AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBenchmarking System One Models for Time-Series Decision Making: A Case Study of Jev
arXiv:2610.33410v1 Announce Type: new Abstract: Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly output answers and t…
- arXiv cs.AIagentsConMem: Counterfactual Multi-Scenario Probing for Resolving LLM Memory Misalignment
arXiv:2610.33432v1 Announce Type: new Abstract: Long-term memory is critical for autonomous Large Language Model (LLM) agents executing multi-session, long-horizon tasks. However, existing memories often inappropriately…
- arXiv cs.AIresearchBoardSafety: AI-Ready Data from Manufacturer Documentation for Development-Board Safety
arXiv:2610.33448v1 Announce Type: new Abstract: The development of large language models (LLMs) are capable to assist with hardware experiments. In this scenario, this raises a practical question: Can LLMs determine whet…
- arXiv cs.AIresearchA Comprehensive Framework for All Stages of Large-Model Training: From Data and Tokenization, through Pretraining, to Post-Training Alignment
arXiv:2610.33451v1 Announce Type: new Abstract: The capabilities of large language models (LLMs) and multimodal LLMs (MLLMs) arise from the synergy of the full training pipeline, including data and tokenization, pretrain…
- arXiv cs.AIagentsLegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery
arXiv:2610.33465v2 Announce Type: replace Abstract: Equation discovery solvers often use different data interfaces, equation representations and evaluation criteria, making their results hard to compare. We present LegoS…
- arXiv cs.AIresearchThe Future Is Finer: Multivariate Time Series Super-Resolution Forecasting
arXiv:2610.33470v1 Announce Type: new Abstract: Web platforms run on multivariate time series, from service telemetry and Web traffic logs to sensor streams served through Web APIs. Yet, retention policies often archive…
- arXiv cs.AIagentsSkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents
arXiv:2610.33528v1 Announce Type: new Abstract: Skill learning enables web agents to reuse prior experience for long-horizon interactive tasks. However, learned skills are typically evaluated in closed environments, over…
- arXiv cs.AIagentsFraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy
arXiv:2610.33536v1 Announce Type: new Abstract: Financial fraud caused over \$15.9 billion in losses in the United States in 2025, according to the Federal Trade Commission~\citep{ftc2026databook}. Understanding such lar…
- arXiv cs.AIagentsCoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems
arXiv:2610.33539v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration architectures often…
- arXiv cs.AIagentsLearning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing
arXiv:2610.33544v1 Announce Type: new Abstract: Multi-agent systems have emerged as a promising paradigm for multimodal medical reasoning, but existing methods rely on two assumptions that hinder practical deployment: (i…
- arXiv cs.AIresearchAuditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
arXiv:2610.33592v1 Announce Type: new Abstract: Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility remains unproven. A re…
- arXiv cs.AIresearchCounterfactual Model Testing Under Distribution Shift
arXiv:2610.33594v1 Announce Type: new Abstract: Deployed machine learning (ML) models can degrade as the distribution of their input data shifts. Such shifts can result from changes in the population or from data integri…
- arXiv cs.AIagentsData Governance for Generative and Agentic AI : Technology, Law, Economics, and Security
arXiv:2610.33605v1 Announce Type: new Abstract: This book develops a unified governance framework for systems that retrieve, generate, infer, remember and act on information from large language models and retrieval augme…
- arXiv cs.AIresearchWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems
arXiv:2610.33617v1 Announce Type: new Abstract: Large language models (LLMs) increasingly produce online articles from material that later becomes input to other models. This circulation connects individual responses to…
- arXiv cs.AIresearchGraph-dependent pooling: adaptive granularity in hierarchical graph neural networks
arXiv:2610.33623v1 Announce Type: new Abstract: Graph Neural Networks are widely used to learn structural representations of relational data. Hierarchical graph pooling learns multi-level representations by progressively…
- arXiv cs.AIresearchRAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches
arXiv:2610.33625v1 Announce Type: new Abstract: Multi-Head Latent Attention (MLA) has been adopted by recent frontier large language models. Instead of per-head keys and values, MLA caches a compressed KV latent that is…
- arXiv cs.AIagentsAlphaIdeaBench: Benchmarking Automated Research Idea Generation under Feasibility Constraints with Peer Review Alignment
arXiv:2610.33644v1 Announce Type: new Abstract: Research ideas are always proposed under resource constraints: a plan must fit the available compute, API credit, and time. Yet evaluations of research-ideation agents most…
- arXiv cs.AIresearchTAD: Distilling Vision-Language-Action Policies from Teacher Trajectories Alone
arXiv:2610.33650v1 Announce Type: new Abstract: Online distillation of vision-language-action (VLA) policies commonly relies on teacher action scores that an inference-only interface may not expose. Trajectory-only Adver…
- arXiv cs.AIagentsJointly Modeling Preferences and Imperfect Decision-Making in Large Language Models
arXiv:2610.33651v1 Announce Type: new Abstract: Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic behaviour? LLM agents interacting with humans and other AI systems…
- arXiv cs.AIresearchPhysics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
arXiv:2610.33654v1 Announce Type: new Abstract: High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution network…
- arXiv cs.AIresearchPower-of-Two Leaky Integrate-and-Fire Neurons for Event-Driven Vision-Language-Action Inference
arXiv:2610.33658v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on dense, continuous-valued computation presents a fun…
- arXiv cs.AIagentsSWE-Ladder: Learning to Localize, Reproduce, and Patch via Progressive Privileged Self-Distillation
arXiv:2610.33667v1 Announce Type: new Abstract: Resolving a repository-level issue typically requires a software-engineering (SWE) agent to localize the faulty code, reproduce the failure, and patch the code. Reinforceme…
- arXiv cs.AIresearchMeta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference
arXiv:2610.33674v1 Announce Type: new Abstract: Medical vision--language pretraining learns transferable representations from paired images and radiology reports, yet differences in scanners and acquisition protocols can…
- arXiv cs.AIagentsMore: Mode-Optimized Reasoning with Self-Adaptive Effort for Self-Evolving Agents
arXiv:2610.33684v1 Announce Type: new Abstract: Self-evolving training improves the performance of agents on difficult tasks through verifiable feedback, but it often applies costly deliberation uniformly across tasks. T…
- arXiv cs.AIagentsPACE: Research Proving Agent for Adaptive Conjecture Exploration
arXiv:2610.33690v1 Announce Type: new Abstract: Many mathematical research agents focus on solving problems with fixed statements. However, conjecture research, especially in applied mathematics, may require revising can…
- arXiv cs.AIagentsSeeing the Options: Typed Decisions over Visual Candidates with Reusable Option Memory
arXiv:2610.33702v2 Announce Type: replace Abstract: Agents end many steps in a typed decision over a set of candidates: which screen element to click, which product to pick. System One models answer in one forward pass,…
- arXiv cs.AIagentsHuman-Feedback-Guided Multi-Agent Inverse Reinforcement Learning for Interactive Driving Behavior Modeling
arXiv:2610.33726v1 Announce Type: new Abstract: Modeling human driving behavior is fundamental to understanding driver decision-making and supporting autonomous vehicles (AVs) in mixed traffic. Reward learning offers a p…
- arXiv cs.AIresearchAll the Facts, Still the Wrong Answer: Integration Gaps in Vision-Language Models
arXiv:2610.33764v1 Announce Type: new Abstract: Multimodal reasoning requires models to combine multiple pieces of evidence into decisions that satisfy all relevant constraints. Evaluations centered on final-answer accur…
- arXiv cs.AIresearchLocal Entropy Fluctuations for Black-Box LLM-Generated Text Detection
arXiv:2610.33767v2 Announce Type: replace Abstract: Zero-shot detection of machine-generated text is vital for mitigating the risks of large language models (LLMs) misuse. While the state-of-the-art dual-model detectors…
- arXiv cs.AIresearchAn actively trained neural operator for near-explicit atmospheric chemistry
arXiv:2610.33771v2 Announce Type: replace Abstract: Earth-system models simplify atmospheric chemistry into condensed mechanisms, because near-explicit mechanisms such as the Master Chemical Mechanism (MCM) are too costl…
- arXiv cs.AIresearchThe Hitchhiker's Guide to System 1 Models: Fast Decision-Making Beyond LLMS
arXiv:2610.33775v2 Announce Type: replace Abstract: Large Language Models excel at reasoning and generation but their autoregressive, token-by-token inference makes them unnecessarily slow, expensive, and poorly calibrat…
- arXiv cs.AIresearchInfiniteWorld: Separating Visual Evidence from Familiar Physics
arXiv:2610.33777v1 Announce Type: new Abstract: Physical reasoning requires adapting familiar expectations to the dynamics of the observed world. Existing benchmarks establish broad physical capabilities, yet an overall…
- arXiv cs.AIresearchUnlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning
arXiv:2610.33788v1 Announce Type: new Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. However, existing approaches to universal PPG representation le…
- arXiv cs.AIresearchAre We Done with UMLS Injection into Language Models for Medical Question Answering?
arXiv:2610.33801v1 Announce Type: new Abstract: Maybe not. The Unified Medical Language System (UMLS) has become an oracle for millions of clinicians and health system developers worldwide. Researchers have increasingly…
- arXiv cs.AIresearchBeyond Static Structures: Linking Protein Dynamics to Function via DynaReason-Pro
arXiv:2610.33804v1 Announce Type: new Abstract: Intrinsically disordered proteins (IDPs) carry out their functions through heterogeneous conformational ensembles that reflect their dynamics, yet most protein function pre…
- arXiv cs.AIresearchRectified Language Flow
arXiv:2610.33811v1 Announce Type: new Abstract: Continuous-state flow and diffusion models offer a promising approach to fast, parallel language generation, but matching the generation quality of autoregressive models re…
- arXiv cs.AIagentsEnvLunch: The Environment as a Free Lunch for Learning to Interact
arXiv:2610.33817v1 Announce Type: new Abstract: For agentic data analysis, language-model agents often require a large number of annotated tasks to learn how to interact effectively with executable environments (i.e., da…
- arXiv cs.AIagentsBuild, Don't Play: Self-Improving LLM Research Agents Develop a Hearthstone Battlegrounds AI That Rivals Top Players
arXiv:2610.33845v1 Announce Type: new Abstract: Can LLM agents carry a hard real-world problem from raw data to a system that holds its own against people? We study this in Hearthstone Battlegrounds, an eight-player auto…
- arXiv cs.AIagentsNumina Fuse: Interactive Autoformalization in Lean 4
arXiv:2610.33860v1 Announce Type: new Abstract: Numina Fuse is an agent-native application for formalizing mathematics in Lean 4. Since Lean can compile while not representing its intended informal meaning, and agents no…
- arXiv cs.AIagentsReGE: Stronger Agents through Experience Synthesis
arXiv:2610.33861v1 Announce Type: new Abstract: Repeated attempts at interactive tasks uncover facts, useful operations, and failures that can inform subsequent execution. We introduce ReGE, a framework for turning this…
- arXiv cs.AIagentsOS-Wizard: Scaling Computer-Using Agents to Specialized Software via Knowledge-Guided Exploration
arXiv:2610.33907v1 Announce Type: new Abstract: Computer-using agents are increasingly capable of performing complex tasks in the digital world. However, high-quality interaction trajectories remain scarce for specialize…
- arXiv cs.AIresearchWebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
arXiv:2610.33929v1 Announce Type: new Abstract: Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-language instructions…
- arXiv cs.AIresearchFed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems
arXiv:2610.34009v1 Announce Type: new Abstract: Conventional federated learners implicitly optimize for global means, creating a "one-size-fits-all" paradigm that inevitably suppresses the minority under cross-site heter…
- arXiv cs.AIresearchFrom Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
arXiv:2610.34094v1 Announce Type: new Abstract: Large language models (LLMs) generally lack deep causal reasoning capabilities in temporal reasoning. Inspired by document layout reordering, this paper proposes a paradigm…
- arXiv cs.AIagentsCan LLMs Trust Appropriately? Evaluating Calibrated Reliance on Imperfect Partners
arXiv:2610.34107v1 Announce Type: new Abstract: Large language models increasingly collaborate with humans and peer AI agents whose contributions may be incomplete, inconsistent, or incorrect. Effective collaboration req…
- arXiv cs.AIagentsTask Success Is Not Enough: Side-effect-Aware Evaluation of Tool-Using Language Model Agents
arXiv:2610.34167v1 Announce Type: new Abstract: Tool-using language model agents are usually evaluated by whether they finish the user's task. That metric misses a common failure: an agent can complete the requested work…
- arXiv cs.AIagentsR2A-Sales: A Policy-Atom-Matched Benchmark for Rule-to-Action Gaps in Goal-Directed Sales Agents
arXiv:2610.34185v1 Announce Type: new Abstract: LLM-based sales agents are often evaluated on static policy questions or task success, but deployment asks a sharper question: when a model can state a rule, will it choose…
- arXiv cs.AIagentsMatOS: An Agent Operating System for Materials Research
arXiv:2610.34203v1 Announce Type: new Abstract: Materials research relies on scattered and growing data, knowledge, and simulation tools, and a single study often spans multi-day workflows across literature analysis, com…
- arXiv cs.AIresearchVariational Cognitive Memory Abstraction for Long-Horizon Vision-Language-Action Models
arXiv:2610.34251v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable success in robotic manipulation, yet their ability to execute long-horizon tasks remains limited by insufficient l…
- arXiv cs.AIagentsMultiEvoSkill: Structure-Aware Multi-Agent Skill Evolution for Generalizable Skill Artifacts
arXiv:2610.34260v1 Announce Type: new Abstract: Skill documents are becoming the default adaptation surface for frozen large language model (LLM) agents, but current skill generators face two key limitations. First, huma…