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ff6b1008ef0fc0293e087c16567dbc96164421c150c862c53998a78f9f90d41a | 2026-01-22T07:00:22+00:00 | In Science Journals | Science, Volume 391, Issue 6783, Page 363-365, January 2026. | https://www.science.org/doi/abs/10.1126/science.aef5688?af=R | Academic Papers | svg |
654bb6d5317a419342b292fd1dd51fa1df307c1160a34eba3286cd521de6fb14 | 2026-01-22T07:00:22+00:00 | Editorial Expression of Concern for the Report “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances” | Science, Volume 391, Issue 6783, Page 360-360, January 2026. | https://www.science.org/doi/abs/10.1126/science.aee6983?af=R | Academic Papers | svg |
7beb35b17a6ed254b7d8a48ead5fe03b7e7d2f78c9470973c0ae44174acfd00e | 2026-01-23T00:00:00-05:00 | Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarios | 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 s... | https://arxiv.org/abs/2601.15290 | Academic Papers | svg |
6c84da605489ce8c72981561b9706b61d2e9ef442dde09e9b7abec34dba39553 | 2026-01-23T00:00:00-05:00 | Public transport challenges and technology-assisted accessibility for visually impaired elderly residents in urban environments | 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 ... | https://arxiv.org/abs/2601.15291 | Academic Papers | svg |
0ba16c6761ad7aff12b1d1163d042cfd07cf36d591da0310b4c396200fd4036d | 2026-01-23T00:00:00-05:00 | A Mobile Application Front-End for Presenting Explainable AI Results in Diabetes Risk Estimation | 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. Explai... | https://arxiv.org/abs/2601.15292 | Academic Papers | svg |
05def5a25790a92f13410a80e10b5b8135a73dcf53f70493916ddc879d11eb14 | 2026-01-23T00:00:00-05:00 | Social Robotics for Disabled Students: An Empirical Investigation of Embodiment, Roles and Interaction | 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 stude... | https://arxiv.org/abs/2601.15293 | Academic Papers | svg |
210a1fe6fac038851b6d49eaf47268315f73399c5e0cc043c973e37ef73b4271 | 2026-01-23T00:00:00-05:00 | KnowTeX: Visualizing Mathematical Dependencies | 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 t... | https://arxiv.org/abs/2601.15294 | Academic Papers | svg |
ef8330f089220902622e5d2b6d34c84f927e2c1175458c880110aa9dcd95b32d | 2026-01-23T00:00:00-05:00 | Elsewise: Authoring AI-Based Interactive Narrative with Possibility Space Visualization | 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... | https://arxiv.org/abs/2601.15295 | Academic Papers | svg |
0d5e3b9393a5c70cf8beaf8c7bae7405420ef0fa120a9e5bf0f9712f168d162d | 2026-01-23T00:00:00-05:00 | Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration | 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 b... | https://arxiv.org/abs/2601.15296 | Academic Papers | svg |
883883b3079df370054df44ecea51f46bbabdecd9781734f6e78b9445e320169 | 2026-01-23T00:00:00-05:00 | AfriEconQA: A Benchmark Dataset for African Economic Analysis based on World Bank Reports | 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 d... | https://arxiv.org/abs/2601.15297 | Academic Papers | svg |
9520ddf70893676efc3d393155ff8f9ddcc78e02868a2fd14e08be5e264d030f | 2026-01-23T00:00:00-05:00 | Embedding Retrofitting: Data Engineering for better RAG | 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 pape... | https://arxiv.org/abs/2601.15298 | Academic Papers | svg |
26e7e1e82e0d107da5528098e516275f64880d4917fc2b6f095a864fd5273fa7 | 2026-01-23T00:00:00-05:00 | MALTopic: Multi-Agent LLM Topic Modeling Framework | 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 categori... | https://arxiv.org/abs/2601.15299 | Academic Papers | svg |
602c98e4c59755b6b255bc8573b811dfc2035a251285826dd52ed15e143977ea | 2026-01-23T00:00:00-05:00 | Intelligence Degradation in Long-Context LLMs: Critical Threshold Determination via Natural Length Distribution Analysis | 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 l... | https://arxiv.org/abs/2601.15300 | Academic Papers | svg |
6a8372dc691bcb98060a91ff07a9b70b51eb17872322fc79eef7b95109c9150a | 2026-01-23T00:00:00-05:00 | Can We Trust LLM Detectors? | 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 distribut... | https://arxiv.org/abs/2601.15301 | Academic Papers | svg |
eb43dab26339fe9d32cd531584137f9d378194d947135457dee1a59067169c3c | 2026-01-23T00:00:00-05:00 | Gated Sparse Attention: Combining Computational Efficiency with Training Stability for Long-Context Language Models | 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 whi... | https://arxiv.org/abs/2601.15305 | Academic Papers | svg |
906607b17f20ad534584176a81016c398bb5c96b96d8010fa7fe4bb4f9467a21 | 2026-01-23T00:00:00-05:00 | Uncovering Latent Bias in LLM-Based Emergency Department Triage Through Proxy Variables | 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... | https://arxiv.org/abs/2601.15306 | Academic Papers | svg |
149330be11ba97ce3d25b73a72a4f5c2febc4f47929a8595c414192ade5e4023 | 2026-01-23T00:00:00-05:00 | DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Survey | 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 su... | https://arxiv.org/abs/2601.15307 | Academic Papers | svg |
617c581d5e249e89558e52da1e42778479c02e8dd963bab8d11db7b7bb89e55b | 2026-01-23T00:00:00-05:00 | When Generative AI Meets Extended Reality: Enabling Scalable and Natural Interactions | 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... | https://arxiv.org/abs/2601.15308 | Academic Papers | svg |
c31543d45882981f06711a82ab5e08dfc30e43e4154bd52e2f856d21a7e3a279 | 2026-01-23T00:00:00-05:00 | Designing Persuasive Social Robots for Health Behavior Change: A Systematic Review of Behavior Change Strategies and Evaluation Methods | 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 soci... | https://arxiv.org/abs/2601.15309 | Academic Papers | svg |
71da0366fcdbf2aa212cfc0acb4c29a2fcd4c2fa61748688ae176e986e4f9e30 | 2026-01-23T00:00:00-05:00 | Aeon: High-Performance Neuro-Symbolic Memory Management for Long-Horizon LLM Agents | 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... | https://arxiv.org/abs/2601.15311 | Academic Papers | svg |
1903278c8c0a11b96fa0f8708a5521b3be3f99a1e8b85eda4594e29f28573820 | 2026-01-23T00:00:00-05:00 | Do people expect different behavior from large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games | 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 deci... | https://arxiv.org/abs/2601.15312 | Academic Papers | svg |
4183d86febb059786e1755d7e7a6d0a32ae9a1d16e1871b476e0d80a4e3e32f3 | 2026-01-23T00:00:00-05:00 | The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection | 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... | https://arxiv.org/abs/2601.15316 | Academic Papers | svg |
96694704c46d05cb3d96c82126abffa69f2b668cc29712f988b7e35dfc9bac5e | 2026-01-23T00:00:00-05:00 | On the closest balanced game | 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 ... | https://arxiv.org/abs/2601.15318 | Academic Papers | svg |
f8c138ab4b4fd2746582977d05da7912feee9ee34d19e11181d430e5e3e6af50 | 2026-01-23T00:00:00-05:00 | Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents | 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... | https://arxiv.org/abs/2601.15322 | Academic Papers | svg |
b80f1095036962bbd0a62df69096cab2550a6ac2be6f2a473bceecfbb2be90bb | 2026-01-23T00:00:00-05:00 | Prometheus Mind: Retrofitting Memory to Frozen Language Models | 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... | https://arxiv.org/abs/2601.15324 | Academic Papers | svg |
03a2ebd666cf56f351d2b437793a3cf5ded65c8f65eac73bafd7953fc7b28fa0 | 2026-01-23T00:00:00-05:00 | MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection | 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 communit... | https://arxiv.org/abs/2601.15325 | Academic Papers | svg |
a72ef1ad2dabee5a6d42e8e0d799b6e39c53b64fae455a5a9744432450f89add | 2026-01-23T00:00:00-05:00 | Rules Create Unequal Rewards: Elite Tennis Players Allocate Resources Efficiently | 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. Howe... | https://arxiv.org/abs/2601.15327 | Academic Papers | svg |
09d14d0f215d39ecef298035cce23f21ff15ed5cd35b1c93da34ca9fdaf53a9f | 2026-01-23T00:00:00-05:00 | ICPO: Illocution-Calibrated Policy Optimization for Multi-Turn Conversation | 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-tr... | https://arxiv.org/abs/2601.15330 | Academic Papers | svg |
7001214685803a0227c01cb884dadf0b5ad668c62f0b12bab37717a70bb694e6 | 2026-01-23T00:00:00-05:00 | RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models | 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 a... | https://arxiv.org/abs/2601.15331 | Academic Papers | svg |
6a700598af08f458aeb6963282ea3dcc0e405703223b9611338c87f4a9ae621b | 2026-01-23T00:00:00-05:00 | Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference | 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 chall... | https://arxiv.org/abs/2601.15333 | Academic Papers | svg |
d2eca52a81cf002c49d30d99665a8e65699ce6912748e84ea4a0da9350d5c571 | 2026-01-23T00:00:00-05:00 | No Reliable Evidence of Self-Reported Sentience in Small Large Language Models | 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 cla... | https://arxiv.org/abs/2601.15334 | Academic Papers | svg |
5146d3fa2cd5aad5caefdccc26807878e12ef4f9253d1f6a7ef569bcdc2d7b56 | 2026-01-23T00:00:00-05:00 | ToolCaching: Towards Efficient Caching for LLM Tool-calling | 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 e... | https://arxiv.org/abs/2601.15335 | Academic Papers | svg |
3db707e6c188e3c0e2e977fb18423604f17a0f24e2a44290d5f5429ae1988600 | 2026-01-23T00:00:00-05:00 | Language Models Entangle Language and Culture | 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 ... | https://arxiv.org/abs/2601.15337 | Academic Papers | svg |
f0a1c01863430c263e20cdbd2f0a02e9e8dfa03ac2f8b1aa197a6ae2158dbb83 | 2026-01-23T00:00:00-05:00 | From Quotes to Concepts: Axial Coding of Political Debates with Ensemble LMs | 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 o... | https://arxiv.org/abs/2601.15338 | Academic Papers | svg |
ffc56cd9b2f6356678ca5353025b08adfe6311435c028360ac8ef467db67580a | 2026-01-23T00:00:00-05:00 | Lost in Transcription: How Speech-to-Text Errors Derail Code Understanding | 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, pa... | https://arxiv.org/abs/2601.15339 | Academic Papers | svg |
6a9951102944daa1c2b76c9e25ea2bca58926ce78f5c79b848dbeee9356a8703 | 2026-01-23T00:00:00-05:00 | Logic Programming on Knowledge Graph Networks And its Application in Medical Domain | 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 st... | https://arxiv.org/abs/2601.15347 | Academic Papers | svg |
44393e6d53982bfe96447fa50c619a145984a13e67143017170a4102cad010c3 | 2026-01-23T00:00:00-05:00 | Abusive music and song transformation using GenAI and LLMs | 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 (Ge... | https://arxiv.org/abs/2601.15348 | Academic Papers | svg |
d4815d82baddf417eae2edbfc82fde85564f70dbb1891c06b4a7dcce99854e8e | 2026-01-23T00:00:00-05:00 | Preparation and Motion Study of Magnetically Driven Micro Soft Robot Mimicking the Cownose Ray | 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... | https://arxiv.org/abs/2601.15349 | Academic Papers | svg |
1f5fcee09cd701472c4cf4c07c49ddd857974efc26080a339dfef44a6268c2e9 | 2026-01-23T00:00:00-05:00 | A Prompt-Based Framework for Loop Vulnerability Detection Using Local LLMs | 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 tradition... | https://arxiv.org/abs/2601.15352 | Academic Papers | svg |
9698adbbfae2a1d9eb2436207b571c92a52b9937b48408c9064fe0619f1ef4ae | 2026-01-23T00:00:00-05:00 | AI-Based Culvert-Sewer Inspection | 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 ... | https://arxiv.org/abs/2601.15366 | Academic Papers | svg |
d2de1c9e15b756bc1d41340fad660d83086f81c9e9e91cb4918003e89d5c4726 | 2026-01-23T00:00:00-05:00 | Improving MoE Compute Efficiency by Composing Weight and Data Sparsity | 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 dir... | https://arxiv.org/abs/2601.15370 | Academic Papers | svg |
e1f7ff65eed794a08205939385758ddef1124ecee386ed5eadc88a774fa1f440 | 2026-01-23T00:00:00-05:00 | You Need Better Attention Priors | 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 Tra... | https://arxiv.org/abs/2601.15380 | Academic Papers | svg |
36f053133062245399edfddeccbfe08475032728d7223d876d50a1c9374345af | 2026-01-23T00:00:00-05:00 | VegaChat: A Robust Framework for LLM-Based Chart Generation and Assessment | 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 ... | https://arxiv.org/abs/2601.15385 | Academic Papers | svg |
ed7768d7c76dabfc43414bb7c5f2ca8f8a647b71b95cd5ec2c3b055138b0c1f6 | 2026-01-23T00:00:00-05:00 | FedUMM: A General Framework for Federated Learning with Unified Multimodal Models | 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... | https://arxiv.org/abs/2601.15390 | Academic Papers | svg |
818cdaa52aa108961de2784e12c29cdf60f1f623f4e9067730b8af6edb0cdc28 | 2026-01-23T00:00:00-05:00 | GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation | 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 present... | https://arxiv.org/abs/2601.15392 | Academic Papers | svg |
d9f47ea0c8e775272c13884f4aeb7a4363fd740d18e2b11e66b7a793af0ba919 | 2026-01-23T00:00:00-05:00 | Memorization Dynamics in Knowledge Distillation for Language Models | 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 p... | https://arxiv.org/abs/2601.15394 | Academic Papers | svg |
e5914365f8673cb4094e47395a4f042788e5f63efc88c5aa606eb8dfcd441e1a | 2026-01-23T00:00:00-05:00 | Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind | 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 introdu... | https://arxiv.org/abs/2601.15395 | Academic Papers | svg |
19c04d52df877e18ba1946ce91231f1839eb928d97393f5aef93d4424ce7efa1 | 2026-01-23T00:00:00-05:00 | Beyond Prompting: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration (LOGIC) | 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 t... | https://arxiv.org/abs/2601.15397 | Academic Papers | svg |
3f4bdbcd5c45bcadd9a723f4950d4387012977fb8506706f31c6c1db00a879e8 | 2026-01-23T00:00:00-05:00 | Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC | 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) fr... | https://arxiv.org/abs/2601.15399 | Academic Papers | svg |
15ba943dc10ca801a9b35e211846205b1a632cca6558a34e4cea95b0877ec90a | 2026-01-23T00:00:00-05:00 | Multi-Input Ciphertext Multiplication for Homomorphic Encryption | 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 multiplica... | https://arxiv.org/abs/2601.15401 | Academic Papers | svg |
e1825e5808dc06227aa02b34c6d0b4b1a2c1259abd135aee3c1bd1ce2cf5b886 | 2026-01-23T00:00:00-05:00 | Partially Polarized Polar Codes: A New Design for 6G Control Channels | 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 guarantee... | https://arxiv.org/abs/2601.15404 | Academic Papers | svg |
7cb67fab21be83f6261f9699bc36458d00ff3728301583218b20bbc928660427 | 2026-01-23T00:00:00-05:00 | Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition | 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... | https://arxiv.org/abs/2601.15406 | Academic Papers | svg |
9b95362c8676856d1749b012a84cfa58e6b7323c687721f007e3bb09337d0c24 | 2026-01-23T00:00:00-05:00 | CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation | 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.... | https://arxiv.org/abs/2601.15408 | Academic Papers | svg |
6f825687f1485407222f8aeb4f802d85b30d1136512c8f9cad24d8542f675077 | 2026-01-23T00:00:00-05:00 | A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots | 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 concer... | https://arxiv.org/abs/2601.15412 | Academic Papers | svg |
c72f6a1e612f786e3b3017b734d02ec53bb5017b010b5cc6f6944c0b9c6468ff | 2026-01-23T00:00:00-05:00 | DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction | 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-b... | https://arxiv.org/abs/2601.15416 | Academic Papers | svg |
5dae8a073febd18327bb516fa4152bdf82e36d861e027d38aa1dfcc9ceb6ceb1 | 2026-01-23T00:00:00-05:00 | Ambient Dataloops: Generative Models for Dataset Refinement | 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 ofte... | https://arxiv.org/abs/2601.15417 | Academic Papers | svg |
5e4cdd366319146602837a0c95d0b5134fa6809f2638b5af04c3dfbf6d3ed2e3 | 2026-01-23T00:00:00-05:00 | Learning a Unified Latent Space for Cross-Embodiment Robot Control | 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 ... | https://arxiv.org/abs/2601.15419 | Academic Papers | svg |
ecf689bf81c05d6e7258f72cc26cbc0dd022d39d42ad9624fc0cbc3e30ba0c7d | 2026-01-23T00:00:00-05:00 | Problems with fixpoints of polynomials of polynomials | 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 fa... | https://arxiv.org/abs/2601.15420 | Academic Papers | svg |
38086516243bd8fde1f4351c3c903e6ffcfd4668d221cee68b32b6a270b0d610 | 2026-01-23T00:00:00-05:00 | Lattice: A Confidence-Gated Hybrid System for Uncertainty-Aware Sequential Prediction with Behavioral Archetypes | 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-ba... | https://arxiv.org/abs/2601.15423 | Academic Papers | svg |
c917a735e56f0bd545b7f552f7c78cc61f18afeccd6326039facbf02569d1476 | 2026-01-23T00:00:00-05:00 | Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs | 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: $\... | https://arxiv.org/abs/2601.15429 | Academic Papers | svg |
7f6f62b0b3aed8cf691ffda8d3d509f274efdf2f31ce1fcbba8c1f0060f7cb60 | 2026-01-23T00:00:00-05:00 | SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication | 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 Splatti... | https://arxiv.org/abs/2601.15431 | Academic Papers | svg |
1faf21bc8c1b96824f71e46d214779ae22cd3c1187a6d02fa34cb0d16d096aa8 | 2026-01-23T00:00:00-05:00 | MEDFORD in a Box: Improvements and Future Directions for a Metadata Description Language | 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, barrier... | https://arxiv.org/abs/2601.15432 | Academic Papers | svg |
db0b343af317ed3563d8e8f363ba0a6882cd73e45e7310c03892bbd1a91f967d | 2026-01-23T00:00:00-05:00 | ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering | 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... | https://arxiv.org/abs/2601.15434 | Academic Papers | svg |
9f301da13363782094ccc2c57db979d7762a4fcc9b889f2b33ed6972cbc8e55a | 2026-01-23T00:00:00-05:00 | Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models | 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, ... | https://arxiv.org/abs/2601.15436 | Academic Papers | svg |
9b35dbbf375fe825668a64fbbc432732af3194c10848ebd63ed02b30444f2e10 | 2026-01-23T00:00:00-05:00 | Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations | 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, ... | https://arxiv.org/abs/2601.15437 | Academic Papers | svg |
cbf6ca68af7919eae720938b46c5200cff32a24be70670efa4beb288b69a171c | 2026-01-23T00:00:00-05:00 | CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models | 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 mode... | https://arxiv.org/abs/2601.15441 | Academic Papers | svg |
be5eac4beb12ce8ed9e5933f52b11dd171a2d320c04731ece8b7d9f77fbf9cea | 2026-01-23T00:00:00-05:00 | A tensor network formalism for neuro-symbolic AI | 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 pa... | https://arxiv.org/abs/2601.15442 | Academic Papers | svg |
caaa449a31e703fb4018f8efad15f1f61526b0c852c3c71f5970f0a351202a0a | 2026-01-23T00:00:00-05:00 | Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation | 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 prioritiz... | https://arxiv.org/abs/2601.15445 | Academic Papers | svg |
a062820315546e6aced4eb32d5cc639f57375bfd772f90f27a32416ac0162ff9 | 2026-01-23T00:00:00-05:00 | DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection | 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 m... | https://arxiv.org/abs/2601.15453 | Academic Papers | svg |
aa94df05e7c3468fc4f796ecfd05ca35d70952581000280dcfa61cfae4b12cd6 | 2026-01-23T00:00:00-05:00 | Remarks on Algebraic Reconstruction of Types and Effects | 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-effe... | https://arxiv.org/abs/2601.15455 | Academic Papers | svg |
4a4ebbca07b69f832780657c1106cf28e765249de6f60955a4e04eab04901b89 | 2026-01-23T00:00:00-05:00 | Chunking, Retrieval, and Re-ranking: An Empirical Evaluation of RAG Architectures for Policy Document Question Answering | 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, t... | https://arxiv.org/abs/2601.15457 | Academic Papers | svg |
898e51ac97c37ae03321884031bb8082d5cc38ad4c5c639110fc5b60644da4ce | 2026-01-23T00:00:00-05:00 | MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering | 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 larg... | https://arxiv.org/abs/2601.15458 | Academic Papers | svg |
931dcbf6eb64ec13a85ec681f05ad5b434a8568c7806f605b99040b76627ab06 | 2026-01-23T00:00:00-05:00 | Neural Collision Detection for Multi-arm Laparoscopy Surgical Robots Through Learning-from-Simulation | 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 simulat... | https://arxiv.org/abs/2601.15459 | Academic Papers | svg |
1007343bd830ebd263f7d1f5df3adaadf17777fef1977986012d7c28fa48d05d | 2026-01-23T00:00:00-05:00 | Rank-metric codes over arbitrary fields: Bounds and constructions | 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 rediscov... | https://arxiv.org/abs/2601.15464 | Academic Papers | svg |
46c95f7ef533fa6017e24aa30a7d9fe0db70aaf6920ef5cc716cb5d0017b516d | 2026-01-23T00:00:00-05:00 | Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone Chatbots | 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 thr... | https://arxiv.org/abs/2601.15465 | Academic Papers | svg |
cb125a67c21efccbbdd773e0437eb7f4555cb1a1c068ef17e1ba565544c9cc7a | 2026-01-23T00:00:00-05:00 | Shape of You: Implications of Social Context and Avatar Body Shape on Relatedness, Emotions, and Performance in a Virtual Reality Workout | 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 ... | https://arxiv.org/abs/2601.15466 | Academic Papers | svg |
a2c5d0bc7f87c7a036887f763790e71088aa464cd64d9bf4729f3fa5ff251a67 | 2026-01-23T00:00:00-05:00 | Learning from Synthetic Data: Limitations of ERM | 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 fundamen... | https://arxiv.org/abs/2601.15468 | Academic Papers | svg |
91d80ef09f3f3573c062fe20a8f59418458884ba6feb74e351843a586d452121 | 2026-01-23T00:00:00-05:00 | Nested and outlier embeddings into trees | 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 e... | https://arxiv.org/abs/2601.15470 | Academic Papers | svg |
05c15de2db75c8a96dd14b02f1361de806ad700acdcc344be128932f78d351d6 | 2026-01-23T00:00:00-05:00 | Put Your Muscle Into It: Introducing XEM2, a Novel Approach for Monitoring Exertion in Stationary Physical Exercises Leveraging Muscle Work | 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 techn... | https://arxiv.org/abs/2601.15472 | Academic Papers | svg |
3369bfad5439c1ca57ee0c29966761777497fb5b1b204eae4397abd3edc7fc78 | 2026-01-23T00:00:00-05:00 | Panther: Faster and Cheaper Computations with Randomized Numerical Linear Algebra | 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 adopti... | https://arxiv.org/abs/2601.15473 | Academic Papers | svg |
14804ef229a830ce1341f9a32163e1a0db4c88570e19afb904b03ebd29f7909f | 2026-01-23T00:00:00-05:00 | Multi-Targeted Graph Backdoor Attack | 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 subg... | https://arxiv.org/abs/2601.15474 | Academic Papers | svg |
f79979b3552fa9d307711616b2785ccd16f0905fadf21bc682652700de5d0f71 | 2026-01-23T00:00:00-05:00 | Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events | 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, the... | https://arxiv.org/abs/2601.15475 | Academic Papers | svg |
f911d72c5f843dd064364bfc5089c2b8f6b6cebeee90b33b38335bbb1b527751 | 2026-01-23T00:00:00-05:00 | 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 | 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... | https://arxiv.org/abs/2601.15476 | Academic Papers | svg |
8e2e63df17481a3122a57b62ff249c2714f8d39f788cf5ea14c37f5a925850b0 | 2026-01-23T00:00:00-05:00 | Equal-Pay Contracts | 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 heterogeneou... | https://arxiv.org/abs/2601.15478 | Academic Papers | svg |
40815fac174011ea05ce6252429f6eb6153fe80e1fa1f668b36d046a20cc9335 | 2026-01-23T00:00:00-05:00 | Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts | 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 te... | https://arxiv.org/abs/2601.15479 | Academic Papers | svg |
3fc398ac2f560aaf08b4e275f2415cd14abcf7645e841fb3a89446393c7da1cd | 2026-01-23T00:00:00-05:00 | Early predicting of hospital admission using machine learning algorithms: Priority queues approach | 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 Auto... | https://arxiv.org/abs/2601.15481 | Academic Papers | svg |
b64c956f4f94a51447b6d376ba34407d0a9fb73ed07b9026abec5ff0323ce319 | 2026-01-23T00:00:00-05:00 | Martingale Foresight Sampling: A Principled Approach to Inference-Time LLM Decoding | 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 t... | https://arxiv.org/abs/2601.15482 | Academic Papers | svg |
7f7ce65725b3b4bc43e83b668ee13e188471eb72fc9aa96df165d7044099ab43 | 2026-01-23T00:00:00-05:00 | Is Grokipedia Right-Leaning? Comparing Political Framing in Wikipedia and Grokipedia on Controversial Topics | 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... | https://arxiv.org/abs/2601.15484 | Academic Papers | svg |
1b94537275584c51e641a1ad62b31be4d96a5e1562245e7b361477d597b6a8a6 | 2026-01-23T00:00:00-05:00 | The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding | 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 A... | https://arxiv.org/abs/2601.15485 | Academic Papers | svg |
5f123306f51c81fefa242a919633d02fdc8a79333bd9238e8d3359763df1eb08 | 2026-01-23T00:00:00-05:00 | A Universal Large Language Model -- Drone Command and Control Interface | 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 mode... | https://arxiv.org/abs/2601.15486 | Academic Papers | svg |
88c6b69a701b2e3b62f8c7b0015a0d7bfa1b8beddb3f9d5d18c7edb9c035e61e | 2026-01-23T00:00:00-05:00 | MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation | 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, fa... | https://arxiv.org/abs/2601.15487 | Academic Papers | svg |
a5ec05d93223b2d1e59498a73191c3c15f8c9d3837976739e212d440da5ad2de | 2026-01-23T00:00:00-05:00 | Multi-Persona Thinking for Bias Mitigation in Large Language Models | 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 perspect... | https://arxiv.org/abs/2601.15488 | Academic Papers | svg |
b11275a983228c1fc4bdbd99cba1ce0826decc568d9086b5b21380d7ddd98df2 | 2026-01-23T00:00:00-05:00 | Hybrid Vision Transformer_GAN Attribute Neutralizer for Mitigating Bias in Chest X_Ray Diagnosis | 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 attribu... | https://arxiv.org/abs/2601.15490 | Academic Papers | svg |
b8ab710d0695719c95fd4041b155887b3e1beb50b9ec6c760ab6262e427abdb1 | 2026-01-23T00:00:00-05:00 | Testing Deep Learning Libraries via Neurosymbolic Constraint Learning | 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 li... | https://arxiv.org/abs/2601.15493 | Academic Papers | svg |
6e7559a90abbd652a9e175fde48109910c5b8d26ccb7576cdf1d66c191fa7665 | 2026-01-23T00:00:00-05:00 | Tracking the Limits of Knowledge Propagation: How LLMs Fail at Multi-Step Reasoning with Conflicting Knowledge | 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 mo... | https://arxiv.org/abs/2601.15495 | Academic Papers | svg |
10843968a957829fabc95d276c21e8e93b93400b89cb8ab7bec8f0daf47456ab | 2026-01-23T00:00:00-05:00 | Semantics in Actuation Systems: From Age of Actuation to Age of Actuated Information | 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 freshne... | https://arxiv.org/abs/2601.15496 | Academic Papers | svg |
2548f23a1d1e9996d194cdd5a054105fd151abfd526d92cd4aaff8ad06868f5e | 2026-01-23T00:00:00-05:00 | MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification | 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... | https://arxiv.org/abs/2601.15498 | Academic Papers | svg |
ad93e3da8c7fd7cce43408b9b8fa51f10518508dfb0d3d0eb45391c7bacc6517 | 2026-01-23T00:00:00-05:00 | Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learning | 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... | https://arxiv.org/abs/2601.15503 | Academic Papers | svg |
c8ae64240ab7b04d2403a045a10f79a1573509013cebb485599b6aed511a5c6b | 2026-01-23T00:00:00-05:00 | SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model | 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 networ... | https://arxiv.org/abs/2601.15504 | Academic Papers | svg |
bc3f730122f8f2ca288ed93ef0178b9663097a27454d395e5b71f3f8b2893377 | 2026-01-23T00:00:00-05:00 | Stabilizer-Code Channel Transforms Beyond Repetition Codes for Improved Hashing Bounds | 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 ... | https://arxiv.org/abs/2601.15505 | Academic Papers | svg |
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