AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchFrom Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence
arXiv:2610.20417v1 Announce Type: cross Abstract: Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of,…
- arXiv cs.AIresearchOrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning
arXiv:2610.20418v2 Announce Type: replace-cross Abstract: Tabular foundation models enable in-context learning by predicting unseen observations from a labeled support table without task-specific parameter updates. Despi…
- arXiv cs.AIresearchAnalyzing performance of Retrieval Augmented Generation models for Question Answering Tasks
arXiv:2610.20419v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) models have become almost a cornerstone of open-domain question answering tasks; in which the quality of answers generated in…
- arXiv cs.AIresearchEqual CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR
arXiv:2610.20461v1 Announce Type: cross Abstract: Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's…
- arXiv cs.AIresearchSelective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning
arXiv:2610.20463v1 Announce Type: cross Abstract: Value-based Q-learning for continuous control can discretise the action space, but the resolution of the discretisation trades early exploration against final precision.…
- arXiv cs.AIresearchQuantifying privacy risks in foundation model representations of brain MRI
arXiv:2610.20494v1 Announce Type: cross Abstract: Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patien…
- arXiv cs.AIresearchHindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
arXiv:2610.20497v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual reasoning t…
- arXiv cs.AIresearchVascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution
arXiv:2610.20509v1 Announce Type: cross Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency conten…
- arXiv cs.AIresearchVASPO: Self-Distillation with Privileged Value Estimation
arXiv:2610.20516v1 Announce Type: cross Abstract: On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rath…
- arXiv cs.AIresearchPrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
arXiv:2610.20538v1 Announce Type: cross Abstract: Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models a…
- arXiv cs.AIresearchDetection of Potato Plant Leaf Diseases using Convolutional Neural Networks
arXiv:2610.20544v1 Announce Type: cross Abstract: Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a…
- arXiv cs.AIresearchRecovering Reliable Supervision via Critique-Guided On-Policy Distillation
arXiv:2610.20572v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach a correct answer…
- arXiv cs.AIresearchContinuum Attention
arXiv:2610.20586v2 Announce Type: replace-cross Abstract: Conventional causal attention aggregates values at discrete memory positions. We introduce Continuum Attention (CA), in which neighboring key and value vectors de…
- arXiv cs.AIresearchYour Transformer Is Secretly Elastic
arXiv:2610.20588v1 Announce Type: cross Abstract: Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a fe…
- arXiv cs.AIresearchModeling and Understanding Neural Weights of Humanoid Policies with Diffusion
arXiv:2610.20606v1 Announce Type: cross Abstract: Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control netwo…
- arXiv cs.AIresearchDual-Evolving Reinforcement Learning for Automated Reward Design
arXiv:2512.13399v4 Announce Type: replace Abstract: Crafting effective reward signals remains a central challenge in reinforcement learning (RL) for complex reasoning. Existing methods for automated reward optimization s…
- arXiv cs.AIresearchLingLanMedBench: A Unified Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
arXiv:2602.01779v2 Announce Type: replace Abstract: Reliable evaluation of large language models in traditional Chinese medicine remains limited because existing benchmarks mainly focus on examination-style question answ…
- arXiv cs.AIresearchTKGB: A Comprehensive Benchmark for Temporal Knowledge Graph Forecasting
arXiv:2602.08353v2 Announce Type: replace Abstract: Temporal knowledge graph (TKG) forecasting has advanced remarkably in recent years. Yet we found existing benchmarks inadvertently introduce a shortcut: For a subset of…
- arXiv cs.AIresearchDraft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs
arXiv:2603.00578v2 Announce Type: replace Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial inference cost and overthin…
- arXiv cs.AIresearchThe Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness
arXiv:2603.09200v2 Announce Type: replace Abstract: Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason strategically about its circ…
- arXiv cs.AIresearchBenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
arXiv:2603.16557v3 Announce Type: replace Abstract: Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communicat…
- arXiv cs.AIresearchLearning to Disprove: Formal Counterexample Generation with Large Language Models
arXiv:2603.19514v2 Announce Type: replace Abstract: Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false…
- arXiv cs.AIresearchReasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models
arXiv:2603.21854v2 Announce Type: replace Abstract: Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental prog…
- arXiv cs.AIresearchStaRPO: Stability-Augmented Reinforcement Policy Optimization
arXiv:2604.08905v2 Announce Type: replace Abstract: Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely…
- arXiv cs.AIresearchReward Design for Physical Reasoning in Vision-Language Models
arXiv:2604.13993v2 Announce Type: replace Abstract: Physical reasoning over images requires integrating perception, domain knowledge, and multi-step symbolic inference, yet Vision-Language Models (VLMs) fall far short of…
- arXiv cs.AIresearchDiffTSP: A Conditional Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
arXiv:2604.18344v2 Announce Type: replace Abstract: Triple set prediction (TSP) aims to discover missing facts from an incomplete knowledge graph without predefined queries. Existing approaches typically construct the pr…
- arXiv cs.AIresearchIdeological Bias in LLMs' Economic Causal Reasoning
arXiv:2604.21334v3 Announce Type: replace Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and e…
- arXiv cs.AIresearchCORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings
arXiv:2605.10337v2 Announce Type: replace Abstract: Intracranial electrocorticography (ECoG) offers high signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient data has led mos…
- arXiv cs.AIresearchC-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
arXiv:2605.27860v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) trained with reinforcement learning (RL) is a promising way to ground large language models in medical evidence for clinical diagno…
- arXiv cs.AIresearchAUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification
arXiv:2606.03031v2 Announce Type: replace Abstract: Structured financial reporting verification requires connecting reported facts to accounting constraints, reconstructing expected values, and executing numerical checks…
- arXiv cs.AIresearchPosition: Hippocampal Explicit Memory Is the Cornerstone for AGI
arXiv:2606.11245v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence (AGI). This positi…
- arXiv cs.AIresearchMaking Transformers Logical
arXiv:2608.00418v2 Announce Type: replace Abstract: Transformers do not merely lack data on some Boolean extrapolation tasks; they generalize in a systematically wrong way. Recent work on generalization on the unseen has…
- arXiv cs.AIresearchSPAR-Hate: Auditor-Guided Multi-Perspective Role Reasoning for Bilingual Hate Speech Parsing
arXiv:2608.22018v4 Announce Type: replace Abstract: Hate speech research has moved from coarse-grained classification towards structured parsing, where systems jointly identify targets, supporting arguments, and target-l…
- arXiv cs.AIresearchRight Values, Wrong Places: Diagnosing and Fixing Record Misplacement in LLM-Generated JSON
arXiv:2608.25358v2 Announce Type: replace Abstract: Large language models can generate valid JSON that preserves field values yet binds them to the wrong records or hierarchy levels. We introduce JSONPlacement, a control…
- arXiv cs.AIresearchFaithSieve: Fine-Grained Evaluation of Math Proofs with Faithful Formal Evidence
arXiv:2608.26310v2 Announce Type: replace Abstract: Large language models can now generate complex, multi-step mathematical proofs, but reliably determining their correctness and localizing early logical errors remains a…
- arXiv cs.AIresearchLLMs Can Design Near-Optimal OR Algorithms
arXiv:2608.27296v3 Announce Type: replace Abstract: We ask whether large language models (LLMs) can design effective algorithms for well-specified operations research (OR) problems. We study inventory control, queueing n…
- arXiv cs.AIresearchSHAPE of Thinking: Mathematical Heuristics for Analyzing and Improving LLM Reasoning
arXiv:2608.28600v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet final-answer accuracy reveals little about the strategies they use. We…
- arXiv cs.AIresearchSame Request, Different Boundary: Evaluating Cybersecurity Assistance across Conversational Contexts
arXiv:2609.00578v2 Announce Type: replace Abstract: Cybersecurity assistance is dual use, requiring decisions about when to answer, limit, or refuse a request. Prior work studies refusal evaluation and conversational jai…
- arXiv cs.AIresearchWhen Do Language Models Add Value? Relative Competence in Hybrid Event Forecasting
arXiv:2609.12101v2 Announce Type: replace Abstract: When a forecasting system already has a prediction from a market, crowd, or statistical model, when should it use a language model? Standalone accuracy cannot answer th…
- arXiv cs.AIresearchBeyond Size: How Gradients Shape Pruning Decisions in Large Language Models
arXiv:2311.04902v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) with billions of parameters are prime targets for network pruning, which removes some model weights without hurting performance. Prio…
- arXiv cs.AIresearchForget About It: Mitigating Forgetting Without Replay or Auxiliary Objectives
arXiv:2501.05559v2 Announce Type: replace-cross Abstract: Have we made mitigating catastrophic forgetting more complicated than it needs to be? Continual adaptation often degrades performance on earlier tasks, motivating…
- arXiv cs.AIresearchVideo Depth Anything: Consistent Depth Estimation for Super-Long Videos
arXiv:2501.12375v4 Announce Type: replace-cross Abstract: Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency i…
- arXiv cs.AIresearchTrustworthy AI Must Account for Interactions
arXiv:2504.07170v3 Announce Type: replace-cross Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncer…
- arXiv cs.AIresearchSynergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks
arXiv:2505.03380v3 Announce Type: replace-cross Abstract: Accurate segmentation of tumors and organs-at-risk is essential for oncology but remains time-consuming and expertise-intensive. Existing AI systems often require…
- arXiv cs.AIresearchRule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
arXiv:2506.06297v2 Announce Type: replace-cross Abstract: Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variabl…
- arXiv cs.AIresearchIs This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility
arXiv:2507.12553v4 Announce Type: replace-cross Abstract: Language models (LMs) are used for a diverse range of tasks, from question answering to writing fantastical stories. In order to reliably accomplish these tasks,…
- arXiv cs.AIresearchThe Invisible Leash: Why RLVR May or May Not Escape Its Origin
arXiv:2507.14843v5 Announce Type: replace-cross Abstract: Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However, it remains unclear…
- arXiv cs.AIresearchAlign Where the Words Look: Cross-Attention-Guided Patch Alignment with Contrastive and Transport Regularization for Bengali Captioning
arXiv:2509.18369v2 Announce Type: replace-cross Abstract: Adapting vision-language models in low-resource languages remains challenging, as they often produce fluent text about the wrong objects. This stems from scarce p…
- arXiv cs.AIresearchAnticipatory Evaluation of Language Models: Predicting LLM Benchmark Scores from Descriptions
arXiv:2509.20645v4 Announce Type: replace-cross Abstract: Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how much can be i…
- arXiv cs.AIresearchContext Value Informed In-Context Reinforcement Learning
arXiv:2509.23209v2 Announce Type: replace-cross Abstract: In-context reinforcement learning (ICRL) has emerged as an effective paradigm for test time adaptation to unseen tasks without parameter updates. However, existin…