extrinsic metrics aligned with human-assigned topic labels (ROUGE-L, cosine, BERTScore), and intrinsic metrics describing code groups (coverage, brevity, coherence, novelty, JSD divergence). Our results reveal a trade-off: density-based clustering achieves high coverage and strong cluster alignment, while direct LLM grouping results in higher fine-grained alignment, but lower coverage 20%. Overall, clustering maximizes coverage and structural separation, whereas LLM grouping produces more concise, interpretable, and semantically aligned categories. To support future research, we publicly release the full dataset of utterances and codes, enabling reproducibility and comparative studies.
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https://arxiv.org/abs/2601.15338
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Academic Papers
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ffc56cd9b2f6356678ca5353025b08adfe6311435c028360ac8ef467db67580a
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2026-01-23T00:00:00-05:00
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Lost in Transcription: How Speech-to-Text Errors Derail Code Understanding
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arXiv:2601.15339v1 Announce Type: new Abstract: Code understanding is a foundational capability in software engineering tools and developer workflows. However, most existing systems are designed for English-speaking users interacting via keyboards, which limits accessibility in multilingual and voice-first settings, particularly in regions like India. Voice-based interfaces offer a more inclusive modality, but spoken queries involving code present unique challenges due to the presence of non-standard English usage, domain-specific vocabulary, and custom identifiers such as variable and function names, often combined with code-mixed expressions. In this work, we develop a multilingual speech-driven framework for code understanding that accepts spoken queries in a user native language, transcribes them using Automatic Speech Recognition (ASR), applies code-aware ASR output refinement using Large Language Models (LLMs), and interfaces with code models to perform tasks such as code question answering and code retrieval through benchmarks such as CodeSearchNet, CoRNStack, and CodeQA. Focusing on four widely spoken Indic languages and English, we systematically characterize how transcription errors impact downstream task performance. We also identified key failure modes in ASR for code and demonstrated that LLM-guided refinement significantly improves performance across both transcription and code understanding stages. Our findings underscore the need for code-sensitive adaptations in speech interfaces and offer a practical solution for building robust, multilingual voice-driven programming tools.
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https://arxiv.org/abs/2601.15339
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Academic Papers
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6a9951102944daa1c2b76c9e25ea2bca58926ce78f5c79b848dbeee9356a8703
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2026-01-23T00:00:00-05:00
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Logic Programming on Knowledge Graph Networks And its Application in Medical Domain
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arXiv:2601.15347v1 Announce Type: new Abstract: The rash development of knowledge graph research has brought big driving force to its application in many areas, including the medicine and healthcare domain. However, we have found that the application of some major information processing techniques on knowledge graph still lags behind. This defect includes the failure to make sufficient use of advanced logic reasoning, advanced artificial intelligence techniques, special-purpose programming languages, modern probabilistic and statistic theories et al. on knowledge graphs development and application. In particular, the multiple knowledge graphs cooperation and competition techniques have not got enough attention from researchers. This paper develops a systematic theory, technique and application of the concept 'knowledge graph network' and its application in medical and healthcare domain. Our research covers its definition, development, reasoning, computing and application under different conditions such as unsharp, uncertain, multi-modal, vectorized, distributed, federated. Almost in each case we provide (real data) examples and experiment results. Finally, a conclusion of innovation is provided.
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https://arxiv.org/abs/2601.15347
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Academic Papers
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44393e6d53982bfe96447fa50c619a145984a13e67143017170a4102cad010c3
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2026-01-23T00:00:00-05:00
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Abusive music and song transformation using GenAI and LLMs
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arXiv:2601.15348v1 Announce Type: new Abstract: Repeated exposure to violence and abusive content in music and song content can influence listeners' emotions and behaviours, potentially normalising aggression or reinforcing harmful stereotypes. In this study, we explore the use of generative artificial intelligence (GenAI) and Large Language Models (LLMs) to automatically transform abusive words (vocal delivery) and lyrical content in popular music. Rather than simply muting or replacing a single word, our approach transforms the tone, intensity, and sentiment, thus not altering just the lyrics, but how it is expressed. We present a comparative analysis of four selected English songs and their transformed counterparts, evaluating changes through both acoustic and sentiment-based lenses. Our findings indicate that Gen-AI significantly reduces vocal aggressiveness, with acoustic analysis showing improvements in Harmonic to Noise Ratio, Cepstral Peak Prominence, and Shimmer. Sentiment analysis reduced aggression by 63.3-85.6\% across artists, with major improvements in chorus sections (up to 88.6\% reduction). The transformed versions maintained musical coherence while mitigating harmful content, offering a promising alternative to traditional content moderation that avoids triggering the "forbidden fruit" effect, where the censored content becomes more appealing simply because it is restricted. This approach demonstrates the potential for GenAI to create safer listening experiences while preserving artistic expression.
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https://arxiv.org/abs/2601.15348
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Academic Papers
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d4815d82baddf417eae2edbfc82fde85564f70dbb1891c06b4a7dcce99854e8e
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2026-01-23T00:00:00-05:00
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Preparation and Motion Study of Magnetically Driven Micro Soft Robot Mimicking the Cownose Ray
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arXiv:2601.15349v1 Announce Type: new Abstract: In narrow, unstructured underwater environments such as environmental monitoring and minimally invasive medical procedures, micro soft robots exhibit unique advantages due to their flexible movement capabilities and small size. At the same time, applying bionic technology to the structural design of micro soft robots can significantly improve their swimming performance. However, limited by their miniaturization, these robots are difficult to power internally and usually adopt a wireless power supply method. This study designs and fabricates a magnetically responsive, cownose ray-inspired micro soft robot based on the swimming principle of the cownose ray. The robot is made of a certain proportion of NdFeB and PDMS. Then, a three-dimensional Helmholtz coil is used to generate an oscillating harmonic magnetic field to conduct swimming experiments on the robot, exploring the influence of magnetic field parameters on the robot's swimming performance. The experimental results show that the swimming speed is the fastest at B = 5 mT and f = 11 Hz, reaching 5.25 mm/s, which is about 0.5 body lengths per second. In addition, by adjusting the current direction and frequency of the coil, the robot can perform different swimming modes such as straight swimming, turning swimming, and directional swimming. By employing a stepwise adjustment method, the impact of response errors on the robot's trajectory can be effectively reduced. This study demonstrates a method for magnetically driven micro soft robots, laying a foundation for the application of wireless-driven robots in underwater narrow spaces.
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https://arxiv.org/abs/2601.15349
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Academic Papers
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1f5fcee09cd701472c4cf4c07c49ddd857974efc26080a339dfef44a6268c2e9
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2026-01-23T00:00:00-05:00
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A Prompt-Based Framework for Loop Vulnerability Detection Using Local LLMs
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arXiv:2601.15352v1 Announce Type: new Abstract: Loop vulnerabilities are one major risky construct in software development. They can easily lead to infinite loops or executions, exhaust resources, or introduce logical errors that degrade performance and compromise security. The problem are often undetected by traditional static analyzers because such tools rely on syntactic patterns, which makes them struggle to detect semantic flaws. Consequently, Large Language Models (LLMs) offer new potential for vulnerability detection because of their ability to understand code contextually. Moreover, local LLMs unlike commercial ones like ChatGPT or Gemini addresses issues such as privacy, latency, and dependency concerns by facilitating efficient offline analysis. Consequently, this study proposes a prompt-based framework that utilize local LLMs for the detection of loop vulnerabilities within Python 3.7+ code. The framework targets three categories of loop-related issues, such as control and logic errors, security risks inside loops, and resource management inefficiencies. A generalized and structured prompt-based framework was designed and tested with two locally deployed LLMs (LLaMA 3.2; 3B and Phi 3.5; 4B) by guiding their behavior via iterative prompting. The designed prompt-based framework included key safeguarding features such as language-specific awareness, code-aware grounding, version sensitivity, and hallucination prevention. The LLM results were validated against a manually established baseline truth, and the results indicate that Phi outperforms LLaMA in precision, recall, and F1-score. The findings emphasize the importance of designing effective prompts for local LLMs to perform secure and accurate code vulnerability analysis.
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https://arxiv.org/abs/2601.15352
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Academic Papers
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9698adbbfae2a1d9eb2436207b571c92a52b9937b48408c9064fe0619f1ef4ae
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2026-01-23T00:00:00-05:00
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AI-Based Culvert-Sewer Inspection
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arXiv:2601.15366v1 Announce Type: new Abstract: Culverts and sewer pipes are critical components of drainage systems, and their failure can lead to serious risks to public safety and the environment. In this thesis, we explore methods to improve automated defect segmentation in culverts and sewer pipes. Collecting and annotating data in this field is cumbersome and requires domain knowledge. Having a large dataset for structural defect detection is therefore not feasible. Our proposed methods are tested under conditions with limited annotated data to demonstrate applicability to real-world scenarios. Overall, this thesis proposes three methods to significantly enhance defect segmentation and handle data scarcity. This can be addressed either by enhancing the training data or by adjusting a models architecture. First, we evaluate preprocessing strategies, including traditional data augmentation and dynamic label injection. These techniques significantly improve segmentation performance, increasing both Intersection over Union (IoU) and F1 score. Second, we introduce FORTRESS, a novel architecture that combines depthwise separable convolutions, adaptive Kolmogorov-Arnold Networks (KAN), and multi-scale attention mechanisms. FORTRESS achieves state-of-the-art performance on the culvert sewer pipe defect dataset, while significantly reducing the number of trainable parameters, as well as its computational cost. Finally, we investigate few-shot semantic segmentation and its applicability to defect detection. Few-shot learning aims to train models with only limited data available. By employing a bidirectional prototypical network with attention mechanisms, the model achieves richer feature representations and achieves satisfactory results across evaluation metrics.
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https://arxiv.org/abs/2601.15366
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Academic Papers
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d2de1c9e15b756bc1d41340fad660d83086f81c9e9e91cb4918003e89d5c4726
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2026-01-23T00:00:00-05:00
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Improving MoE Compute Efficiency by Composing Weight and Data Sparsity
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arXiv:2601.15370v1 Announce Type: new Abstract: Mixture-of-Experts layers achieve compute efficiency through weight sparsity: each token activates only a subset of experts. Data sparsity, where each expert processes only a subset of tokens, offers a complementary axis. Expert-choice routing implements data sparsity directly but violates causality in autoregressive models, creating train-inference mismatch. We recover data sparsity within causal token-choice MoE by leveraging zero-compute (null) experts within the routing pool. When a token routes to null experts, those slots consume no compute. The standard load balancing objective trains the model to uniformly use all experts (real and null) therefore creating data sparsity in expectation without the causality violations. We evaluate on vision-language model training, where data heterogeneity is pronounced: vision encoders produce many low-information tokens while text tokens are denser. At matched expected FLOPs, composing weight and data sparsity yields a more compute-efficient frontier than weight sparsity alone, with gains in training loss and downstream performance. The model learns implicit modality-aware allocation, routing vision tokens to null experts more aggressively than text, without explicit modality routing.
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https://arxiv.org/abs/2601.15370
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Academic Papers
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e1f7ff65eed794a08205939385758ddef1124ecee386ed5eadc88a774fa1f440
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2026-01-23T00:00:00-05:00
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You Need Better Attention Priors
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arXiv:2601.15380v1 Announce Type: new Abstract: We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresponds to a transport problem regularized by an implicit uniform prior. We introduce Generalized Optimal transport Attention with Trainable priors (GOAT), a new attention mechanism that replaces this naive assumption with a learnable, continuous prior. This prior maintains full compatibility with optimized kernels such as FlashAttention. GOAT also provides an EOT-based explanation of attention sinks and materializes a solution for them, avoiding the representational trade-offs of standard attention. Finally, by absorbing spatial information into the core attention computation, GOAT learns an extrapolatable prior that combines the flexibility of learned positional embeddings with the length generalization of fixed encodings.
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https://arxiv.org/abs/2601.15380
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Academic Papers
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36f053133062245399edfddeccbfe08475032728d7223d876d50a1c9374345af
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2026-01-23T00:00:00-05:00
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VegaChat: A Robust Framework for LLM-Based Chart Generation and Assessment
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arXiv:2601.15385v1 Announce Type: new Abstract: Natural-language-to-visualization (NL2VIS) systems based on large language models (LLMs) have substantially improved the accessibility of data visualization. However, their further adoption is hindered by two coupled challenges: (i) the absence of standardized evaluation metrics makes it difficult to assess progress in the field and compare different approaches; and (ii) natural language descriptions are inherently underspecified, so multiple visualizations may be valid for the same query. To address these issues, we introduce VegaChat, a framework for generating, validating, and assessing declarative visualizations from natural language. We propose two complementary metrics: Spec Score, a deterministic metric that measures specification-level similarity without invoking an LLM, and Vision Score, a library-agnostic, image-based metric that leverages a multimodal LLM to assess chart similarity and prompt compliance. We evaluate VegaChat on the NLV Corpus and on the annotated subset of ChartLLM. VegaChat achieves near-zero rates of invalid or empty visualizations, while Spec Score and Vision Score exhibit strong correlation with human judgments (Pearson 0.65 and 0.71, respectively), indicating that the proposed metrics support consistent, cross-library comparison. The code and evaluation artifacts are available at https://zenodo.org/records/17062309.
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https://arxiv.org/abs/2601.15385
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Academic Papers
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ed7768d7c76dabfc43414bb7c5f2ca8f8a647b71b95cd5ec2c3b055138b0c1f6
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2026-01-23T00:00:00-05:00
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FedUMM: A General Framework for Federated Learning with Unified Multimodal Models
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arXiv:2601.15390v1 Announce Type: new Abstract: Unified multimodal models (UMMs) are emerging as strong foundation models that can do both generation and understanding tasks in a single architecture. However, they are typically trained in centralized settings where all training and downstream datasets are gathered in a central server, limiting the deployment in privacy-sensitive and geographically distributed scenarios. In this paper, we present FedUMM, a general federated learning framework for UMMs under non-IID multimodal data with low communication cost. Built on NVIDIA FLARE, FedUMM instantiates federation for a BLIP3o backbone via parameter-efficient fine-tuning: clients train lightweight LoRA adapters while freezing the foundation models, and the server aggregates only adapter updates. We evaluate on VQA v2 and the GenEval compositional generation benchmarks under Dirichlet-controlled heterogeneity with up to 16 clients. Results show slight degradation as client count and heterogeneity increase, while remaining competitive with centralized training. We further analyze computation--communication trade-offs and demonstrate that adapter-only federation reduces per-round communication by over an order of magnitude compared to full fine-tuning, enabling practical federated UMM training. This work provides empirical experience for future research on privacy-preserving federated unified multimodal models.
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https://arxiv.org/abs/2601.15390
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Academic Papers
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818cdaa52aa108961de2784e12c29cdf60f1f623f4e9067730b8af6edb0cdc28
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2026-01-23T00:00:00-05:00
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GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation
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arXiv:2601.15392v1 Announce Type: new Abstract: Biomedical research increasingly relies on integrating diverse data modalities, including gene expression profiles, medical images, and clinical metadata. While medical images and clinical metadata are routinely collected in clinical practice, gene expression data presents unique challenges for widespread research use, mainly due to stringent privacy regulations and costly laboratory experiments. To address these limitations, we present GeMM-GAN, a novel Generative Adversarial Network conditioned on histopathology tissue slides and clinical metadata, designed to synthesize realistic gene expression profiles. GeMM-GAN combines a Transformer Encoder for image patches with a final Cross Attention mechanism between patches and text tokens, producing a conditioning vector to guide a generative model in generating biologically coherent gene expression profiles. We evaluate our approach on the TCGA dataset and demonstrate that our framework outperforms standard generative models and generates more realistic and functionally meaningful gene expression profiles, improving by more than 11\% the accuracy on downstream disease type prediction compared to current state-of-the-art generative models. Code will be available at: https://github.com/francescapia/GeMM-GAN
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https://arxiv.org/abs/2601.15392
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Academic Papers
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d9f47ea0c8e775272c13884f4aeb7a4363fd740d18e2b11e66b7a793af0ba919
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2026-01-23T00:00:00-05:00
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Memorization Dynamics in Knowledge Distillation for Language Models
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arXiv:2601.15394v1 Announce Type: new Abstract: Knowledge Distillation (KD) is increasingly adopted to transfer capabilities from large language models to smaller ones, offering significant improvements in efficiency and utility while often surpassing standard fine-tuning. Beyond performance, KD is also explored as a privacy-preserving mechanism to mitigate the risk of training data leakage. While training data memorization has been extensively studied in standard pre-training and fine-tuning settings, its dynamics in a knowledge distillation setup remain poorly understood. In this work, we study memorization across the KD pipeline using three large language model (LLM) families (Pythia, OLMo-2, Qwen-3) and three datasets (FineWeb, Wikitext, Nemotron-CC-v2). We find: (1) distilled models memorize significantly less training data than standard fine-tuning (reducing memorization by more than 50%); (2) some examples are inherently easier to memorize and account for a large fraction of memorization during distillation (over ~95%); (3) student memorization is predictable prior to distillation using features based on zlib entropy, KL divergence, and perplexity; and (4) while soft and hard distillation have similar overall memorization rates, hard distillation poses a greater risk: it inherits $2.7\times$ more teacher-specific examples than soft distillation. Overall, we demonstrate that distillation can provide both improved generalization and reduced memorization risks compared to standard fine-tuning.
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https://arxiv.org/abs/2601.15394
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Academic Papers
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e5914365f8673cb4094e47395a4f042788e5f63efc88c5aa606eb8dfcd441e1a
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2026-01-23T00:00:00-05:00
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Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind
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arXiv:2601.15395v1 Announce Type: new Abstract: User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state). However, existing persona datasets (like PersonaChat, PANDORA etc.) capture only trait, and ignore the impact of state. We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from 1,667 Reddit users, each measured across multiple contexts. Using the Chameleon dataset, we present three key findings. First, inspired by Latent State-Trait theory, we decompose variance and find that 74\% is within-person(state) while only 26\% is between-person (trait). Second, we find that LLMs are state-blind: they focus on trait only, and produce similar responses regardless of state. Third, we find that reward models react to user state, but inconsistently: different models favor or penalize the same users in opposite directions. We release Chameleon to support research on affective computing, personalized dialogue, and RLHF alignment.
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https://arxiv.org/abs/2601.15395
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Academic Papers
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19c04d52df877e18ba1946ce91231f1839eb928d97393f5aef93d4424ce7efa1
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2026-01-23T00:00:00-05:00
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Beyond Prompting: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration (LOGIC)
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arXiv:2601.15397v1 Announce Type: new Abstract: The rapid emergence of new entities -- driven by cultural shifts, evolving trends, and personalized user data -- poses a significant challenge for existing Speech Large Language Models (Speech LLMs). While these models excel at general conversational tasks, their static training knowledge limits their ability to recognize domain-specific terms such as contact names, playlists, or technical jargon. Existing solutions primarily rely on prompting, which suffers from poor scalability: as the entity list grows, prompting encounters context window limitations, increased inference latency, and the "lost-in-the-middle" phenomenon. An alternative approach, Generative Error Correction (GEC), attempts to rewrite transcripts via post-processing but frequently suffers from "over-correction", introducing hallucinations of entities that were never spoken. In this work, we introduce LOGIC (Logit-Space Integration for Contextual Biasing), an efficient and robust framework that operates directly in the decoding layer. Unlike prompting, LOGIC decouples context injection from input processing, ensuring constant-time complexity relative to prompt length. Extensive experiments using the Phi-4-MM model across 11 multilingual locales demonstrate that LOGIC achieves an average 9% relative reduction in Entity WER with a negligible 0.30% increase in False Alarm Rate.
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https://arxiv.org/abs/2601.15397
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Academic Papers
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3f4bdbcd5c45bcadd9a723f4950d4387012977fb8506706f31c6c1db00a879e8
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2026-01-23T00:00:00-05:00
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Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC
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arXiv:2601.15399v1 Announce Type: new Abstract: High-Performance Computing (HPC) schedulers must balance user performance with facility-wide resource constraints. The task boils down to selecting the optimal number of nodes for a given job. We present a surrogate-assisted multi-objective Bayesian optimization (MOBO) framework to automate this complex decision. Our core hypothesis is that surrogate models informed by attention-based embeddings of job telemetry can capture performance dynamics more effectively than standard regression techniques. We pair this with an intelligent sample acquisition strategy to ensure the approach is data-efficient. On two production HPC datasets, our embedding-informed method consistently identified higher-quality Pareto fronts of runtime-power trade-offs compared to baselines. Furthermore, our intelligent data sampling strategy drastically reduced training costs while improving the stability of the results. To our knowledge, this is the first work to successfully apply embedding-informed surrogates in a MOBO framework to the HPC scheduling problem, jointly optimizing for performance and power on production workloads.
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https://arxiv.org/abs/2601.15399
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Academic Papers
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15ba943dc10ca801a9b35e211846205b1a632cca6558a34e4cea95b0877ec90a
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2026-01-23T00:00:00-05:00
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Multi-Input Ciphertext Multiplication for Homomorphic Encryption
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arXiv:2601.15401v1 Announce Type: new Abstract: Homomorphic encryption (HE) enables arithmetic operations to be performed directly on encrypted data. It is essential for privacy-preserving applications such as machine learning, medical diagnosis, and financial data analysis. In popular HE schemes, ciphertext multiplication is only defined for two inputs. However, the multiplication of multiple inputs is needed in many HE applications. In our previous work, a three-input ciphertext multiplication method for the CKKS HE scheme was developed. This paper first reformulates the three-input ciphertext multiplication to enable the combination of computations in order to further reduce the complexity. The second contribution is extending the multiplication to multiple inputs without compromising the noise overhead. Additional evaluation keys are introduced to achieve relinearization of polynomial multiplication results. To minimize the complexity of the large number of rescaling units in the multiplier, a theoretical analysis is developed to relocate the rescaling, and a multi-level rescaling approach is proposed to implement combined rescaling with complexity similar to that of a single rescaling unit. Guidelines and examples are provided on the input partition to enable the combination of more rescaling. Additionally, efficient hardware architectures are designed to implement our proposed multipliers. The improved three-input ciphertext multiplier reduces the logic area and latency by 15% and 50%, respectively, compared to the best prior design. For multipliers with more inputs, ranging from 4 to 12, the architectural analysis reveals 32% savings in area and 45% shorter latency, on average, compared to prior work.
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https://arxiv.org/abs/2601.15401
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e1825e5808dc06227aa02b34c6d0b4b1a2c1259abd135aee3c1bd1ce2cf5b886
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2026-01-23T00:00:00-05:00
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Partially Polarized Polar Codes: A New Design for 6G Control Channels
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arXiv:2601.15404v1 Announce Type: new Abstract: We introduce a new family of polar-like codes, called Partially Polarized Polar (PPP) codes. PPP codes are constructed from conventional polar codes by selectively pruning polarization kernels, thereby modifying the synthesized bit-channel capacities to ensure a guaranteed number of non-frozen bits available early in decoding. These early-access information bits enable more effective early termination, which is particularly valuable for blind decoding in downlink control channels, where user equipment (UE) must process multiple candidates, many of which carry no valid control information. Our results show that PPP codes offer substantial performance gains over conventional polar codes, particularly at larger block lengths where hardware limitations restrict straightforward scaling. Compared with existing methods such as aggregation or segmentation, PPP codes achieve higher efficiency without the need for additional hardware support. Finally, we propose several frozen-bitmap design strategies tailored to PPP codes.
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https://arxiv.org/abs/2601.15404
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Academic Papers
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7cb67fab21be83f6261f9699bc36458d00ff3728301583218b20bbc928660427
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2026-01-23T00:00:00-05:00
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Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition
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arXiv:2601.15406v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance on a wide range of vision-language tasks, raising interest in their potential use for biometric applications. In this paper, we conduct a systematic evaluation of state-of-the-art MLLMs for heterogeneous face recognition (HFR), where enrollment and probe images are from different sensing modalities, including visual (VIS), near infrared (NIR), short-wave infrared (SWIR), and thermal camera. We benchmark multiple open-source MLLMs across several cross-modality scenarios, including VIS-NIR, VIS-SWIR, and VIS-THERMAL face recognition. The recognition performance of MLLMs is evaluated using biometric protocols and based on different metrics, including Acquire Rate, Equal Error Rate (EER), and True Accept Rate (TAR). Our results reveal substantial performance gaps between MLLMs and classical face recognition systems, particularly under challenging cross-spectral conditions, in spite of recent advances in MLLMs. Our findings highlight the limitations of current MLLMs for HFR and also the importance of rigorous biometric evaluation when considering their deployment in face recognition systems.
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https://arxiv.org/abs/2601.15406
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Academic Papers
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9b95362c8676856d1749b012a84cfa58e6b7323c687721f007e3bb09337d0c24
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2026-01-23T00:00:00-05:00
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CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
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arXiv:2601.15408v1 Announce Type: new Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present CURE, an error-aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine-tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy-grounded report generation using public datasets. The method dynamically adjusts sampling based on model performance, emphasizing harder samples to improve spatial and textual alignment. CURE improves grounding accuracy by +0.37 IoU, boosts report quality by +0.188 CXRFEScore, and reduces hallucinations by 18.6%. CURE is a data-efficient framework that enhances both grounding accuracy and report reliability. Code is available at https://github.com/PabloMessina/CURE and model weights at https://huggingface.co/pamessina/medgemma-4b-it-cure
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https://arxiv.org/abs/2601.15408
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Academic Papers
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6f825687f1485407222f8aeb4f802d85b30d1136512c8f9cad24d8542f675077
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2026-01-23T00:00:00-05:00
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A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots
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arXiv:2601.15412v1 Announce Type: new Abstract: Mental health concerns are rising globally, prompting increased reliance on technology to address the demand-supply gap in mental health services. In particular, mental health chatbots are emerging as a promising solution, but these remain largely untested, raising concerns about safety and potential harms. In this paper, we dive into the literature to identify critical gaps in the design and implementation of mental health chatbots. We contribute an operational checklist to help guide the development and design of more trustworthy, safe, and user-friendly chatbots. The checklist serves as both a developmental framework and an auditing tool to ensure ethical and effective chatbot design. We discuss how this checklist is a step towards supporting more responsible design practices and supporting new standards for sociotechnically sound digital mental health tools.
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https://arxiv.org/abs/2601.15412
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Academic Papers
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c72f6a1e612f786e3b3017b734d02ec53bb5017b010b5cc6f6944c0b9c6468ff
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2026-01-23T00:00:00-05:00
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DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction
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arXiv:2601.15416v1 Announce Type: new Abstract: Sparse-view Cone-Beam Computed Tomography reconstruction from limited X-ray projections remains a challenging problem in medical imaging due to the inherent undersampling of fine-grained anatomical details, which correspond to high-frequency components. Conventional CNN-based methods often struggle to recover these fine structures, as they are typically biased toward learning low-frequency information. To address this challenge, this paper presents DuFal (Dual-Frequency-Aware Learning), a novel framework that integrates frequency-domain and spatial-domain processing via a dual-path architecture. The core innovation lies in our High-Local Factorized Fourier Neural Operator, which comprises two complementary branches: a Global High-Frequency Enhanced Fourier Neural Operator that captures global frequency patterns and a Local High-Frequency Enhanced Fourier Neural Operator that processes spatially partitioned patches to preserve spatial locality that might be lost in global frequency analysis. To improve efficiency, we design a Spectral-Channel Factorization scheme that reduces the Fourier Neural Operator parameter count. We also design a Cross-Attention Frequency Fusion module to integrate spatial and frequency features effectively. The fused features are then decoded through a Feature Decoder to produce projection representations, which are subsequently processed through an Intensity Field Decoding pipeline to reconstruct a final Computed Tomography volume. Experimental results on the LUNA16 and ToothFairy datasets demonstrate that DuFal significantly outperforms existing state-of-the-art methods in preserving high-frequency anatomical features, particularly under extremely sparse-view settings.
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https://arxiv.org/abs/2601.15416
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Academic Papers
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5dae8a073febd18327bb516fa4152bdf82e36d861e027d38aa1dfcc9ceb6ceb1
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2026-01-23T00:00:00-05:00
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Ambient Dataloops: Generative Models for Dataset Refinement
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arXiv:2601.15417v1 Announce Type: new Abstract: We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets contain samples of highly varying quality, and training directly on such heterogeneous data often yields suboptimal models. We propose a dataset-model co-evolution process; at each iteration of our method, the dataset becomes progressively higher quality, and the model improves accordingly. To avoid destructive self-consuming loops, at each generation, we treat the synthetically improved samples as noisy, but at a slightly lower noisy level than the previous iteration, and we use Ambient Diffusion techniques for learning under corruption. Empirically, Ambient Dataloops achieve state-of-the-art performance in unconditional and text-conditional image generation and de novo protein design. We further provide a theoretical justification for the proposed framework that captures the benefits of the data looping procedure.
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https://arxiv.org/abs/2601.15417
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Academic Papers
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5e4cdd366319146602837a0c95d0b5134fa6809f2638b5af04c3dfbf6d3ed2e3
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2026-01-23T00:00:00-05:00
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Learning a Unified Latent Space for Cross-Embodiment Robot Control
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arXiv:2601.15419v1 Announce Type: new Abstract: We present a scalable framework for cross-embodiment humanoid robot control by learning a shared latent representation that unifies motion across humans and diverse humanoid platforms, including single-arm, dual-arm, and legged humanoid robots. Our method proceeds in two stages: first, we construct a decoupled latent space that captures localized motion patterns across different body parts using contrastive learning, enabling accurate and flexible motion retargeting even across robots with diverse morphologies. To enhance alignment between embodiments, we introduce tailored similarity metrics that combine joint rotation and end-effector positioning for critical segments, such as arms. Then, we train a goal-conditioned control policy directly within this latent space using only human data. Leveraging a conditional variational autoencoder, our policy learns to predict latent space displacements guided by intended goal directions. We show that the trained policy can be directly deployed on multiple robots without any adaptation. Furthermore, our method supports the efficient addition of new robots to the latent space by learning only a lightweight, robot-specific embedding layer. The learned latent policies can also be directly applied to the new robots. Experimental results demonstrate that our approach enables robust, scalable, and embodiment-agnostic robot control across a wide range of humanoid platforms.
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https://arxiv.org/abs/2601.15419
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Academic Papers
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ecf689bf81c05d6e7258f72cc26cbc0dd022d39d42ad9624fc0cbc3e30ba0c7d
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2026-01-23T00:00:00-05:00
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Problems with fixpoints of polynomials of polynomials
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arXiv:2601.15420v1 Announce Type: new Abstract: Motivated by applications in computable analysis, we study fixpoints of certain endofunctors over categories of containers. More specifically, we focus on fibred endofunctors over the fibrewise opposite of the codomain fibration that can be themselves be represented by families of polynomial endofunctors. In this setting, we show how to compute initial algebras, terminal coalgebras and another kind of fixpoint $\zeta$. We then explore a number of examples of derived operators inspired by Weihrauch complexity and the usual construction of the free polynomial monad. We introduce $\zeta$-expressions as the syntax of $\mu$-bicomplete categories, extended with $\zeta$-binders and parallel products, which thus have a natural denotation in containers. By interpreting certain $\zeta$-expressions in a category of type 2 computable maps, we are able to capture a number of meaningful Weihrauch degrees, ranging from closed choice on $\{0, 1\}$ to determinacy of infinite parity games, via an "answerable part" operator.
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https://arxiv.org/abs/2601.15420
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Academic Papers
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38086516243bd8fde1f4351c3c903e6ffcfd4668d221cee68b32b6a270b0d610
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2026-01-23T00:00:00-05:00
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Lattice: A Confidence-Gated Hybrid System for Uncertainty-Aware Sequential Prediction with Behavioral Archetypes
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arXiv:2601.15423v1 Announce Type: new Abstract: We introduce Lattice, a hybrid sequential prediction system that conditionally activates learned behavioral structure using binary confidence gating. The system clusters behavior windows into behavioral archetypes and uses binary confidence gating to activate archetype-based scoring only when confidence exceeds a threshold, falling back to baseline predictions when uncertain. We validate Lattice on recommendation systems (MovieLens), scientific time-series (LIGO), and financial markets, using LSTM and transformer backbones. On MovieLens with LSTM, Lattice achieves +31.9% improvement over LSTM baseline in HR@10 (p < 3.29 x 10^-25, 30 seeds), outperforming transformer baselines by 109.4% over SASRec and 218.6% over BERT4Rec. On LIGO and financial data, the system correctly refuses archetype activation when distribution shift occurs - a successful outcome demonstrating confidence gating prevents false activation. On transformer backbones, Lattice provides 0.0% improvement (neutral, no degradation), gracefully deferring when structure is already present. This bidirectional validation - activating when patterns apply, refusing when they don't, and deferring when redundant - supports confidence gating as a promising architectural principle for managing epistemic uncertainty in safety-critical applications.
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https://arxiv.org/abs/2601.15423
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Academic Papers
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c917a735e56f0bd545b7f552f7c78cc61f18afeccd6326039facbf02569d1476
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2026-01-23T00:00:00-05:00
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Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs
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arXiv:2601.15429v1 Announce Type: new Abstract: Large Language Models (LLMs) generate fluent answers but can struggle with trustworthy, domain-specific reasoning. We evaluate whether domain knowledge graphs (KGs) improve Retrieval-Augmented Generation (RAG) for healthcare by constructing three PubMed-derived graphs: $\mathbb{G}_1$ (T2DM), $\mathbb{G}_2$ (Alzheimer's disease), and $\mathbb{G}_3$ (AD+T2DM). We design two probes: Probe 1 targets merged AD T2DM knowledge, while Probe 2 targets the intersection of $\mathbb{G}_1$ and $\mathbb{G}_2$. Seven instruction-tuned LLMs are tested across retrieval sources {No-RAG, $\mathbb{G}_1$, $\mathbb{G}_2$, $\mathbb{G}_1$ + $\mathbb{G}_2$, $\mathbb{G}_3$, $\mathbb{G}_1$+$\mathbb{G}_2$ + $\mathbb{G}_3$} and three decoding temperatures. Results show that scope alignment between probe and KG is decisive: precise, scope-matched retrieval (notably $\mathbb{G}_2$) yields the most consistent gains, whereas indiscriminate graph unions often introduce distractors that reduce accuracy. Larger models frequently match or exceed KG-RAG with a No-RAG baseline on Probe 1, indicating strong parametric priors, whereas smaller/mid-sized models benefit more from well-scoped retrieval. Temperature plays a secondary role; higher values rarely help. We conclude that precision-first, scope-matched KG-RAG is preferable to breadth-first unions, and we outline practical guidelines for graph selection, model sizing, and retrieval/reranking. Code and Data available here - https://github.com/sydneyanuyah/RAGComparison
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https://arxiv.org/abs/2601.15429
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Academic Papers
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7f6f62b0b3aed8cf691ffda8d3d509f274efdf2f31ce1fcbba8c1f0060f7cb60
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2026-01-23T00:00:00-05:00
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SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication
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arXiv:2601.15431v1 Announce Type: new Abstract: Radiance field-based rendering methods have attracted significant interest from the computer vision and computer graphics communities. They enable high-fidelity rendering with complex real-world lighting effects, but at the cost of high rendering time. 3D Gaussian Splatting solves this issue with a rasterisation-based approach for real-time rendering, enabling applications such as autonomous driving, robotics, virtual reality, and extended reality. However, current 3DGS implementations are difficult to integrate into traditional mesh-based rendering pipelines, which is a common use case for interactive applications and artistic exploration. To address this limitation, this software solution uses Nvidia's interprocess communication (IPC) APIs to easily integrate into implementations and allow the results to be viewed in external clients such as Unity, Blender, Unreal Engine, and OpenGL viewers. The code is available at https://github.com/RockyXu66/splatbus.
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https://arxiv.org/abs/2601.15431
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Academic Papers
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1faf21bc8c1b96824f71e46d214779ae22cd3c1187a6d02fa34cb0d16d096aa8
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2026-01-23T00:00:00-05:00
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MEDFORD in a Box: Improvements and Future Directions for a Metadata Description Language
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arXiv:2601.15432v1 Announce Type: new Abstract: Scientific research metadata is vital to ensure the validity, reusability, and cost-effectiveness of research efforts. The MEDFORD metadata language was previously introduced to simplify the process of writing and maintaining metadata for non-programmers. However, barriers to entry and usability remain, including limited automatic validation, difficulty of data transport, and user unfamiliarity with text file editing. To address these issues, we introduce MEDFORD-in-a-Box (MIAB), a documentation ecosystem to facilitate researcher adoption and earlier metadata capture. MIAB contains many improvements, including an updated MEDFORD parser with expanded validation routines and BagIt export capability. MIAB also includes an improved VS Code extension that supports these changes through a visual IDE. By simplifying metadata generation, this new tool supports the creation of correct, consistent, and reusable metadata, ultimately improving research reproducibility.
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https://arxiv.org/abs/2601.15432
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db0b343af317ed3563d8e8f363ba0a6882cd73e45e7310c03892bbd1a91f967d
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2026-01-23T00:00:00-05:00
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ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering
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arXiv:2601.15434v1 Announce Type: new Abstract: The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data, such as text, images, formulas, and tables. Conventional Retrieval Augmented Generation (RAG) methods often fall short in handling this complexity, resulting in subpar performance. We introduce ManuRAG, an innovative multi-modal RAG framework designed for manufacturing QA, incorporating specialized techniques to improve answer accuracy, reliability, and interpretability. To benchmark performance, we evaluate ManuRAG on three datasets comprising a total of 1,515 QA pairs, corresponding to mathematical, multiple-choice, and review-based questions in manufacturing principles and practices. Experimental results show that ManuRAG consistently outperforms existing methods across all evaluated datasets. Furthermore, ManuRAG's adaptable design makes it applicable to other domains, including law, healthcare, and finance, positioning it as a versatile tool for domain-specific QA.
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https://arxiv.org/abs/2601.15434
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9f301da13363782094ccc2c57db979d7762a4fcc9b889f2b33ed6972cbc8e55a
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2026-01-23T00:00:00-05:00
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Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models
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arXiv:2601.15436v1 Announce Type: new Abstract: We propose a novel way to evaluate sycophancy of LLMs in a direct and neutral way, mitigating various forms of uncontrolled bias, noise, or manipulative language, deliberately injected to prompts in prior works. A key novelty in our approach is the use of LLM-as-a-judge, evaluation of sycophancy as a zero-sum game in a bet setting. Under this framework, sycophancy serves one individual (the user) while explicitly incurring cost on another. Comparing four leading models - Gemini 2.5 Pro, ChatGpt 4o, Mistral-Large-Instruct-2411, and Claude Sonnet 3.7 - we find that while all models exhibit sycophantic tendencies in the common setting, in which sycophancy is self-serving to the user and incurs no cost on others, Claude and Mistral exhibit "moral remorse" and over-compensate for their sycophancy in case it explicitly harms a third party. Additionally, we observed that all models are biased toward the answer proposed last. Crucially, we find that these two phenomena are not independent; sycophancy and recency bias interact to produce `constructive interference' effect, where the tendency to agree with the user is exacerbated when the user's opinion is presented last.
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https://arxiv.org/abs/2601.15436
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9b35dbbf375fe825668a64fbbc432732af3194c10848ebd63ed02b30444f2e10
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2026-01-23T00:00:00-05:00
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Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations
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arXiv:2601.15437v1 Announce Type: new Abstract: Large Language Models (LLMs) like ChatGPT offer potential support for autistic people, but this potential requires understanding the implicit perspectives these models might carry, including their biases and assumptions about autism. Moving beyond single-agent prompting, we utilized LLM-based multi-agent systems to investigate complex social scenarios involving autistic and non-autistic agents. In our study, agents engaged in group-task conversations and answered structured interview questions, which we analyzed to examine ChatGPT's biases and how it conceptualizes autism. We found that ChatGPT assumes autistic people are socially dependent, which may affect how it interacts with autistic users or conveys information about autism. To address these challenges, we propose adopting the double empathy problem, which reframes communication breakdowns as a mutual challenge. We describe how future LLMs could address the biases we observed and improve interactions involving autistic people by incorporating the double empathy problem into their design.
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https://arxiv.org/abs/2601.15437
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Academic Papers
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cbf6ca68af7919eae720938b46c5200cff32a24be70670efa4beb288b69a171c
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2026-01-23T00:00:00-05:00
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CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
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arXiv:2601.15441v1 Announce Type: new Abstract: Internal activations of diffusion models encode rich semantic information, but interpreting such representations remains challenging. While Sparse Autoencoders (SAEs) have shown promise in disentangling latent representations, existing SAE-based methods for diffusion model understanding rely on unsupervised approaches that fail to align sparse features with human-understandable concepts. This limits their ability to provide reliable semantic control over generated images. We introduce CASL (Concept-Aligned Sparse Latents), a supervised framework that aligns sparse latent dimensions of diffusion models with semantic concepts. CASL first trains an SAE on frozen U-Net activations to obtain disentangled latent representations, and then learns a lightweight linear mapping that associates each concept with a small set of relevant latent dimensions. To validate the semantic meaning of these aligned directions, we propose CASL-Steer, a controlled latent intervention that shifts activations along the learned concept axis. Unlike editing methods, CASL-Steer is used solely as a causal probe to reveal how concept-aligned latents influence generated content. We further introduce the Editing Precision Ratio (EPR), a metric that jointly measures concept specificity and the preservation of unrelated attributes. Experiments show that our method achieves superior editing precision and interpretability compared to existing approaches. To the best of our knowledge, this is the first work to achieve supervised alignment between latent representations and semantic concepts in diffusion models.
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https://arxiv.org/abs/2601.15441
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be5eac4beb12ce8ed9e5933f52b11dd171a2d320c04731ece8b7d9f77fbf9cea
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2026-01-23T00:00:00-05:00
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A tensor network formalism for neuro-symbolic AI
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arXiv:2601.15442v1 Announce Type: new Abstract: The unification of neural and symbolic approaches to artificial intelligence remains a central open challenge. In this work, we introduce a tensor network formalism, which captures sparsity principles originating in the different approaches in tensor decompositions. In particular, we describe a basis encoding scheme for functions and model neural decompositions as tensor decompositions. The proposed formalism can be applied to represent logical formulas and probability distributions as structured tensor decompositions. This unified treatment identifies tensor network contractions as a fundamental inference class and formulates efficiently scaling reasoning algorithms, originating from probability theory and propositional logic, as contraction message passing schemes. The framework enables the definition and training of hybrid logical and probabilistic models, which we call Hybrid Logic Network. The theoretical concepts are accompanied by the python library tnreason, which enables the implementation and practical use of the proposed architectures.
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https://arxiv.org/abs/2601.15442
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caaa449a31e703fb4018f8efad15f1f61526b0c852c3c71f5970f0a351202a0a
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2026-01-23T00:00:00-05:00
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Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation
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arXiv:2601.15445v1 Announce Type: new Abstract: Reflexive Thematic Analysis (RTA) is a critical method for generating deep interpretive insights. Yet its core tenets, including researcher reflexivity, tangible analytical evolution, and productive disagreement, are often poorly supported by software tools that prioritize speed and consensus over interpretive depth. To address this gap, we introduce Reflexis, a collaborative workspace that centers these practices. It supports reflexivity by integrating in-situ reflection prompts, makes code evolution transparent and tangible, and scaffolds collaborative interpretation by turning differences into productive, positionality-aware dialogue. Results from our paired-analyst study (N=12) indicate that Reflexis encouraged participants toward more granular reflection and reframed disagreements as productive conversations. The evaluation also surfaced key design tensions, including a desire for higher-level, networked memos and more user control over the timing of proactive alerts. Reflexis contributes a design framework for tools that prioritize rigor and transparency to support deep, collaborative interpretation in an age of automation.
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https://arxiv.org/abs/2601.15445
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a062820315546e6aced4eb32d5cc639f57375bfd772f90f27a32416ac0162ff9
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2026-01-23T00:00:00-05:00
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DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection
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arXiv:2601.15453v1 Announce Type: new Abstract: Few-normal shot anomaly detection (FNSAD) aims to detect abnormal regions in images using only a few normal training samples, making the task highly challenging due to limited supervision and the diversity of potential defects. Recent approaches leverage vision-language models such as CLIP with prompt-based learning to align image and text features. However, existing methods often exhibit weak discriminability between normal and abnormal prompts and lack principled scoring mechanisms for patch-level anomalies. We propose a deviation-guided prompt learning framework that integrates the semantic power of vision-language models with the statistical reliability of deviation-based scoring. Specifically, we replace fixed prompt prefixes with learnable context vectors shared across normal and abnormal prompts, while anomaly-specific suffix tokens enable class-aware alignment. To enhance separability, we introduce a deviation loss with Top-K Multiple Instance Learning (MIL), modeling patch-level features as Gaussian deviations from the normal distribution. This allows the network to assign higher anomaly scores to patches with statistically significant deviations, improving localization and interpretability. Experiments on the MVTecAD and VISA benchmarks demonstrate superior pixel-level detection performance compared to PromptAD and other baselines. Ablation studies further validate the effectiveness of learnable prompts, deviation-based scoring, and the Top-K MIL strategy.
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https://arxiv.org/abs/2601.15453
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aa94df05e7c3468fc4f796ecfd05ca35d70952581000280dcfa61cfae4b12cd6
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2026-01-23T00:00:00-05:00
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Remarks on Algebraic Reconstruction of Types and Effects
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arXiv:2601.15455v1 Announce Type: new Abstract: In their 1991 paper "Algebraic Reconstruction of Types and Effects," Pierre Jouvelot and David Gifford presented a type-and-effect reconstruction algorithm based on an algebraic structure of effects. Their work is considered a milestone in the development of type-and-effect systems, and has inspired numerous subsequent works in the area of static analysis. However, unlike the later research it spawned, the original algorithm considered a language with higher-rank polymorphism, a feature which is challenging to implement correctly. In this note, we identify subtle bugs related to variable binding in their approach to this feature. We revisit their type system and reconstruction algorithm, and describe the discovered issues.
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https://arxiv.org/abs/2601.15455
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4a4ebbca07b69f832780657c1106cf28e765249de6f60955a4e04eab04901b89
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2026-01-23T00:00:00-05:00
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Chunking, Retrieval, and Re-ranking: An Empirical Evaluation of RAG Architectures for Policy Document Question Answering
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arXiv:2601.15457v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into the public health policy sector offers a transformative approach to navigating the vast repositories of regulatory guidance maintained by agencies such as the Centers for Disease Control and Prevention (CDC). However, the propensity for LLMs to generate hallucinations, defined as plausible but factually incorrect assertions, presents a critical barrier to the adoption of these technologies in high-stakes environments where information integrity is non-negotiable. This empirical evaluation explores the effectiveness of Retrieval-Augmented Generation (RAG) architectures in mitigating these risks by grounding generative outputs in authoritative document context. Specifically, this study compares a baseline Vanilla LLM against Basic RAG and Advanced RAG pipelines utilizing cross-encoder re-ranking. The experimental framework employs a Mistral-7B-Instruct-v0.2 model and an all-MiniLM-L6-v2 embedding model to process a corpus of official CDC policy analytical frameworks and guidance documents. The analysis measures the impact of two distinct chunking strategies, recursive character-based and token-based semantic splitting, on system accuracy, measured through faithfulness and relevance scores across a curated set of complex policy scenarios. Quantitative findings indicate that while Basic RAG architectures provide a substantial improvement in faithfulness (0.621) over Vanilla baselines (0.347), the Advanced RAG configuration achieves a superior faithfulness average of 0.797. These results demonstrate that two-stage retrieval mechanisms are essential for achieving the precision required for domain-specific policy question answering, though structural constraints in document segmentation remain a significant bottleneck for multi-step reasoning tasks.
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https://arxiv.org/abs/2601.15457
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898e51ac97c37ae03321884031bb8082d5cc38ad4c5c639110fc5b60644da4ce
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2026-01-23T00:00:00-05:00
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MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering
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arXiv:2601.15458v1 Announce Type: new Abstract: Motivation: The multiple sequence alignment (MSA) problem has been extensively studied, with numerous approaches developed over recent years. With the rapid growth of sequence data, there is an increasing need for fast and accurate MSA tools that scale effectively to large datasets. Building on our previous work on CLAM, we are able to use exact dynamic programming (Needleman-Wunsch) while scaling to large datasets. We introduce MuSAlS (Multiple Sequence Alignment at Scale), a fast and scalable de novo MSA aligner. MuSAlS uses hierarchical clustering to construct a guide tree based on the Levenshtein distance metric, enabling efficient and accurate alignment through a bottom-up approach. Results: MuSAlS achieves competitive accuracy compared to state-of-the-art methods while significantly improving runtime performance. This makes it a valuable tool for researchers analyzing large-scale genomic and metagenomic datasets, addressing the growing demand for scalable bioinformatics solutions. Availability and Implementation: MuSAlS is implemented in the Rust programming language, and available at https://github.com/URI-ABD/clam
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https://arxiv.org/abs/2601.15458
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931dcbf6eb64ec13a85ec681f05ad5b434a8568c7806f605b99040b76627ab06
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2026-01-23T00:00:00-05:00
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Neural Collision Detection for Multi-arm Laparoscopy Surgical Robots Through Learning-from-Simulation
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arXiv:2601.15459v1 Announce Type: new Abstract: This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing key challenges in collision detection and minimum distance estimation. By combining analytical modeling, real-time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering precise theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7-DOF Kinova robotic arms, generating a diverse dataset of configurations for collision detection and distance estimation. Using these insights, a deep neural network model was trained with joint actuators of robot arms and relative positions as inputs, achieving a mean absolute error of 282.2 mm and an R-squared value of 0.85. The close alignment between predicted and actual distances highlights the network's accuracy and its ability to generalize spatial relationships. This work demonstrates the effectiveness of combining analytical precision with machine learning algorithms to enhance the precision and reliability of robotic systems.
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https://arxiv.org/abs/2601.15459
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1007343bd830ebd263f7d1f5df3adaadf17777fef1977986012d7c28fa48d05d
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2026-01-23T00:00:00-05:00
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Rank-metric codes over arbitrary fields: Bounds and constructions
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arXiv:2601.15464v1 Announce Type: new Abstract: Rank-metric codes, defined as sets of matrices over a finite field with the rank distance, have gained significant attention due to their applications in network coding and connections to diverse mathematical areas. Initially studied by Delsarte in 1978 and later rediscovered by Gabidulin, these codes have become a central topic in coding theory. This paper surveys the development and mathematical foundations, in particular, regarding bounds and constructions of rank-metric codes, emphasizing their extension beyond finite fields to more general settings. We examine Singleton-like bounds on code parameters, demonstrating their sharpness in finite field cases and contrasting this with contexts where the bounds are not tight. Furthermore, we discuss constructions of Maximum Rank Distance (MRD) codes over fields with cyclic Galois extensions and the relationship between linear rank-metric codes with systems and evasive subspaces. The paper also reviews results for algebraically closed fields and real numbers, previously appearing in the context of topology and measure theory. We conclude by proposing future research directions, including conjectures on MRD code existence and the exploration of rank-metric codes over various field extensions.
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https://arxiv.org/abs/2601.15464
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46c95f7ef533fa6017e24aa30a7d9fe0db70aaf6920ef5cc716cb5d0017b516d
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2026-01-23T00:00:00-05:00
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Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone Chatbots
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arXiv:2601.15465v1 Announce Type: new Abstract: As digital tools increasingly mediate mental health care, self-clone chatbots can offer a uniquely novel approach to intra-personal exploration and self-derived support. Trained to replicate users' conversational patterns, self-clones allow users to talk to themselves through their digital replicas. Despite the promises, these systems may carry risks around identity confusion, negative reinforcement, and blurred user agency. Through interviews with 16 mental health professionals and 6 general users, we aim to uncover tensions and design opportunities in this emerging space to guide responsible self-clone design. Our analysis produces a design framework organized around three priorities: (1) defining goals and grounding the approach in existing therapeutic models, (2) design dimensions including the self-clone persona and user-clone relationship dynamics, and (3) considerations for minimizing potential emotional and ethical harms. This framework contributes an interdisciplinary foundation for designing self-clone chatbots as AI-mediated self-interaction tools that are emotionally and ethically attuned in mental health contexts.
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https://arxiv.org/abs/2601.15465
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cb125a67c21efccbbdd773e0437eb7f4555cb1a1c068ef17e1ba565544c9cc7a
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2026-01-23T00:00:00-05:00
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Shape of You: Implications of Social Context and Avatar Body Shape on Relatedness, Emotions, and Performance in a Virtual Reality Workout
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arXiv:2601.15466v1 Announce Type: new Abstract: It is obvious that emotions are causal variables of motivation, as they elicit states, forces and energies that trigger and guide labor behavior. Thus, a motivational tension that is not informed by needs alone, but also by emotions, intention, goals and means to achieve them is therefore generated within the mental, emotional and physical plane. Based on Montserrat's opinion (2004: 131), that "to motivate means, above all, to move and to transmit an emotion", we will undertake to identify the mutual influences between emotions and motivation. The main objectives of this article are to display a summary of the theories and definitions about emotions and to explore the links between emotions and motivation. Although interconnected, emotions and motivation can be contemplated from a double perspective: (1) emotions influence motivation and (2) motivation influences emotions. Moreover, we will consider motivation from three dimensions: (1) cognitive, (2) affective and (3) volitional. The ultimate purpose of this article is to issue a warning as to the importance of the emotional side of motivation. An important part in implementing such insight is to be played by managers (and by employees, also), who should develop the skills and know-how needed to keep a well-balanced emotional climate that effectively favors the maximization of individual and group motivation at the workplace.
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https://arxiv.org/abs/2601.15466
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a2c5d0bc7f87c7a036887f763790e71088aa464cd64d9bf4729f3fa5ff251a67
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2026-01-23T00:00:00-05:00
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Learning from Synthetic Data: Limitations of ERM
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arXiv:2601.15468v1 Announce Type: new Abstract: The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, ``natural'' content has been contaminated by data points that appear similar to natural data, but are in fact LLM-generated. In this work we revisit fundamental learning theory questions in this, now ubiquitous, setting. We model this scenario as a sequence of learning tasks where the input is a mix of natural and synthetic data, and the learning algorithms are oblivious to the origin of any individual example. We study the possibilities and limitations of ERM in this setting. For the problem of estimating the mean of an arbitrary $d$-dimensional distribution, we find that while ERM converges to the true mean, it is outperformed by an algorithm that assigns non-uniform weights to examples from different generations of data. For the PAC learning setting, the disparity is even more stark. We find that ERM does not always converge to the true concept, echoing the model collapse literature. However, we show there are algorithms capable of learning the correct hypothesis for arbitrary VC classes and arbitrary amounts of contamination.
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https://arxiv.org/abs/2601.15468
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91d80ef09f3f3573c062fe20a8f59418458884ba6feb74e351843a586d452121
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2026-01-23T00:00:00-05:00
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Nested and outlier embeddings into trees
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arXiv:2601.15470v1 Announce Type: new Abstract: In this paper, we consider outlier embeddings into HSTs and ultrametrics. In particular, for $(X,d)$, let $k$ be the size of the smallest subset of $X$ such that all but that subset (i.e. the ``outlier set'') can be probabilistically embedded into the space of HSTs with expected distortion at most $c$. Our primary result is showing that there exists an efficient algorithm that takes in $(X,d)$ and a target distortion $c$ and samples from a probabilistic embedding with at most $O(\frac k \epsilon \log^2k)$ outliers and distortion at most $(32+\epsilon)c$, for any $\epsilon>0$. This leads to better instance-specific approximations for certain instances of the buy-at-bulk and dial-a-ride problems, whose current best approximation algorithms go through HST embeddings. In order to facilitate our results, we largely focus on the concept of compositions of nested embeddings introduced by [Chawla and Sheridan 2024]. A nested embedding is a composition of two embeddings of a metric space $(X,d)$ -- a low distortion embedding of a subset $S$ of nodes, and a higher distortion embedding of the entire metric. The composition is a single embedding that preserves the low distortion over $S$ and does not increase distortion over the remaining points by much. In this paper, we expand this concept from the setting of deterministic embeddings to the setting of probabilistic embeddings. We show how to find good nested compositions of embeddings into HSTs, and combine this with an approximation algorithm of [Munagala et al. 2023] to obtain our results.
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https://arxiv.org/abs/2601.15470
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05c15de2db75c8a96dd14b02f1361de806ad700acdcc344be128932f78d351d6
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2026-01-23T00:00:00-05:00
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Put Your Muscle Into It: Introducing XEM2, a Novel Approach for Monitoring Exertion in Stationary Physical Exercises Leveraging Muscle Work
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arXiv:2601.15472v1 Announce Type: new Abstract: We present a novel system for camera-based measurement and visualization of muscle work based on the Hill-Type-Muscle-Model: the exercise exertion muscle-work monitor (\textit{XEM}$^{2}$). Our aim is to complement and, thus, address issues of established measurement techniques that offer imprecise data for non-uniform movements (burned calories) or provide limited information on strain across different body parts (self-perception scales). We validate the reliability of XEM's measurements through a technical evaluation of ten participants and five exercises. Further, we assess the acceptance, usefulness, benefits, and opportunities of \textit{XEM}$^{2}$ in an empirical user study. Our results show that \textit{XEM}$^{2}$ provides reliable values of muscle work and supports participants in understanding their workout while also providing reliable information about perceived exertion per muscle group. With this paper, we introduce a novel system capable of measuring and visualizing exertion for single muscle groups, which has the potential to improve exercise monitoring to prevent unbalanced workouts.
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https://arxiv.org/abs/2601.15472
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3369bfad5439c1ca57ee0c29966761777497fb5b1b204eae4397abd3edc7fc78
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2026-01-23T00:00:00-05:00
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Panther: Faster and Cheaper Computations with Randomized Numerical Linear Algebra
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arXiv:2601.15473v1 Announce Type: new Abstract: Training modern deep learning models is increasingly constrained by GPU memory and compute limits. While Randomized Numerical Linear Algebra (RandNLA) offers proven techniques to compress these models, the lack of a unified, production-grade library prevents widely adopting these methods. We present Panther, a PyTorch-compatible library that consolidates established RandNLA algorithms into a single high-performance framework. Panther engineers efficient, drop-in replacements for standard components including sketched linear layers, 2D convolution, multi-head attention, and randomized matrix decompositions (such as pivoted CholeskyQR). By implementing a custom C++/CUDA backend (pawX), Panther provides an optimized implementation that can run on both CPUs and GPUs. We demonstrate the effectiveness of RandNLA techniques and Panther's ease of adoption. By replacing standard PyTorch linear layers with Panther layers (requiring only a few lines of code) we achieve significant memory savings (up to 75%) on BERT while maintaining comparable loss. Source code is available (MIT License) at https://github.com/FahdSeddik/panther, along with demonstration video at https://youtu.be/7M3RQb4KWxs.
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https://arxiv.org/abs/2601.15473
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14804ef229a830ce1341f9a32163e1a0db4c88570e19afb904b03ebd29f7909f
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2026-01-23T00:00:00-05:00
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Multi-Targeted Graph Backdoor Attack
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arXiv:2601.15474v1 Announce Type: new Abstract: Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack for graph classification are limited to single target attack using subgraph replacement based mechanism where the attacker implants only one trigger into the GNN model. In this paper, we introduce the first multi-targeted backdoor attack for graph classification task, where multiple triggers simultaneously redirect predictions to different target labels. Instead of subgraph replacement, we propose subgraph injection which preserves the structure of the original graphs while poisoning the clean graphs. Extensive experiments demonstrate the efficacy of our approach, where our attack achieves high attack success rates for all target labels with minimal impact on the clean accuracy. Experimental results on five dataset demonstrate the superior performance of our attack framework compared to the conventional subgraph replacement-based attack. Our analysis on four GNN models confirms the generalization capability of our attack which is effective regardless of the GNN model architectures and training parameters settings. We further investigate the impact of the attack design parameters including injection methods, number of connections, trigger sizes, trigger edge density and poisoning ratios. Additionally, our evaluation against state-of-the-art defenses (randomized smoothing and fine-pruning) demonstrates the robustness of our proposed multi-target attacks. This work highlights the GNN vulnerability against multi-targeted backdoor attack in graph classification task. Our source codes will be available at https://github.com/SiSL-URI/Multi-Targeted-Graph-Backdoor-Attack.
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https://arxiv.org/abs/2601.15474
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f79979b3552fa9d307711616b2785ccd16f0905fadf21bc682652700de5d0f71
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2026-01-23T00:00:00-05:00
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Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events
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arXiv:2601.15475v1 Announce Type: new Abstract: Novel view synthesis from low dynamic range (LDR) blurry images, which are common in the wild, struggles to recover high dynamic range (HDR) and sharp 3D representations in extreme lighting conditions. Although existing methods employ event data to address this issue, they ignore the sensor-physics mismatches between the camera output and physical world radiance, resulting in suboptimal HDR and deblurring results. To cope with this problem, we propose a unified sensor-physics grounded NeRF framework for sharp HDR novel view synthesis from single-exposure blurry LDR images and corresponding events. We employ NeRF to directly represent the actual radiance of the 3D scene in the HDR domain and model raw HDR scene rays hitting the sensor pixels as in the physical world. A pixel-wise RGB mapping field is introduced to align the above rendered pixel values with the sensor-recorded LDR pixel values of the input images. A novel event mapping field is also designed to bridge the physical scene dynamics and actual event sensor output. The two mapping fields are jointly optimized with the NeRF network, leveraging the spatial and temporal dynamic information in events to enhance the sharp HDR 3D representation learning. Experiments on the collected and public datasets demonstrate that our method can achieve state-of-the-art deblurring HDR novel view synthesis results with single-exposure blurry LDR images and corresponding events.
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https://arxiv.org/abs/2601.15475
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f911d72c5f843dd064364bfc5089c2b8f6b6cebeee90b33b38335bbb1b527751
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2026-01-23T00:00:00-05:00
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Reliability by design: quantifying and eliminating fabrication risk in LLMs. From generative to consultative AI: a comparative analysis in the legal domain and lessons for high-stakes knowledge bases
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arXiv:2601.15476v1 Announce Type: new Abstract: This paper examines how to make large language models reliable for high-stakes legal work by reducing hallucinations. It distinguishes three AI paradigms: (1) standalone generative models ("creative oracle"), (2) basic retrieval-augmented systems ("expert archivist"), and (3) an advanced, end-to-end optimized RAG system ("rigorous archivist"). The authors introduce two reliability metrics -False Citation Rate (FCR) and Fabricated Fact Rate (FFR)- and evaluate 2,700 judicial-style answers from 12 LLMs across 75 legal tasks using expert, double-blind review. Results show that standalone models are unsuitable for professional use (FCR above 30%), while basic RAG greatly reduces errors but still leaves notable misgrounding. Advanced RAG, using techniques such as embedding fine-tuning, re-ranking, and self-correction, reduces fabrication to negligible levels (below 0.2%). The study concludes that trustworthy legal AI requires rigor-focused, retrieval-based architectures emphasizing verification and traceability, and provides an evaluation framework applicable to other high-risk domains.
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https://arxiv.org/abs/2601.15476
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8e2e63df17481a3122a57b62ff249c2714f8d39f788cf5ea14c37f5a925850b0
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2026-01-23T00:00:00-05:00
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Equal-Pay Contracts
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arXiv:2601.15478v1 Announce Type: new Abstract: We study multi-agent contract design, where a principal incentivizes a team of agents to take costly actions that jointly determine the project success via a combinatorial reward function. While prior work largely focuses on unconstrained contracts that allow heterogeneous payments across agents, many real-world environments limit payment dispersion. Motivated by this, we study equal-pay contracts, where all agents receive identical payments. Our results also extend to nearly-equal-pay contracts where any two payments are identical up to a constant factor. We provide both algorithmic and hardness results across a broad hierarchy of reward functions, under both binary and combinatorial action models. While we focus on equal-pay contracts, our analysis also yields new insights into unconstrained contract design, and resolves two important open problems. On the positive side, we design polynomial-time O(1)-approximation algorithms for (i) submodular rewards under combinatorial actions, and (ii) XOS rewards under binary actions. These guarantees are tight: We rule out the existence of (i) a PTAS for combinatorial actions, even for gross substitutes rewards (unless P = NP), and (ii) any O(1)-approximation for XOS rewards with combinatorial actions. Crucially, our hardness results hold even for unconstrained contracts, thereby settling the corresponding open problems in this setting. Finally, we quantify the loss induced by fairness via the price of equality, defined as the worst-case ratio between the optimal principal's utility achievable by unconstrained contracts and that achievable by equal-pay contracts. We obtain a bound of $\Theta(\log n/ \log \log n)$, where $n$ is the number of agents. This gap is tight in a strong sense: the upper bound applies even for XOS rewards with combinatorial actions, while the lower bound arises already for additive rewards with binary actions.
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https://arxiv.org/abs/2601.15478
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40815fac174011ea05ce6252429f6eb6153fe80e1fa1f668b36d046a20cc9335
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2026-01-23T00:00:00-05:00
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Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts
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arXiv:2601.15479v1 Announce Type: new Abstract: The safe deployment of large language models (LLMs) in high-stakes fields like biomedicine, requires them to be able to reason about cause and effect. We investigate this ability by testing 13 open-source LLMs on a fundamental task: pairwise causal discovery (PCD) from text. Our benchmark, using 12 diverse datasets, evaluates two core skills: 1) \textbf{Causal Detection} (identifying if a text contains a causal link) and 2) \textbf{Causal Extraction} (pulling out the exact cause and effect phrases). We tested various prompting methods, from simple instructions (zero-shot) to more complex strategies like Chain-of-Thought (CoT) and Few-shot In-Context Learning (FICL). The results show major deficiencies in current models. The best model for detection, DeepSeek-R1-Distill-Llama-70B, only achieved a mean score of 49.57\% ($C_{detect}$), while the best for extraction, Qwen2.5-Coder-32B-Instruct, reached just 47.12\% ($C_{extract}$). Models performed best on simple, explicit, single-sentence relations. However, performance plummeted for more difficult (and realistic) cases, such as implicit relationships, links spanning multiple sentences, and texts containing multiple causal pairs. We provide a unified evaluation framework, built on a dataset validated with high inter-annotator agreement ($\kappa \ge 0.758$), and make all our data, code, and prompts publicly available to spur further research. \href{https://github.com/sydneyanuyah/CausalDiscovery}{Code available here: https://github.com/sydneyanuyah/CausalDiscovery}
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https://arxiv.org/abs/2601.15479
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Academic Papers
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3fc398ac2f560aaf08b4e275f2415cd14abcf7645e841fb3a89446393c7da1cd
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2026-01-23T00:00:00-05:00
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Early predicting of hospital admission using machine learning algorithms: Priority queues approach
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arXiv:2601.15481v1 Announce Type: new Abstract: Emergency Department overcrowding is a critical issue that compromises patient safety and operational efficiency, necessitating accurate demand forecasting for effective resource allocation. This study evaluates and compares three distinct predictive models: Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), EXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks for forecasting daily ED arrivals over a seven-day horizon. Utilizing data from an Australian tertiary referral hospital spanning January 2017 to December 2021, this research distinguishes itself by decomposing demand into eight specific ward categories and stratifying patients by clinical complexity. To address data distortions caused by the COVID-19 pandemic, the study employs the Prophet model to generate synthetic counterfactual values for the anomalous period. Experimental results demonstrate that all three proposed models consistently outperform a seasonal naive baseline. XGBoost demonstrated the highest accuracy for predicting total daily admissions with a Mean Absolute Error of 6.63, while the statistical SARIMAX model proved marginally superior for forecasting major complexity cases with an MAE of 3.77. The study concludes that while these techniques successfully reproduce regular day-to-day patterns, they share a common limitation in underestimating sudden, infrequent surges in patient volume.
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https://arxiv.org/abs/2601.15481
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Academic Papers
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b64c956f4f94a51447b6d376ba34407d0a9fb73ed07b9026abec5ff0323ce319
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2026-01-23T00:00:00-05:00
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Martingale Foresight Sampling: A Principled Approach to Inference-Time LLM Decoding
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arXiv:2601.15482v1 Announce Type: new Abstract: Standard autoregressive decoding in large language models (LLMs) is inherently short-sighted, often failing to find globally optimal reasoning paths due to its token-by-token generation process. While inference-time strategies like foresight sampling attempt to mitigate this by simulating future steps, they typically rely on ad-hoc heuristics for valuing paths and pruning the search space. This paper introduces Martingale Foresight Sampling (MFS), a principled framework that reformulates LLM decoding as a problem of identifying an optimal stochastic process. By modeling the quality of a reasoning path as a stochastic process, we leverage Martingale theory to design a theoretically-grounded algorithm. Our approach replaces heuristic mechanisms with principles from probability theory: step valuation is derived from the Doob Decomposition Theorem to measure a path's predictable advantage, path selection uses Optional Stopping Theory for principled pruning of suboptimal candidates, and an adaptive stopping rule based on the Martingale Convergence Theorem terminates exploration once a path's quality has provably converged. Experiments on six reasoning benchmarks demonstrate that MFS surpasses state-of-the-art methods in accuracy while significantly improving computational efficiency. Code will be released at https://github.com/miraclehetech/EACL2026-Martingale-Foresight-Sampling.
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https://arxiv.org/abs/2601.15482
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Academic Papers
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7f7ce65725b3b4bc43e83b668ee13e188471eb72fc9aa96df165d7044099ab43
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2026-01-23T00:00:00-05:00
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Is Grokipedia Right-Leaning? Comparing Political Framing in Wikipedia and Grokipedia on Controversial Topics
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arXiv:2601.15484v1 Announce Type: new Abstract: Online encyclopedias are central to contemporary information infrastructures and have become focal points of debates over ideological bias. Wikipedia, in particular, has long been accused of left-leaning bias, while Grokipedia, an AI-generated encyclopedia launched by xAI, has been framed as a right-leaning alternative. This paper presents a comparative analysis of Wikipedia and Grokipedia on well-established politically contested topics. Specifically, we examine differences in semantic framing, political orientation, and content prioritization. We find that semantic similarity between the two platforms decays across article sections and diverges more strongly on controversial topics than on randomly sampled ones. Additionally, we show that both encyclopedias predominantly exhibit left-leaning framings, although Grokipedia exhibits a more bimodal distribution with increased prominence of right-leaning content. The experimental code is publicly available.
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https://arxiv.org/abs/2601.15484
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Academic Papers
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1b94537275584c51e641a1ad62b31be4d96a5e1562245e7b361477d597b6a8a6
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2026-01-23T00:00:00-05:00
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The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
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arXiv:2601.15485v1 Announce Type: new Abstract: Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diffusing into scientific practice, holding substantial promise while raising widespread concerns. Despite growing attention to AI use in scientific writing and evaluation, little is known about how the rise of LLMs is reshaping the public funding landscape. Here, we examine LLM involvement at key stages of the federal funding pipeline by combining two complementary data sources: confidential National Science Foundation (NSF) and National Institutes of Health (NIH) proposal submissions from two large US R1 universities, including funded, unfunded, and pending proposals, and the full population of publicly released NSF and NIH awards. We find that LLM use rises sharply beginning in 2023 and exhibits a bimodal distribution, indicating a clear split between minimal and substantive use. Across both private submissions and public awards, higher LLM involvement is consistently associated with lower semantic distinctiveness, positioning projects closer to recently funded work within the same agency. The consequences of this shift are agency-dependent. LLM use is positively associated with proposal success and higher subsequent publication output at NIH, whereas no comparable associations are observed at NSF. Notably, the productivity gains at NIH are concentrated in non-hit papers rather than the most highly cited work. Together, these findings provide large-scale evidence that the rise of LLMs is reshaping how scientific ideas are positioned, selected, and translated into publicly funded research, with implications for portfolio governance, research diversity, and the long-run impact of science.
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https://arxiv.org/abs/2601.15485
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Academic Papers
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5f123306f51c81fefa242a919633d02fdc8a79333bd9238e8d3359763df1eb08
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2026-01-23T00:00:00-05:00
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A Universal Large Language Model -- Drone Command and Control Interface
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arXiv:2601.15486v1 Announce Type: new Abstract: The use of artificial intelligence (AI) for drone control can have a transformative impact on drone capabilities, especially when real world information can be integrated with drone sensing, command, and control, part of a growing field of physical AI. Large language models (LLMs) can be advantageous if trained at scale on general knowledge, but especially and in particular when the training data includes information such as detailed map geography topology of the entire planet, as well as the ability to access real time situational data such as weather. However, challenges remain in the interface between drones and LLMs in general, with each application requiring a tedious, labor intensive effort to connect the LLM trained knowledge to drone command and control. Here, we solve that problem, using an interface strategy that is LLM agnostic and drone agnostic, providing the first universal, versatile, comprehensive and easy to use drone control interface. We do this using the new model context protocol (MCP) standard, an open standard that provides a universal way for AI systems to access external data, tools, and services. We develop and deploy a cloud based Linux machine hosting an MCP server that supports the Mavlink protocol, an ubiquitous drone control language used almost universally by millions of drones including Ardupilot and PX4 framework.We demonstrate flight control of a real unmanned aerial vehicle. In further testing, we demonstrate extensive flight planning and control capability in a simulated drone, integrated with a Google Maps MCP server for up to date, real time navigation information. This demonstrates a universal approach to integration of LLMs with drone command and control, a paradigm that leverages and exploits virtually all of modern AI industry with drone technology in an easy to use interface that translates natural language to drone control.
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https://arxiv.org/abs/2601.15486
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Academic Papers
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88c6b69a701b2e3b62f8c7b0015a0d7bfa1b8beddb3f9d5d18c7edb9c035e61e
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2026-01-23T00:00:00-05:00
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MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation
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arXiv:2601.15487v1 Announce Type: new Abstract: The rapid evolution of Retrieval-Augmented Generation (RAG) toward multimodal, high-stakes enterprise applications has outpaced the development of domain specific evaluation benchmarks. Existing datasets often rely on general-domain corpora or purely textual retrieval, failing to capture the complexity of specialized technical documents where information is inextricably multimodal and reasoning requires synthesizing disjoint evidence. We address this gap by introducing MiRAGE, a Multiagent framework for RAG systems Evaluation, that leverages a collaborative swarm of specialized agents to generate verified, domain-specific, multimodal, and multi-hop Question-Answer datasets. MiRAGE orchestrates a swarm of specialized agents: a recursive context optimization loop to aggregate scattered evidence, an adversarial verifier agent to guarantee factual grounding, and an agent to recognize the expert persona and the relevant domain to mimic expert cognitive workflows. Extensive empirical evaluation across four distinct domains (regulations, finance, quantitative biology, and journalism) demonstrates that MiRAGE generates datasets with significantly higher reasoning complexity (>2.3 average hops) and factual faithfulness. Our ablation studies point that MiRAGE can be powered by LLMs if textual descriptions of the images are available. Visual grounding still remains a frontier. By automating the creation of gold standard evaluation datasets that reflect the latent thematic structure of proprietary corpora, MiRAGE provides the necessary infrastructure to rigorously benchmark the next generation information retrieval systems.
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https://arxiv.org/abs/2601.15487
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Academic Papers
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svg
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a5ec05d93223b2d1e59498a73191c3c15f8c9d3837976739e212d440da5ad2de
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2026-01-23T00:00:00-05:00
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Multi-Persona Thinking for Bias Mitigation in Large Language Models
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arXiv:2601.15488v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit significant social biases that can perpetuate harmful stereotypes and unfair outcomes. In this paper, we propose Multi-Persona Thinking (MPT), a novel inference-time framework that leverages dialectical reasoning from multiple perspectives to reduce bias. MPT guides models to adopt contrasting social identities (e.g., male and female) along with a neutral viewpoint, and then engages these personas iteratively to expose and correct biases. Through a dialectical reasoning process, the framework transforms the potential weakness of persona assignment into a strength for bias mitigation. We evaluate MPT on two widely used bias benchmarks across both open-source and closed-source models of varying scales. Our results demonstrate substantial improvements over existing prompting-based strategies: MPT achieves the lowest bias while maintaining core reasoning ability.
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https://arxiv.org/abs/2601.15488
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Academic Papers
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b11275a983228c1fc4bdbd99cba1ce0826decc568d9086b5b21380d7ddd98df2
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2026-01-23T00:00:00-05:00
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Hybrid Vision Transformer_GAN Attribute Neutralizer for Mitigating Bias in Chest X_Ray Diagnosis
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arXiv:2601.15490v1 Announce Type: new Abstract: Bias in chest X-ray classifiers frequently stems from sex- and age-related shortcuts, leading to systematic underdiagnosis of minority subgroups. Previous pixel-space attribute neutralizers, which rely on convolutional encoders, lessen but do not fully remove this attribute leakage at clinically usable edit strengths. This study evaluates whether substituting the U-Net convolutional encoder with a Vision Transformer backbone in the Attribute-Neutral Framework can reduce demographic attribute leakage while preserving diagnostic accuracy. A data-efficient Image Transformer Small (DeiT-S) neutralizer was trained on the ChestX-ray14 dataset. Its edited images, generated across eleven edit-intensity levels, were evaluated with an independent AI judge for attribute leakage and with a convolutional neural network (ConvNet) for disease prediction. At a moderate edit level (alpha = 0.5), the Vision Transformer (ViT) neutralizer reduces patient sex-recognition area under the curve (AUC) to approximately 0.80, about 10 percentage points below the original framework's convolutional U-Net encoder, despite being trained for only half as many epochs. Meanwhile, macro receiver operating characteristic area under the curve (ROC AUC) across 15 findings stays within five percentage points of the unedited baseline, and the worst-case subgroup AUC remains near 0.70. These results indicate that global self-attention vision models can further suppress attribute leakage without sacrificing clinical utility, suggesting a practical route toward fairer chest X-ray AI.
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https://arxiv.org/abs/2601.15490
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Academic Papers
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b8ab710d0695719c95fd4041b155887b3e1beb50b9ec6c760ab6262e427abdb1
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2026-01-23T00:00:00-05:00
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Testing Deep Learning Libraries via Neurosymbolic Constraint Learning
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arXiv:2601.15493v1 Announce Type: new Abstract: Deep Learning (DL) libraries (e.g., PyTorch) are popular in AI development. These libraries are complex and contain bugs. Researchers have proposed various bug-finding techniques for such libraries. Yet, there is much room for improvement. A key challenge in testing DL libraries is the lack of API specifications. Prior testing approaches often inaccurately model the input specifications of DL APIs, resulting in missed valid inputs that could reveal bugs or false alarms due to invalid inputs. To address this challenge, we develop Centaur -- the first neurosymbolic technique to test DL library APIs using dynamically learned input constraints. Centaur leverages the key idea that formal API constraints can be learned from a small number of automatically generated seed inputs, and that the learned constraints can be solved using SMT solvers to generate valid and diverse test inputs. We develop a novel grammar that represents first-order logic formulae over API parameters and expresses tensor-related properties (e.g., shape, data types) as well as relational properties between parameters. We use the grammar to guide a Large Language Model (LLM) to enumerate syntactically correct candidate rules, validated using seed inputs. Further, we develop a custom refinement strategy to prune the set of learned rules to eliminate spurious or redundant rules. We use the learned constraints to systematically generate valid and diverse inputs by integrating SMT-based solving with randomized sampling. We evaluate Centaur for testing PyTorch and TensorFlow. Our results show that Centaur's constraints have a recall of 94.0% and a precision of 94.0% on average. In terms of coverage, Centaur covers 203, 150, and 9,608 more branches than TitanFuzz, ACETest and Pathfinder, respectively. Using Centaur, we also detect 26 new bugs in PyTorch and TensorFlow, 18 of which are confirmed.
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https://arxiv.org/abs/2601.15493
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Academic Papers
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6e7559a90abbd652a9e175fde48109910c5b8d26ccb7576cdf1d66c191fa7665
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2026-01-23T00:00:00-05:00
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Tracking the Limits of Knowledge Propagation: How LLMs Fail at Multi-Step Reasoning with Conflicting Knowledge
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arXiv:2601.15495v1 Announce Type: new Abstract: A common solution for mitigating outdated or incorrect information in Large Language Models (LLMs) is to provide updated facts in-context or through knowledge editing. However, these methods introduce knowledge conflicts when the knowledge update fails to overwrite the model's parametric knowledge, which propagate to faulty reasoning. Current benchmarks for this problem, however, largely focus only on single knowledge updates and fact recall without evaluating how these updates affect downstream reasoning. In this work, we introduce TRACK (Testing Reasoning Amid Conflicting Knowledge), a new benchmark for studying how LLMs propagate new knowledge through multi-step reasoning when it conflicts with the model's initial parametric knowledge. Spanning three reasoning-intensive scenarios (WIKI, CODE, and MATH), TRACK introduces multiple, realistic conflicts to mirror real-world complexity. Our results on TRACK reveal that providing updated facts to models for reasoning can worsen performance compared to providing no updated facts to a model, and that this performance degradation exacerbates as more updated facts are provided. We show this failure stems from both inability to faithfully integrate updated facts, but also flawed reasoning even when knowledge is integrated. TRACK provides a rigorous new benchmark to measure and guide future progress on propagating conflicting knowledge in multi-step reasoning.
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https://arxiv.org/abs/2601.15495
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Academic Papers
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svg
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10843968a957829fabc95d276c21e8e93b93400b89cb8ab7bec8f0daf47456ab
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2026-01-23T00:00:00-05:00
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Semantics in Actuation Systems: From Age of Actuation to Age of Actuated Information
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arXiv:2601.15496v1 Announce Type: new Abstract: In this paper, we study the timeliness of actions in communication systems where actuation is constrained by control permissions or energy availability. Building on the Age of Actuation (AoA) metric, which quantifies the timeliness of actions independently of data freshness, we introduce a new metric, the \emph{Age of Actuated Information (AoAI)}. AoAI captures the end-to-end timeliness of actions by explicitly accounting for the age of the data packet at the moment it is actuated. We analyze and characterize both AoA and AoAI in discrete-time systems with data storage capabilities under multiple actuation scenarios. The actuator requires both a data packet and an actuation opportunity, which may be provided by a controller or enabled by harvested energy. Data packets may be stored either in a single-packet buffer or an infinite-capacity queue for future actuation. For these settings, we derive closed-form expressions for the average AoA and AoAI and investigate their structural differences. While AoA and AoAI coincide in instantaneous actuation systems, they differentiate when data buffering is present. Our results reveal counterintuitive regimes in which increasing update or actuation rates degrade action timeliness for both AoA and AoAI. Moreover, as part of the analysis, we obtain a novel closed-form characterization of the steady-state distribution of a Geo/Geo/1 queue operating under the FCFS discipline, expressed solely in terms of the queue length and the age of the head-of-line packet. The proposed metrics and analytical results provide new insights into the semantics of timeliness in systems where information ultimately serves the purpose of actuation.
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https://arxiv.org/abs/2601.15496
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Academic Papers
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svg
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2548f23a1d1e9996d194cdd5a054105fd151abfd526d92cd4aaff8ad06868f5e
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2026-01-23T00:00:00-05:00
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MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification
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arXiv:2601.15498v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent methods improve draft quality by tightly coupling the drafter with the target model, the verification mechanism itself remains largely unchanged, relying on strict token-level rejection sampling. In practice, modern LLMs frequently operate in low-margin regimes where the target model exhibits weak preference among top candidates. In such cases, rejecting plausible runner-up tokens yields negligible information gain while incurring substantial rollback cost, leading to a fundamental inefficiency in verification. We propose Margin-Aware Speculative Verification, a training-free and domain-agnostic verification strategy that adapts to the target model's local decisiveness. Our method conditions verification on decision stability measured directly from the target logits and relaxes rejection only when strict verification provides minimal benefit. Importantly, the approach modifies only the verification rule and is fully compatible with existing target-coupled speculative decoding frameworks. Extensive experiments across model scales ranging from 8B to 235B demonstrate that our method delivers consistent and significant inference speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks.
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https://arxiv.org/abs/2601.15498
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Academic Papers
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svg
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ad93e3da8c7fd7cce43408b9b8fa51f10518508dfb0d3d0eb45391c7bacc6517
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2026-01-23T00:00:00-05:00
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Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learning
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arXiv:2601.15503v1 Announce Type: new Abstract: Volunteer-led lake monitoring yields irregular, seasonal time series with many gaps arising from ice cover, weather-related access constraints, and occasional human errors, complicating forecasting and early warning of harmful algal blooms. We study Secchi Disk Depth (SDD) forecasting on a 30-lake, data-rich subset drawn from three decades of in situ records collected across Maine lakes. Missingness is handled via Multiple Imputation by Chained Equations (MICE), and we evaluate performance with a normalized Mean Absolute Error (nMAE) metric for cross-lake comparability. Among six candidates, ridge regression provides the best mean test performance. Using ridge regression, we then quantify the minimal sample size, showing that under a backward, recent-history protocol, the model reaches within 5% of full-history accuracy with approximately 176 training samples per lake on average. We also identify a minimal feature set, where a compact four-feature subset matches the thirteen-feature baseline within the same 5% tolerance. Bringing these results together, we introduce a joint feasibility function that identifies the minimal training history and fewest predictors sufficient to achieve the target of staying within 5% of the complete-history, full-feature baseline. In our study, meeting the 5% accuracy target required about 64 recent samples and just one predictor per lake, highlighting the practicality of targeted monitoring. Hence, our joint feasibility strategy unifies recent-history length and feature choice under a fixed accuracy target, yielding a simple, efficient rule for setting sampling effort and measurement priorities for lake researchers.
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https://arxiv.org/abs/2601.15503
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Academic Papers
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svg
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c8ae64240ab7b04d2403a045a10f79a1573509013cebb485599b6aed511a5c6b
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2026-01-23T00:00:00-05:00
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SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model
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arXiv:2601.15504v1 Announce Type: new Abstract: Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCNs) trained with a masked central spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that robustly recover masked genes, with 91% of masked genes showing significant correlations (p < 0.05). The embeddings generated by SAGE-FM outperform MOFA and existing spatial transcriptomics methods in unsupervised clustering and preservation of biological heterogeneity. SAGE-FM generalizes to downstream tasks, enabling 81% accuracy in pathologist-defined spot annotation in oropharyngeal squamous cell carcinoma and improving glioblastoma subtype prediction relative to MOFA. In silico perturbation experiments further demonstrate that the model captures directional ligand-receptor and upstream-downstream regulatory effects consistent with ground truth. These results demonstrate that simple, parameter-efficient GCNs can serve as biologically interpretable and spatially aware foundation models for large-scale spatial transcriptomics.
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https://arxiv.org/abs/2601.15504
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Academic Papers
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svg
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bc3f730122f8f2ca288ed93ef0178b9663097a27454d395e5b71f3f8b2893377
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2026-01-23T00:00:00-05:00
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Stabilizer-Code Channel Transforms Beyond Repetition Codes for Improved Hashing Bounds
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arXiv:2601.15505v1 Announce Type: new Abstract: The quantum hashing bound guarantees that rates up to $1-H(p_I, p_X, p_Y, p_Z)$ are achievable for memoryless Pauli channels, but it is not generally tight. A known way to improve achievable rates for certain asymmetric Pauli channels is to apply a small inner stabilizer code to a few channel uses, decode, and treat the resulting logical noise as an induced Pauli channel; reapplying the hashing argument to this induced channel can beat the baseline hashing bound. We generalize this induced-channel viewpoint to arbitrary stabilizer codes used purely as channel transforms. Given any $ [\![ n, k ]\!] $ stabilizer generator set, we construct a full symplectic tableau, compute the induced joint distribution of logical Pauli errors and syndromes under the physical Pauli channel, and obtain an achievable rate via a hashing bound with decoder side information. We perform a structured search over small transforms and report instances that improve the baseline hashing bound for a family of Pauli channels with skewed and independent errors studied in prior work.
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https://arxiv.org/abs/2601.15505
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Academic Papers
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