AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- Engadget AIpolicyDutch government to cut 16 percent of workforce as part of AI efficiency push
The cabinet announced that it wants to get rid of 25,000 full-time jobs by 2035.
- Guardian AIcommunity'Dear ChatGPT': 12-year-old wins top youth prize at Australian Poetry Slam – video
Blake Mackenzie, a 12-year-old from Newcastle, has won the top youth prize at the Australian Poetry Slam for his reflections on artificial intelligence. The winning piece, performed at the Sydney Opera House on Sunday ni…
- LessWrongresearchA cat-belling problem for FDT
[AI disclosure: I used Opus 5.5 for light edits after my first draft] Functional decision theory (Yudkowsky and Soares 2017) proposes choosing actions as follows (equation 4 in the paper): mjx-container[jax="CHTML"] { li…
- arXiv cs.AIresearchTradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation
arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organi…
- arXiv cs.AIagentsPhronix: The Capability-Centric Learning Paradigm for Agents
arXiv:2610.31789v1 Announce Type: new Abstract: Scientific computing increasingly relies on large language model-based agents for performance engineering tasks such as instrumentation, analysis, and workflow porting. How…
- arXiv cs.AIresearchPatronizing and Condescending Language Detection in English and Mandarin
arXiv:2610.31808v1 Announce Type: new Abstract: Patronizing and condescending language (PCL) is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes.…
- arXiv cs.AIagentsPaperFormalizer: Author-in-the-Loop Autoformalization of Research Papers in Lean
arXiv:2610.31810v2 Announce Type: replace Abstract: Even a carefully proofread paper can leave small gaps in its proofs or its stated assumptions. We introduce PaperFormalizer, a Lean-based agentic pipeline that helps au…
- arXiv cs.AIagentsDon't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons
arXiv:2610.31846v1 Announce Type: new Abstract: Intelligent agents should learn from their mistakes so that experience leads to continuous improvement. Yet today's agents recover from a mistake only temporarily: in-conte…
- arXiv cs.AIagentsTACS-RF: Tool-driven Agentic Circuit Synthesis of RF and mmWave Modules
arXiv:2610.31882v1 Announce Type: new Abstract: We present the framework of TACS-RF - a Tool-driven Agentic Circuit Synthesis of RF and mmWave modules. In this framework, a large-language-model (LLM) coding agent (Claude…
- arXiv cs.AIagentsPermission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
arXiv:2610.31916v1 Announce Type: new Abstract: A durable agent can resume a consequential action while its authority changes and the action's external outcome remains uncertain. We study execution-time reliability of to…
- arXiv cs.AIagentsBeyond On-Policy vs. Off-Policy: At What Level Should LLM Agent Distillation Follow the Student?
arXiv:2610.31918v1 Announce Type: new Abstract: Distilling interactive LLM agents requires choosing between teacher-generated demonstrations and student-generated experience, corresponding broadly to off-policy and on-po…
- arXiv cs.AIresearchReject, Remember, Retry: Why Multi-Turn Attackers Should Remember Their Mistakes
arXiv:2610.31937v2 Announce Type: replace Abstract: Existing multi-turn jailbreak attackers condition on the same conversation history as the target. We introduce $R^3$ (Reject, Remember, Retry), a black-box multi-LLM fr…
- arXiv cs.AIagentsKnowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation
arXiv:2610.31971v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particula…
- arXiv cs.AIagentsReproducible Evaluation as the Substrate for Safe Prompt Compression in Production Agents
arXiv:2610.31972v1 Announce Type: new Abstract: Evaluating conversational agents that act on live, mutable enterprise data is hard: replayed sessions drift as backend state changes, and a benchmark built today can silent…
- arXiv cs.AIagentsThe Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
arXiv:2610.31985v1 Announce Type: new Abstract: LLM agents are increasingly deployed in collaborative settings where they pursue separate tasks while sharing resources and operating under review or oversight processes. S…
- arXiv cs.AIagentsCEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition
arXiv:2610.32022v1 Announce Type: new Abstract: Intent recognition in human-robot interaction requires agents to resolve ambiguous natural language instructions by inferring the speaker's underlying goals. Research has i…
- arXiv cs.AIagentsBARC-Bench: Benign Asks, Risky Consequences
arXiv:2610.32024v1 Announce Type: new Abstract: Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks. To study these risks, we introduce…
- arXiv cs.AIagentsRSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training
arXiv:2610.32026v1 Announce Type: new Abstract: Conventional neural-network training typically follows a predefined optimization recipe, motivating a LLM agent that monitors training dynamics and adjusts optimizer settin…
- arXiv cs.AIresearchPPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots
arXiv:2610.32034v1 Announce Type: new Abstract: The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved gras…
- arXiv cs.AIagentsDo AI-for-Science Agents Preserve Model Safety?
arXiv:2610.32045v1 Announce Type: new Abstract: AI-for-Science agents are increasingly being used for formulating scientific hypotheses, running autonomous experiments, and interpreting evidence. These agents are constru…
- arXiv cs.AIresearchCan Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery?
arXiv:2610.32046v1 Announce Type: new Abstract: Recent \emph{test-time training} (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models o…
- arXiv cs.AIresearchHyScale: Scaling Long-Context Training for Learned Sparse Attention
arXiv:2610.32057v2 Announce Type: replace Abstract: We present HyScale, a long-context training system for learned sparse attention. Learned sparse attention is a promising architecture for million-token contexts, widely…
- arXiv cs.AIagentsUnderstanding Communication Failures in Multi-Agent LLM Systems
arXiv:2610.32060v1 Announce Type: new Abstract: Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy al…
- arXiv cs.AIagentsDriveMind: Mind-Evolving Belief Tracking for Closed-Loop Autonomous Driving
arXiv:2610.32069v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) can perceive a traffic scene, reason about it, and propose a driving action within a single model. However, existing MLLM-ba…
- arXiv cs.AIagentsBehaviour analysis for large language model-driven agents
arXiv:2610.32087v1 Announce Type: new Abstract: Understanding language-agent behaviour requires learning how actions are organised across a trajectory and how this organisation relates to context and outcomes. We propose…
- arXiv cs.AIresearchDELA: Learning Controllable Edits for Diffusion Language Models
arXiv:2610.32096v1 Announce Type: new Abstract: Diffusion language models (DLMs) support bidirectional denoising and iterative refinement, but generation remains largely controlled by token-level masking schedules. This…
- arXiv cs.AIresearchLearning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and Communication
arXiv:2610.32099v1 Announce Type: new Abstract: Fixed source superposition and frequency partitioning constrain how communication content interacts with a probing reference in integrated sensing and communication (ISAC).…
- arXiv cs.AIagentsPersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers
arXiv:2610.32106v1 Announce Type: new Abstract: Scientific agents collaborate with researchers whose expertise and preferences differ, but benchmarks usually hold the collaborator fixed. We introduce PersonaScienceBench:…
- arXiv cs.AIagentsWho Verifies the Tools? How Warped Formatters Mislead Agents and Their Verifiers
arXiv:2610.32110v1 Announce Type: new Abstract: Agents and verifiers often rely on the text a tool prints, and that text can misrepresent what the tool actually did, even when the tool's computation is correct. We saw th…
- arXiv cs.AIresearchMPQ: A Message-Passing View of Post-Training Quantization
arXiv:2610.32127v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a key technique for efficiently deploying large language models, as it compresses weights into low-bit representations without retrainin…
- arXiv cs.AIresearch$\Psi$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model
arXiv:2610.32177v1 Announce Type: new Abstract: Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI:…
- arXiv cs.AIresearchNot All Information Is Worth Asking For: Decision-Informed Information Seeking with LLMs
arXiv:2610.32181v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the…
- arXiv cs.AIresearchEvidence-Grounded SLM Behavioral Prediction Architecture for Future Assistance Use
arXiv:2610.32193v1 Announce Type: new Abstract: Users often exhibit changes in interaction behavior before explicitly seeking assistance, enabling systems to anticipate future assistance use. This is especially important…
- arXiv cs.AIagentsSWE-Shift: Evaluating Coding Agents in Evolving Workspaces
arXiv:2610.32196v1 Announce Type: new Abstract: Repository-level coding benchmarks largely assume that tasks and environments remain unchanged throughout execution. In software development, code, dependencies, runtime st…
- arXiv cs.AIresearchWatermarking Diffusion Language Models with Learned Foresight and Balanced Bands
arXiv:2610.32205v1 Announce Type: new Abstract: Green-red watermarks favor tokens from a green list that a secret key derives from the preceding tokens. Diffusion language models, however, often choose a token before its…
- arXiv cs.AIresearchPEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language Models
arXiv:2610.32216v1 Announce Type: new Abstract: Effective multimodal reasoning requires a model to reliably utilize visual information while sufficiently exploring diverse reasoning paths. To achieve this goal, prior wor…
- arXiv cs.AIresearchRxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature
arXiv:2610.32221v1 Announce Type: new Abstract: Screen Tables and Substrate Scopes are information-dense figures widely used in synthetic chemistry literature, yet automated extraction of structured reaction data from th…
- arXiv cs.AIagentsCorrect Answers, Closed Gates: SolveBind for GUI Agents at the Verification Barrier
arXiv:2610.32223v1 Announce Type: new Abstract: Websites and applications deploy CAPTCHAs and anti-automation mechanisms that use behavioral signals such as interaction trajectories and timing, not outcomes alone, to dis…
- arXiv cs.AIresearchTraining an MoE LLM in MXFP4: How We Closed the Gap to BF16
arXiv:2610.32229v2 Announce Type: replace Abstract: Training large language models (LLMs) in sub-8-bit arithmetic promises substantial gains in memory bandwidth and compute throughput, yet FP4 formats introduce quantizat…
- arXiv cs.AIagentsFinancial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading
arXiv:2610.32237v1 Announce Type: new Abstract: In modern financial markets, more than 3/4th of the large volume trade is executed by algorithmic trading based on advanced trading strategies to attain higher precision an…
- arXiv cs.AIresearchSpatial Heuristic Strategy based Diffusion Reinforcement Learning for Online 3D Bin Packing
arXiv:2610.32241v1 Announce Type: new Abstract: Traditional heuristic methods have accumulated mature experience in core objectives, such as space utilization and layout compactness of 3D bin packing, relying on artifici…
- arXiv cs.AIagentsMinimum Description Length Unsupervisedly Separates Law-Grade from Statistical-Grade Knowledge in a Self-Improving Science Agent - and Reveals 86% of Its Own Discoveries Are Statistical
arXiv:2610.32245v1 Announce Type: new Abstract: AI systems that discover scientific relations now generate candidate "laws" at scale, but the bottleneck has shifted from generation to vetting: which candidates deserve be…
- arXiv cs.AIagentsRSIHub: A Composable Framework for Agent-Evolution Methods
arXiv:2610.32253v1 Announce Type: new Abstract: Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery…
- arXiv cs.AIagentsMedCaseAgent: From Multimodal Clinical Evidence to Rare Disease Case Reports
arXiv:2610.32258v1 Announce Type: new Abstract: Although Large Language Models (LLMs) have demonstrated potential in clinical documentation, generating the rigorous, evidence-based reasoning required for unprecedented, a…
- arXiv cs.AIagentsTrial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure Attribution
arXiv:2610.32285v1 Announce Type: new Abstract: LLM agents are increasingly used to solve real-world problems, and their effectiveness depends on both the underlying models and the harnesses that support their operation.…
- arXiv cs.AIagentsLifelong Context Adaptation through Bidirectional Memory-Skill Learning
arXiv:2610.32295v1 Announce Type: new Abstract: Lifelong LLM agents must learn from an expanding history of interactions while making each decision within a bounded context window. Memory makes past experience available,…
- arXiv cs.AIagentsDMAIC-Bench: From Self-Reflection to Self-Restraint for Agents in Multi-Stage Analytical Tasks
arXiv:2610.32305v1 Announce Type: new Abstract: Multi-stage analytical tasks require LLM agents to use intermediate evidence to decide whether to continue, redirect or stop. Self-reflection, in which an agent critiques i…
- arXiv cs.AIresearchSolvent: Long-Horizon Reasoning for Multi-Step Retrosynthesis
arXiv:2610.32308v1 Announce Type: new Abstract: Multi-step retrosynthesis requires recursively transforming a target molecule through successive reaction steps into purchasable building blocks, and recent work has begun…
- arXiv cs.AIagentsBeyond Gain: Measuring Skill Internalization for Agent Skill Retirement
arXiv:2610.32322v1 Announce Type: new Abstract: LLM agents retrieve skills from public hubs that hold up to about 100,000 entries and serve many models. As models improve, a hub must find the skills a given model has int…
- arXiv cs.AIresearchFrom Visual Affinity to Learning Credit in Multimodal Reinforcement Learning
arXiv:2610.32324v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves multimodal reasoning, but uniformly assigning a single response-level advantage to all tokens overlooks the d…