AI news archive.
4,959 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsPOLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents
arXiv:2610.08082v1 Announce Type: new Abstract: LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors manifest. Existing pr…
- arXiv cs.AIagentsBeyond Corrected Memory: Execution Consistency in Multi-Agent Systems
arXiv:2610.08101v1 Announce Type: new Abstract: Shared memory coordinates agents' actions, but correct records do not establish that those actions satisfy task requirements. Memory governance and failure diagnosis regula…
- arXiv cs.AIagentsDSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents
arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management and business operati…
- arXiv cs.AIagentsChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications
arXiv:2610.08106v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) advance rapidly, while conventional benchmark development lags behind, delaying investigation of newly observed capability gaps. Su…
- arXiv cs.AIagentsTest-Time Agent Evolution for Long-Horizon Legal Reasoning
arXiv:2610.08138v1 Announce Type: new Abstract: Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. However, real-world legal…
- arXiv cs.AIagentsMathematical Proof Assistants for Teaching Logic: The LogiKEy Methodology
arXiv:2610.08214v1 Announce Type: new Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic LogiKEy methodology, u…
- arXiv cs.AIagentsLearn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?
arXiv:2610.08215v2 Announce Type: replace Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for understanding h…
- arXiv cs.AIagentsMASC: A Multi-Agent Self-Calibration Framework with Latent Construct Alignment for Consistent Client Role-Playing in Psychological Counseling
arXiv:2610.08250v1 Announce Type: new Abstract: Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation requires clients to re…
- arXiv cs.AIagentsSCOPE: Certified Theorem Proving with a Language Model as the Policy Planner
arXiv:2610.08319v2 Announce Type: replace Abstract: In proof assistants such as Lean, a generated proof must pass machine compilation checks, so evaluation needs no human scoring. Direct generation fails on multi-step nu…
- arXiv cs.AIagentsMedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation
arXiv:2610.08327v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in medical question answering and clinical reasoning, yet their improvement remains constrained by static parametric kno…
- arXiv cs.AIagentsTransect: Retaining Observability for Long-Horizon LLM Agent Evaluations
arXiv:2610.08364v2 Announce Type: replace Abstract: Frontier AI evaluations increasingly use open-ended, agentic, long-horizon tasks whose transcripts can span hundreds of pages of outputs and actions from complex multi-…
- arXiv cs.AIagentsEMHO: EMbodied Agent Harness Optimization via Experience Traces
arXiv:2610.08432v1 Announce Type: new Abstract: Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning, context, and tool u…
- arXiv cs.AIagentsHow Much Evidence Should a Coding Agent's Self-Correction Carry? Adaptive Dirichlet Evidence for Self-Distillation
arXiv:2610.08514v1 Announce Type: new Abstract: Execution feedback lets coding agents revise programs and learn from their own corrections. A correction's learning weight should reflect both the transitions supported by…
- arXiv cs.AIagentsMINDSET: Energy-based Schema Evolution for Long Conversational Agent Memory
arXiv:2610.08586v1 Announce Type: new Abstract: Long conversational agents have become essential in our daily lives. They must remember what was said long back in order to help us efficiently complete a task without need…
- arXiv cs.AIagentsRecursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness
arXiv:2610.08621v2 Announce Type: replace Abstract: Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experience for players.…
- arXiv cs.AIagentsSquidAgent: Parallelize Wisely, Coordinate Efficiently
arXiv:2610.08647v1 Announce Type: new Abstract: LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency. In principle, parallelizing work across multiple agents should yield n…
- arXiv cs.AIagentsParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding
arXiv:2610.08662v3 Announce Type: replace Abstract: As coding agents increasingly undertake real-world work autonomously, judging whether their risk treatments are warranted has become important. Existing work evaluates…
- arXiv cs.AIagentsCoupled but Late: Turn-Taking Between Full-Duplex Speech Models in Unscripted Dialogue
arXiv:2610.08683v1 Announce Type: new Abstract: Full-duplex speech models are trained to converse with a person, but they are increasingly made to converse with each other, in self-play data generation, agent societies,…
- arXiv cs.AIagentsScienceClaw: Benchmarking Continual Self-Evolution of AI-for-Science Agents Across the Natural and Social Sciences
arXiv:2610.08691v1 Announce Type: new Abstract: Large language model agents are accelerating scientific automation, yet verified executions rarely become persistent program-level improvements, and existing evaluations do…
- arXiv cs.AIagentsnanoMuse: An Open-Source Personal Agent for Every Device You Own
arXiv:2610.08699v1 Announce Type: new Abstract: Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices,…
- arXiv cs.AIagentsWorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?
arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical testbed for reproduc…
- arXiv cs.AIagentsDoes an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus
arXiv:2610.08722v1 Announce Type: new Abstract: Many long-horizon agents compact their context on a global rule, usually a token budget, blind to what the agent was doing. We ask whether the agent's recent behaviour pred…
- arXiv cs.AIagentsAgent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?
arXiv:2610.08775v1 Announce Type: new Abstract: Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive. Can LLM agents auto…
- arXiv cs.AIagentsVerdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?
arXiv:2610.06962v1 Announce Type: cross Abstract: In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than recording the decision…
- arXiv cs.AIagentsAPEX: Active Protection at Execution Boundaries for LLM Agents
arXiv:2610.06966v2 Announce Type: replace-cross Abstract: Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose heterogeneous…
- arXiv cs.AIagentsSchemaFill: Efficient LLM Tool Calling via Slot-Parallel Speculative Decoding
arXiv:2610.07086v1 Announce Type: cross Abstract: LLM agents interact with external systems by generating structured tool calls. Given a user request, conversational context, and a catalog of tool schemas, a tool-calling…
- arXiv cs.AIagentsTowards a Unified Misuse Monitoring Benchmark
arXiv:2610.07089v1 Announce Type: cross Abstract: LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request is split into innoc…
- arXiv cs.AIagentsAgentic Design Space Exploration for Joint Hardware Configuration Selection and Mapping of AI Inference Workloads on Heterogeneous Edge SoCs
arXiv:2610.07191v1 Announce Type: cross Abstract: Modern edge Systems-on-Chip (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, each with distinct performance and energy characteristics.…
- arXiv cs.AIagentsSPEAR: Five Principles for Interactive Human-Agent Alignment
arXiv:2610.07204v1 Announce Type: cross Abstract: Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, finetune the model, and…
- arXiv cs.AIagentsLineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents
arXiv:2610.07258v1 Announce Type: cross Abstract: Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, a…
- arXiv cs.AIagentsFlexiFlow: Bandit-based Model Switching in ML Workflows
arXiv:2610.07286v1 Announce Type: cross Abstract: Model optimizations help improve inference performance and accuracy of ML workflows. However, relying on a single model to perform inference across all data batches often…
- arXiv cs.AIagentsCatching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale
arXiv:2610.07289v1 Announce Type: cross Abstract: Manual repair of program failures is time-consuming and disruptive for software developers, particularly during the pre-submit phase where test failures occur within cont…
- arXiv cs.AIagentsSelective Critique for Cost-Aware LLM Agents in Long-Horizon Decision Making
arXiv:2610.07335v1 Announce Type: cross Abstract: Improving the reliability of large language model (LLM) agents in long-horizon decision-making remains a key challenge. When deployed as autonomous agents interacting wit…
- arXiv cs.AIagentsRELACE: retrospective likelihood-based action credit estimation for long-horizon language agents
arXiv:2610.07349v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) avoids a separate critic by estimating advantages from rollout groups. For multi-turn agents, however, trajectory-level supervis…
- arXiv cs.AIagentsWho Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learning
arXiv:2610.07491v1 Announce Type: cross Abstract: When multiple agents share a cost budget, a common Lagrange multiplier can enforce the aggregate constraint but does not determine how its penalty should be allocated acr…
- arXiv cs.AIagentsJarvis: A Proactive Speech Agent for Multi-Party Conversations
arXiv:2610.07506v2 Announce Type: replace-cross Abstract: Speech agents are reactive and dyadic: they speak when spoken to, and to one person at a time. We ask what it takes for a speech agent to instead take part in a c…
- arXiv cs.AIagentsDisentangling Models from Personas in Heterogeneous LLM Simulations
arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may domi…
- arXiv cs.AIagentsFoundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
arXiv:2610.07550v1 Announce Type: cross Abstract: Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from multiple terminals. W…
- arXiv cs.AIagentsCheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?
arXiv:2610.07557v1 Announce Type: cross Abstract: Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and refine the checker thr…
- arXiv cs.AIagentsStateless Language Agents: Scaling Long-Horizon Automated Research
arXiv:2610.07625v1 Announce Type: cross Abstract: Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, d…
- arXiv cs.AIagentsSkillPoison: Progressive Skill Poisoning via Successful Experiences
arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this learning pipeline by in…
- arXiv cs.AIagentsJoint Workflow and Prompt Optimization for User Behavior Simulation
arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of live users. It supp…
- arXiv cs.AIagentsEvaluating human-AI workflows for field research in viticulture
arXiv:2610.07669v1 Announce Type: cross Abstract: We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 2021-2024 commercial sc…
- arXiv cs.AIagentsFrom Evidence to Action: How Tool-Using Agents Fail
arXiv:2610.07753v1 Announce Type: cross Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand…
- arXiv cs.AIagentsServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?
arXiv:2610.07792v1 Announce Type: cross Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct behavior in these e…
- arXiv cs.AIagentsThe Geometry of Empowerment
arXiv:2610.07796v2 Announce Type: replace-cross Abstract: Empowerment captures the capacity for an agent to actively control its environment. While conceptually appealing as an information-theoretic quantity, the connect…
- arXiv cs.AIagentsOne Step at a Time: Trading LLM Autonomy for Process Predictability
arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually ran, and inspect it…
- arXiv cs.AIagentsHarness Engineering for Software Engineering via Modular Executable Dev-Primitives
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, existing agents remain…
- arXiv cs.AIagentsA self-learning scientific agent for X-ray diffraction
arXiv:2610.07862v1 Announce Type: cross Abstract: A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Gan Jiang, a self-lear…
- arXiv cs.AIagentsReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents
arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with steps until the sessio…