AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsTesting the Planning Claims of LLM-Based Agents against Formal Semantics
arXiv:2610.22469v1 Announce Type: new Abstract: LLM-based agents are increasingly credited with the competences of practical reasoning: planning, monitoring, and correcting their own actions. Their success is attributed…
- arXiv cs.AIagentsFRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds
arXiv:2610.22561v1 Announce Type: new Abstract: We present FRACTAL, a self-similar multi-agent architecture for recursive goal decomposition, in which the same five-component organizational pattern -- Orchestrator, Ensem…
- arXiv cs.AIagentsAt the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier
arXiv:2610.22603v2 Announce Type: replace Abstract: Recent breakthroughs in AI for mathematics---ranging from Olympiad medal performances to resolving long-standing open conjectures---have rapidly expanded what automated…
- arXiv cs.AIagentsFrom Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents
arXiv:2610.22668v1 Announce Type: new Abstract: Long-term memory enables LLM agents to reuse past experiences, yet retrieving relevant memories does not necessarily provide sufficient evidence for answering a query. Esse…
- arXiv cs.AIagentsNot All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation
arXiv:2610.22672v1 Announce Type: new Abstract: Modern mobile GUI agents can perceive visible screens and execute local actions, but they still lack persistent app-specific navigation knowledge: where functions live, whi…
- arXiv cs.AIagentsR$^2$Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop
arXiv:2610.22740v2 Announce Type: replace Abstract: In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, includ…
- arXiv cs.AIagentsWhen Can You Trust an Agent's Own Verifier? Stress-Testing Counterfactual Rationale Verification Under Search Pressure and Oracle Access
arXiv:2610.22779v1 Announce Type: new Abstract: Automated agent design increasingly trusts a single score to decide whether a change is an improvement -- but a score optimized aggressively enough ceases to measure what i…
- arXiv cs.AIagentsMS-Eval: Benchmarking Sub-Agent Delegation and Orchestration in MCP-Intensive Working Scenarios
arXiv:2610.22869v1 Announce Type: new Abstract: LLM agents are increasingly integrated into real-world works, yet they must handle diverse domain-specific tools and complex multi-sub-task problems that sequential tool in…
- arXiv cs.AIagentsWhen to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents
arXiv:2610.22897v1 Announce Type: new Abstract: Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses. We study when retaining, compressing, or removing the same his…
- arXiv cs.AIagentsReward Engine: Reward-Centric Self-Evolution for Open-Ended Tool Agents
arXiv:2610.22915v1 Announce Type: new Abstract: Open-ended tool agents can complete the same objective through different tool paths, and their quality depends jointly on the interaction trace, environment state, and fina…
- arXiv cs.AIagentsFrom Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents
arXiv:2610.22987v1 Announce Type: new Abstract: Coding agents use execution harnesses to constrain the consequences of prompt injection by enforcing permission boundaries. These systems deterministically intercept any ou…
- arXiv cs.AIagentsAdaptation of Computer Use Agents to New Software with User Manuals
arXiv:2610.23012v1 Announce Type: new Abstract: Recent advances in multimodal foundation models have significantly improved the ability to operate real-world software through graphical user interfaces (GUIs) using visual…
- arXiv cs.AIagentsWhere Research Ideation Begins: Forward-Citation Supervision for Actionable Limitation Generation
arXiv:2610.23038v1 Announce Type: new Abstract: Automated research ideation has advanced rapidly, with multi-agent workflows that generate whole research ideas end to end. These frameworks leave largely unexamined the st…
- arXiv cs.AIagentsDistill Until Divergence: Error-Truncated Hindsight Distillation for Agentic RL
arXiv:2610.23048v1 Announce Type: new Abstract: Long-horizon interaction requires agents to make multiple decisions in a changing environment. Outcome-based GRPO lacks fine-grained guidance for intermediate decisions. Re…
- arXiv cs.AIagentsAgentic In-Context Learning: What Algorithms Can Transformers Learn in Closed-Loop Interaction?
arXiv:2610.23051v1 Announce Type: new Abstract: The learning algorithm view of in-context learning (ICL) studies how fixed-parameter Transformers learn from supplied examples. Language model agents extend this setting th…
- arXiv cs.AIagentsWebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning
arXiv:2610.23104v1 Announce Type: new Abstract: Real-world frontend development requires agents to compose reproduction, generation, modification, and repair over an evolving application state. Learning a general policy…
- arXiv cs.AIagentsQuAD: Quantitative Assessment via Distribution-Alignment for Qualitative Data Quality Enabled by a Content-Aware Multi-Agent System
arXiv:2610.23153v1 Announce Type: new Abstract: Assessing whether qualitative responses collected from semi-structured questions are ready for a particular analysis goal is a persistent challenge. Manual qualitative anal…
- arXiv cs.AIagentsOfficeTown: Generative Organizations for Evolving Agent Intelligence
arXiv:2610.23194v1 Announce Type: new Abstract: AI agents increasingly use files and software tools to produce professional deliverables. Such work draws on materials, decisions, and feedback accumulated across assignmen…
- arXiv cs.AIagentsBeyond Reward Scoring: Online Multi-Agent Preference Learning for Multimodal Response Generation
arXiv:2610.23256v1 Announce Type: new Abstract: Multimodal preference alignment often follows a score-based RLHF pipeline: a reward model is first trained to learn human preferences, then used to score on-policy response…
- arXiv cs.AIagentsAnnotrace: A Workbench for Step-Level Annotation of Agent Traces
arXiv:2610.23284v3 Announce Type: replace Abstract: Every study of why LLM agents fail begins by labelling traces by hand, and every such study builds its own throwaway tool. Observability platforms record agent traces b…
- arXiv cs.AIagentsOptimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research
arXiv:2610.23299v1 Announce Type: new Abstract: Most retrieval-augmented systems treat literature search as a single retrieval step followed by summarization. Real literature research works differently: the investigator'…
- arXiv cs.AIagentsFrom Memory Hits to Answerable Evidence: Query-Conditioned Memory Compilation for Long-Term Agent Memory
arXiv:2610.23313v1 Announce Type: new Abstract: Long-term agent memory requires recovering answer-relevant evidence dispersed across turns and sessions. Existing systems increasingly use structured memories and adaptive…
- arXiv cs.AIagentsAgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics
arXiv:2610.23317v1 Announce Type: new Abstract: Enterprise data analysis requires agents to connect analytical requests with knowledge distributed across databases, business documents, and existing example tasks. Without…
- arXiv cs.AIagentsRobust Function Calling for LLM Agents Under Interface Drift
arXiv:2610.23342v1 Announce Type: new Abstract: Tool-augmented language-model agents rely on interface text: tool descriptions, argument schemas, and the candidate tool list shown at inference time. These interfaces ofte…
- arXiv cs.AIagentsGatewayBench: Unified Capability and Security Evaluation Platform for MCP Gateways
arXiv:2610.23449v1 Announce Type: new Abstract: AI agents have increasingly interacted with external environments by invoking tools, with Model Context Protocol (MCP) emerging as a standard interface for connecting agent…
- arXiv cs.AIagentsSkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents
arXiv:2610.23554v1 Announce Type: new Abstract: Large language model agents increasingly rely on skills to extend their capabilities in complex tasks. However, scalable skill use requires Selective Skill Exposure (SSE):…
- arXiv cs.AIagentsDarwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents
arXiv:2610.23574v1 Announce Type: new Abstract: While Large Language Model (LLM) agents have demonstrated remarkable capabilities across complex tasks, they predominantly operate in isolated sessions, lacking the ability…
- arXiv cs.AIagentsRL-Guided Temporal Localization for Dual-Channel Retrieval in Long-Horizon Agent Memory
arXiv:2610.23598v1 Announce Type: new Abstract: Time-aware retrieval is crucial for long-horizon LLM agents to effectively leverage ever-growing long-term memory. Existing methods focus on lexical or semantic similarity,…
- arXiv cs.AIagentsFrom Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents
arXiv:2610.23599v1 Announce Type: new Abstract: Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, transferable capabilit…
- arXiv cs.AIagentsDrugHarness: An Evolvable Agent Harness for Computational Drug Discovery
arXiv:2610.23791v1 Announce Type: new Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHarness, an evolvable ag…
- arXiv cs.AIagentsSelf-Building Labs
arXiv:2610.23826v2 Announce Type: replace Abstract: Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experimen…
- arXiv cs.AIagentsAI-Driven Reproducibility and Analytical Robustness in Scientific Discovery
arXiv:2610.23845v1 Announce Type: new Abstract: Reproducible and analytically robust research findings are essential to scientific progress, but evaluating these properties typically requires costly manual effort. We inv…
- arXiv cs.AIagentsCausalEvo: Causal Skill Self-Evolution for Health LLM Agents
arXiv:2610.21016v1 Announce Type: cross Abstract: Large language model (LLM) agents for health can accumulate reusable textual skills from task feedback, such as calculation criteria, measurement time points, and formula…
- arXiv cs.AIagentsWhen is a large language model a good calibration agent? Physics-labeled prompting for budgeted engineering simulator calibration
arXiv:2610.22379v1 Announce Type: cross Abstract: Calibrating expensive engineering simulators requires tuning model parameters to match reference data, a process heavily constrained by computational costs. This study in…
- arXiv cs.AIagentsTowards Adaptive and Real-Survey-Aligned Simulated Urban Mobility Behaviour of Generative Agents
arXiv:2610.22407v1 Announce Type: cross Abstract: In existing agent-based simulations of multimodal urban mobility, agents, representing humans, choose their travel modes either using discrete choice or machine learning…
- arXiv cs.AIagentsDriveWorld3D Evidence Constrained 3D World Modeling and Generative Simulation for Real-World Autonomous Driving
arXiv:2610.22450v1 Announce Type: cross Abstract: Three dimensional modeling for autonomous driving must continuously estimate road geometry, obstacle occupancy, traffic participant behavior, and observational uncertaint…
- arXiv cs.AIagentsAuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems
arXiv:2610.22511v1 Announce Type: cross Abstract: Evaluating language models and agents requires combining benchmark accuracy, judge-based assessment, runtime performance, tool-use behavior, and retrieval quality. Differ…
- arXiv cs.AIagentsAlphaBet: Reinforcement Learning for LLM Event Forecasting Agents
arXiv:2610.22684v1 Announce Type: cross Abstract: Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome. Yet outcome-based training of Large Language Model…
- arXiv cs.AIagentsExplore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
arXiv:2610.22688v1 Announce Type: cross Abstract: Large language model agents must discover relevant tools from large libraries, e.g., thousands of tools, as new needs arise during task execution. When several candidates…
- arXiv cs.AIagentsNoveltyScope: A Multi-Agent Research Assistant for Evidence-Grounded Novelty Assessment of Scientific Papers
arXiv:2610.22703v1 Announce Type: cross Abstract: NoveltyScope is a prototype that compares a scientific paper with retrieved prior work and produces a structured report and a five-level novelty score. The system extract…
- arXiv cs.AIagentsSelf-Evolving Coding Agents via Harness-Policy Optimization
arXiv:2610.22791v1 Announce Type: cross Abstract: Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that med…
- arXiv cs.AIagentsSTRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases
arXiv:2610.22795v1 Announce Type: cross Abstract: In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions. Evaluatin…
- arXiv cs.AIagentsCooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
arXiv:2610.22882v1 Announce Type: cross Abstract: Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes. We examine whether five productio…
- arXiv cs.AIagentsPersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
arXiv:2610.22990v1 Announce Type: cross Abstract: Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before conseque…
- arXiv cs.AIagentsTree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
arXiv:2610.23181v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models. However, training agents over long-horizon interact…
- arXiv cs.AIagentsPreventing Data Hallucinations in Agentic AI for Optical Network Troubleshooting
arXiv:2610.23252v1 Announce Type: cross Abstract: An agentic AI system for automated troubleshooting in optical communication networks is demonstrated that integrates data references into the agent architecture to preven…
- arXiv cs.AIagentsBeyond Marginal Influence: Interaction-Aware Memory Selection for LLM Agents
arXiv:2610.23266v1 Announce Type: cross Abstract: LLM-agent memory selection often uses retrieval rankings based on record relevance or individual influence. Such rankings cannot identify sufficient subsets under redunda…
- arXiv cs.AIagentsDesigning for Imperfect Prompts: A Simulation-Based Evaluation of an LLM-Based Enterprise Data Assistant Using Knowledge Graphs
arXiv:2610.23304v1 Announce Type: cross Abstract: Enterprise data assistants using large language models (LLMs) promise democratized data access in organizations. Yet in our real-world deployment within the CFO unit's da…
- arXiv cs.AIagentsMobileInject: Evaluating Reasoning-to-Action Security of MLLM-Based Mobile Agents under Cross-Modal Consistency Injection
arXiv:2610.23307v1 Announce Type: cross Abstract: Multimodal large language model (MLLM)-based mobile agents are rapidly evolving from conversational assistants into autonomous systems that perceive, reason over, and act…
- arXiv cs.AIagentsRECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
arXiv:2610.23445v1 Announce Type: cross Abstract: In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents. In this pape…