AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchThinkJev: One Thought, Every Budget for Calibrated Typed Decisions
arXiv:2610.37732v1 Announce Type: new Abstract: Many uses of language models end in a typed decision: a choice among a closed set of options, whose probabilities code can act on. Reading those probabilities from one forw…
- arXiv cs.AIresearchOpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
arXiv:2610.37739v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in research-level problem solving, yet a comprehensive collection of such problems is still missing, and evaluati…
- arXiv cs.AIagentsWebAnything: From the Wild Web to Scalable Agent Environments
arXiv:2610.37786v1 Announce Type: new Abstract: Web agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, wh…
- arXiv cs.AIagentsWhat Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs
arXiv:2610.37809v1 Announce Type: new Abstract: LLM agents that answer questions over Web knowledge graphs are increasingly wrapped in checks derived from the graph's ontology, on the assumption that a formal schema make…
- arXiv cs.AIagentsMeasuring Where Intent Gets Lost in Agentic Smart Contract Synthesis
arXiv:2610.37830v1 Announce Type: new Abstract: AI agents can now write software from a plain-English description, but code that runs is not the same as code that does what was asked. This gap matters most for smart cont…
- arXiv cs.AIagentsDetecting Covert Collusion between AI Agents through Partner-Outsider Positive Listening
arXiv:2610.37849v1 Announce Type: new Abstract: AI agents that share a secret convention can coordinate through messages that look innocent, so a monitor that reads only the messages can miss them. We propose a behaviour…
- arXiv cs.AIagentsEvaluating and improving AI agents for sequencing protocol understanding
arXiv:2610.37851v1 Announce Type: new Abstract: Existing scientific-agent benchmarks rarely evaluate whether agents can reconstruct the exact molecular structures and generation workflows specified by sequencing protocol…
- arXiv cs.AIagentsSeq@K: Evaluating Language Models Under Sequential Feedback
arXiv:2610.37859v1 Announce Type: new Abstract: Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context. This capability enables agents to iter…
- arXiv cs.AIresearchWhat Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine
arXiv:2610.37867v1 Announce Type: new Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation…
- arXiv cs.AIresearchVerification Learning Theory: A Process Model of Checking and Retention in AI-Assisted Judgment
arXiv:2610.37875v1 Announce Type: new Abstract: Accurate AI assistance can improve immediate judgments while reducing opportunities to practise the operations needed for independent performance. Verification Learning The…
- arXiv cs.AIresearchThe Anatomy of Socio-Demographics in Pluralistic Offensiveness Modeling: When and Where It Helps, and the Role of Reasoning
arXiv:2610.37896v1 Announce Type: new Abstract: Subjective judgments such as offensiveness often vary substantially across individuals, motivating pluralistic modeling approaches that incorporate annotators' socio-demogr…
- arXiv cs.AIagentsFilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision
arXiv:2610.37905v1 Announce Type: new Abstract: Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents req…
- arXiv cs.AIresearchBeyond Low-Pass Dynamics: Frequency-Selective Spiking Reservoirs with Resonant Neurons
arXiv:2610.37906v1 Announce Type: new Abstract: Spiking reservoir computing combines the training efficiency of fixed recurrent reservoirs with the energy efficiency of event-driven inference, where computation is trigge…
- arXiv cs.AIagentsSciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World
arXiv:2610.37919v1 Announce Type: new Abstract: Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix law inference with programming, score data fitting instead of p…
- arXiv cs.AIresearchVerifiedLab: Harnessing Knowns for Unknowns
arXiv:2610.37923v1 Announce Type: new Abstract: Across human history, knowledge has undergone several transitions, from orality to writing and then to digital archives. At each transition, humans established new conventi…
- arXiv cs.AIagentsGARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning
arXiv:2610.37926v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams le…
- arXiv cs.AIresearchUnderstanding Large Language Models as Numerical Optimizers
arXiv:2610.37932v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as potential tools for numerical optimization. Despite promising results, their intrinsic optimization capabilities and limitation…
- arXiv cs.AIagentsSkillMem: Compact External Skill Memory for Latent Multi-Agent Reasoning
arXiv:2610.37935v1 Announce Type: new Abstract: Latent multi-agent systems reduce inference cost by keeping intermediate reasoning and inter-agent communication in continuous representations.\ Reusable skills, however, a…
- arXiv cs.AIresearchGAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving
arXiv:2610.37989v1 Announce Type: new Abstract: Large language model (LLM) inference has become an operational workload that fleets must provision, monitor, and bill against latency service-level objectives (SLOs). A sin…
- arXiv cs.AIresearchAlgorithmic Bias from the Perspectives of Healthcare Professionals
arXiv:2610.38006v1 Announce Type: new Abstract: This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qu…
- arXiv cs.AIresearchAssessing Language Models for Scientific Applications: Inertial Confinement Fusion as a Use Case
arXiv:2610.38024v1 Announce Type: new Abstract: Advancing language models for specialized scientific applications requires evaluations of scientific fluency, physical reasoning, and quantitative analyses. Using inertial…
- arXiv cs.AIresearchPromptSense: Sentiment-Aware Mental Health Chatbot with Dynamic Prompting
arXiv:2610.38044v1 Announce Type: new Abstract: Current Large Language Models (LLMs)-based mental health chatbots often lack sufficient empathy, contextual awareness, and safety mechanisms, potentially leading to ineffec…
- arXiv cs.AIresearchEvaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation
arXiv:2610.38100v1 Announce Type: new Abstract: Large language models (LLMs) appear to represent and reason about a large range of phenomena. In particular, recent studies have examined whether LLMs can accurately reason…
- arXiv cs.AIresearchAI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization
arXiv:2610.38101v1 Announce Type: new Abstract: In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static produc…
- arXiv cs.AIresearchToo Hard, Too Easy, Just Right: Performance-Driven Challenge Adaptation in Virtual Reality Firefighter Training
arXiv:2610.38117v1 Announce Type: new Abstract: Effective firefighter training requires practice under dynamic and stressful emergency conditions, yet real-world live-fire exercises are costly, risky, and difficult to sc…
- arXiv cs.AIresearchPost-Training Large Language Models with LLMON
arXiv:2610.38120v1 Announce Type: new Abstract: Large Language Models (LLMs) treat all context tokens uniformly, lacking explicit separation between executable instructions and passive data. This leads to prompt injectio…
- arXiv cs.AIresearchDecompose, Don't Expand: Capability-Independent AI Systems by Construction
arXiv:2610.38144v1 Announce Type: new Abstract: A recurring design choice in building AI systems is whether, when a task is too hard for a model, to expand the model (make it larger, better-prompted, fine-tuned), or to d…
- arXiv cs.AIagentsSkillCT: A Multidimensional Benchmark and Execution-Free Baseline for Agent Skill Assessment
arXiv:2610.38147v1 Announce Type: new Abstract: Agent skills package instructions, scripts, and resources for specialized tasks and are increasingly distributed through public Web repositories. However, existing skill be…
- arXiv cs.AIresearchBridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios
arXiv:2610.38152v1 Announce Type: new Abstract: Lightweight representation bridges make pretrained, task-adapted EEG foundation models executable on other biosignal modalities without retraining their backbones. However,…
- arXiv cs.AIagentsDecompGym: Benchmarking LLM Agents on Designing Decomposition Algorithms
arXiv:2610.38175v1 Announce Type: new Abstract: Benders decomposition and column generation solve large optimization problems by passing cuts, columns, and dual values between a master problem and subproblems, and are ha…
- arXiv cs.AIresearchPrototype-Guided Dynamic Domain Modulation and a Large-Scale Multi-SKU Benchmark for Shampoo Bottle Surface Defect Detection
arXiv:2610.38194v1 Announce Type: new Abstract: Industrial shampoo-bottle surface defect detection must operate across many stock keeping units (SKUs), where differences in bottle geometry, material, label layout, reflec…
- arXiv cs.AIagentsFrom Natural Language to Real Schematics: An Automated Workflow for Power Electronics Schematic Synthesis
arXiv:2610.38201v1 Announce Type: new Abstract: Designing power electronics converters traditionally requires intensive human expertise across topology sizing, commercial component selection, circuit simulation verificat…
- arXiv cs.AIresearchGAAC: Lossless KV Compression for Bandwidth-Efficient LLM Inference on Personal Devices
arXiv:2610.38210v1 Announce Type: new Abstract: Weight-only, lossy quantization substantially reduces the storage and memory requirements for serving large language models (LLMs), but the growing key-value (KV) cache rem…
- arXiv cs.AIagentsDisparity Laundering: Consensus Without Fairness in Clinical Multi-Agent Systems
arXiv:2610.38215v1 Announce Type: new Abstract: Clinical large language models are increasingly fielded as multi-agent teams, on the assumption that deliberation averages out individual error. It does not average out bia…
- arXiv cs.AIresearchRubricon: Automated Rubric Generation and Refinement for LLM Evaluation and Training
arXiv:2610.38220v1 Announce Type: new Abstract: Rubrics enable reward assignment on tasks without verifiable answers, but are expensive to manually produce from scratch. We present Rubricon, a pipeline that generates dis…
- arXiv cs.AIagentsFACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence
arXiv:2610.38225v1 Announce Type: new Abstract: Real-world forecasting often requires integrating heterogeneous evidence whose predictive value varies across horizons, operating regimes, and domains. We introduce FACTS-M…
- arXiv cs.AIresearchVerification-Guided Retrosynthesis: Reaction Feedback and Evidence Timing
arXiv:2610.38245v1 Announce Type: new Abstract: Retrosynthesis planning seeks synthesis routes from purchasable starting materials to a target molecule. Search over predicted reactions can connect a target to available m…
- arXiv cs.AIresearchInner-Learning: From Hand-Coded Beliefs to Declarative Knowledge in Trick-Taking Games
arXiv:2610.38304v1 Announce Type: new Abstract: We present a declarative approach to belief-state modeling in trick-taking card games. Starting from a state-of-the-art Skat player, we automatically extract its hand-coded…
- arXiv cs.AIresearchGame-Theoretic Foundations for Undiscounted Bisimulation Metrics
arXiv:2610.38313v1 Announce Type: new Abstract: Bisimulation metrics offer a principled notion of task-relevant state similarity in reinforcement learning, but existing constructions and methods are largely confined to d…
- arXiv cs.AIresearchCHORD: Cross-Model Hallucination Detection via Relational Graph Discrimination
arXiv:2610.38314v1 Announce Type: new Abstract: Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Due to strong empirical performance, internal representation-base…
- arXiv cs.AIresearchCan LLMs Faithfully Solve Ontology Engineering Tasks? A Mechanistic Interpretability Study
arXiv:2610.38326v1 Announce Type: new Abstract: Ontological knowledge underpins large language model (LLM) performance on knowledge-intensive tasks. However, existing evaluations of LLMs' ontological understanding largel…
- arXiv cs.AIagentsInvestor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?
arXiv:2610.38342v1 Announce Type: new Abstract: Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions evaluated against subsequent outcomes. Existing benchmarks asses…
- arXiv cs.AIresearchEncode Once, Answer Many: Internalizing Context via Parametric Flow
arXiv:2610.38356v1 Announce Type: new Abstract: Large language models are typically adapted to a task either by placing knowledge in the input context, which keeps it external and re-encodes it on every call, or by fine-…
- arXiv cs.AIresearchCompute Has Shape: Self-Morphing the Internal Computation of Frozen LLMs
arXiv:2610.38382v1 Announce Type: new Abstract: Test-time scaling improves large language models through additional computation, but how to adapt the topology of internal computation to individual inputs remains underexp…
- arXiv cs.AIagentsSCOPE: Selective Critical-Step On-Policy Self-Distillation for Agentic Reinforcement Learning
arXiv:2610.38391v1 Announce Type: new Abstract: Outcome-based reinforcement learning provides coarse supervision for long-horizon LLM agents: failed trajectories may contain correct steps, while successful trajectories c…
- arXiv cs.AIagentsMasked Erosion: How Acceptance Gates Lose Redundant Safeguards in Self-Improving Agents
arXiv:2610.38392v1 Announce Type: new Abstract: Self-improving LLM agents edit their own harness, the prompts and rules around a fixed model, and keep an edit only if an acceptance gate scores it higher on a batch of tas…
- arXiv cs.AIagentsAgent Transfer Protocol: Server-Mediated Agent-to-Agent Communication Architecture for the Agentic Web
arXiv:2610.38398v1 Announce Type: new Abstract: The Agentic Web needs to support continuous collaboration among agents from different organizations. These collaborative relationships may span multiple tasks, network conn…
- arXiv cs.AIagentsGovernance-Aware Federated Learning for Multi-Agent AI Systems: A Policy-Driven Framework for Trustworthy Collaborative AI
arXiv:2610.38411v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without exchanging raw data, but conventional FL provides limited runtime governance, evidence verification, fa…
- arXiv cs.AIagentsPredict the Reward, Not the World: World Reward Models for ML Agents
arXiv:2610.38439v1 Announce Type: new Abstract: Search-based machine-learning (ML) agents improve a solution by writing candidate programs, executing them, and keeping the best. Execution gives the search reliable feedba…
- arXiv cs.AIresearchDES-JEPA: Learning a Shared Vocabulary for High-Dimensional Discrete Event Sequences
arXiv:2610.38445v1 Announce Type: new Abstract: Temporal foundation models such as Chronos and TimesFM generalize across domains through a shared numerical observation space. In contrast, discrete event sequences (DESs),…