AI news archive.
4,966 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsVestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory
arXiv:2609.33822v1 Announce Type: new Abstract: An agent harness controls how a language model accesses information, uses tools, preserves memory, and checks its work. Improving this software is costly when each evaluati…
- arXiv cs.AIagentsLaya as a Typed Probabilistic Assessor: An Independent Reproduction and a Preregistered Study of Calibration and Selective Escalation
arXiv:2609.33843v1 Announce Type: new Abstract: The shipped Laya Typed-Decisions checkpoint, a 421M-parameter ModernBERT-large assessor that answers typed choice/noul/score questions over workflow state, is uniformly und…
- arXiv cs.AIagentsR$^2$ Flow: Recursive Self-Improvement via Recursive Skill Evolution
arXiv:2609.33867v3 Announce Type: replace Abstract: LLM-based agents can improve themselves across tasks by reusing and revising the skills they orchestrate into executable procedures. Flow-based training fits this loop:…
- arXiv cs.AIagentsWhen Successful Strategies Fail: Adaptation to Environmental Novelty in Terminal Agents
arXiv:2609.33870v1 Announce Type: new Abstract: LLM agents increasingly solve long-horizon tasks by autonomously interacting with their environment. In doing so, their strategies rely on assumptions about that environmen…
- arXiv cs.AIagentsWhen Consent Outlives Context: Residual Authority Replay in Long-Lived Agents
arXiv:2609.33910v1 Announce Type: new Abstract: LLM agents increasingly rely on user approval to authorize security-sensitive actions at runtime. Such approvals are granted within a specific task and execution context. I…
- arXiv cs.AIagentsHyperMCTS: Hypergraph-Augmented MCTS for Long-Horizon LLM Agents
arXiv:2609.33920v1 Announce Type: new Abstract: Long-horizon tasks require large language model (LLM) agents to coordinate decisions under constraints that span an entire solution. Monte Carlo Tree Search (MCTS) offers a…
- arXiv cs.AIagentsDesigning Reliable LLM-as-a-Judge Measurement Systems for Multi-Turn Business Agents
arXiv:2609.33955v1 Announce Type: new Abstract: Many LLM-as-a-judge evaluations score fixed outputs under a fixed task definition. Production multi-turn business agents instead require a maintained measurement system: co…
- arXiv cs.AIagentsLarge Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
arXiv:2609.34039v1 Announce Type: new Abstract: Objective: To develop and characterize CLEAR-Med, a dual-agent framework for natural-language analysis of structured clinical data that separates SQL-based invocation from…
- arXiv cs.AIagentsTowards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization
arXiv:2609.34069v1 Announce Type: new Abstract: The upgrade and rewriting of large scientific codebases has traditionally been a major challenge. While evolutionary search with large language models (LLMs) can port and a…
- arXiv cs.AIagentsPhysFieldBench: Can Multimodal Models Understand Physical Fields?
arXiv:2609.34072v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly envisioned as core components of scientific and engineering agents, yet their ability to interpret physical fields…
- arXiv cs.AIagentsGUITAR: Structured Failure Diagnosis of GUI Agents via State Transitions
arXiv:2609.34113v2 Announce Type: replace Abstract: Understanding where and why Graphical User Interface (GUI) agents fail is essential for building more reliable systems, yet current evaluation relies on step accuracy,…
- arXiv cs.AIagentsFrom Attack Success to Attack Severity: Counterfactual Memory Attacks on LLM Agents
arXiv:2609.34132v1 Announce Type: new Abstract: As LLM agents increasingly rely on persistent memory for long-horizon and personalized behavior, they can retain and reuse information across interactions, but this also cr…
- arXiv cs.AIagentsStateGuard: Analytical-State Management with Validity-Aware Intervention for Long-Horizon Data Agents
arXiv:2609.34134v1 Announce Type: new Abstract: LLM-based agents have shown strong capabilities in automated data analysis and are increasingly moving toward long-horizon, multi-stage analytical workflows. However, as th…
- arXiv cs.AIagentsEvo2Team: When Do Evolved Skills Transfer? From Selection to Deployment
arXiv:2609.34135v1 Announce Type: new Abstract: A skill bank that helps one multi-agent system may leave another's behavior unchanged. A transferred rule helps only when target agents act on it successfully. We study thi…
- arXiv cs.AIagentsWaggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms
arXiv:2609.34136v1 Announce Type: new Abstract: As LLM agents increasingly collaborate on complex tasks, how to organize their interactions becomes a central design question. Existing multi-agent systems typically learn…
- arXiv cs.AIagentsSame Tasks, Different Apps: Why Mobile GUI Agents Fail to Generalize?
arXiv:2609.34139v1 Announce Type: new Abstract: Mobile GUI agents deployed in real settings must work across different applications that support the same functionality. Most existing benchmarks test each task in only one…
- arXiv cs.AIagentsSelf-Evolving Agents via Likelihood-Guided Tool-Space Optimization
arXiv:2609.34151v1 Announce Type: new Abstract: Self-evolving agents can continually improve their behavior, while tools define the executable action space through which they interact with the environment. However, expos…
- arXiv cs.AIagentsTableSeek: Structure-Preserving Agentic Evidence Seeking over Heterogeneous Table Corpora
arXiv:2609.34157v1 Announce Type: new Abstract: Open-domain table retrieval seeks tables that contain sufficient evidence for answering a question or verifying a claim. Yet semantic relevance is often misleading: topical…
- arXiv cs.AIagentsRoutePrism: Tracing Construction Order Effects in Agent Memory
arXiv:2609.34160v1 Announce Type: new Abstract: Processing the same records in a different order can discard different evidence, yet endpoint accuracy alone cannot reveal what changed or whether it mattered. We introduce…
- arXiv cs.AIagentsReplayLens: Auditing Agents' Use of Outcomes
arXiv:2609.34177v2 Announce Type: replace Abstract: When an agent reuses logged experience, a changed decision may reflect the recorded score, the action's name, or the record's position in storage. Standard memory evalu…
- arXiv cs.AIagentsGlyphBench: A Playground for Language-Model Reinforcement Learning
arXiv:2609.34214v1 Announce Type: new Abstract: We introduce GlyphBench, an environment suite for reinforcement learning (RL) post-training of language-model agents, with over 360 tasks spanning diverse games. GlyphBench…
- arXiv cs.AIagentsSame Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures
arXiv:2609.34215v1 Announce Type: new Abstract: Evaluating how LLM agents recover from mid-task failures is central to deploying reliable agentic systems. Existing checkpoint-based benchmarks measure recovery by comparin…
- arXiv cs.AIagentsWhen Does Selection Replace Extraction? A Pre-Registered Test of Agent Memory with a Typed Decision Model
arXiv:2609.34227v1 Announce Type: new Abstract: Does conversational memory need LLM-extracted facts, or is selecting the right raw turns enough? Published results disagree. Extraction-based systems report gains from dist…
- arXiv cs.AIagentsAdaGuard: An Adaptive Guard Model with User-defined Policies
arXiv:2609.34241v2 Announce Type: replace Abstract: Guard models support the safe deployment of language model agents, but fixed risk taxonomies limit their ability to accommodate requirements that vary across applicatio…
- arXiv cs.AIagentsStashbird: Episode-Grounded Lifecycle Reconciliation for Conversational Agent Memory
arXiv:2609.34242v2 Announce Type: replace Abstract: AI agents require memory that remains consistent as conversational evidence changes. We present Stashbird, an episode-grounded memory system that uses provenance to rec…
- arXiv cs.AIagentsMaintaining Benchmarks Against Increasingly Capable Agents: Detection and Remediation of Unearned Passes
arXiv:2609.34262v1 Announce Type: new Abstract: Agentic benchmarks guide model selection and training. Yet an agent can pass a task without demonstrating the intended capability. Such outcomes constitute unearned passes;…
- arXiv cs.AIagentsBIABench: Evaluating AI agents on real-world bioimage analysis tasks
arXiv:2609.34274v1 Announce Type: new Abstract: Artificial-intelligence (AI) agents hold promise for automating bioimage analysis, yet no benchmark evaluates whether they can carry out real-world analyses end to end. Suc…
- arXiv cs.AIagentsControlScope: Workflow Revision and Reliability in LLM Agents
arXiv:2609.34313v1 Announce Type: new Abstract: How much of a running workflow should a language model agent revise? ControlScope compares continuing generated code, editing the next tool call's data arguments, and repla…
- arXiv cs.AIagentsSAGE: Structured Strategic Reasoning for Efficient LLM Game Playing
arXiv:2609.34342v2 Announce Type: replace Abstract: A strong LLM strategic agent should reason prospectively over uncertain futures, adapt its strategy to opponents' behavioral tendencies, and continuously recalibrate it…
- arXiv cs.AIagentsSemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception
arXiv:2609.34353v1 Announce Type: new Abstract: Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent…
- arXiv cs.AIagentsPersMem: Internalizing Personality into Dual-Pathway Memory for LLM Agents
arXiv:2609.34372v1 Announce Type: new Abstract: The profile of a role-playing agent usually depends on the pre-defined personality in a system prompt, whereas its memory processing pipeline, including prioritisation of s…
- arXiv cs.AIagentsOrg-Agent: Beyond Personal Assistants Towards Organizational Agents
arXiv:2609.34392v1 Announce Type: new Abstract: Language model agents serving organizations must coordinate requests from multiple users while using knowledge distributed across their interactions. We identify two comple…
- arXiv cs.AIagentsSkillFocus: Evolving Agent Skills via Capability Decomposition
arXiv:2609.34397v1 Announce Type: new Abstract: Agent skill evolution seeks to improve reusable procedural guidance for large language model (LLM) agents through iterative revision. Existing methods base each revision ma…
- arXiv cs.AIagentsBeyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation
arXiv:2609.34419v1 Announce Type: new Abstract: Automatic human-like facial reaction generation (FRG) is essential for building intelligent systems that can engage in human-computer interaction (HCI). While diverse and c…
- arXiv cs.AIagentsSocial Circuits behind Multi-agent Echo Chambers
arXiv:2609.34444v1 Announce Type: new Abstract: Language-model agents exchange messages to combine evidence, but their communication can also create echo chambers that reinforce shared errors. However, overall task perfo…
- arXiv cs.AIagentsEscaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning
arXiv:2609.34459v1 Announce Type: new Abstract: Efficient cooperation is challenging due to the usual partial observability of each agent in multi-agent reinforcement learning. Recurrent networks encode local interaction…
- arXiv cs.AIagentsPowerBench: A Benchmark for Agentic Retrieval and Reasoning in Power Systems
arXiv:2609.34492v1 Announce Type: new Abstract: Large language model (LLM) agents offer new opportunities for automated analysis in industry. However, rigorous evaluation of such agents-for example, within power system s…
- arXiv cs.AIagentsCan AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets
arXiv:2609.34510v1 Announce Type: new Abstract: AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is…
- arXiv cs.AIagentsPairPref: When Should Memory Guide the Answer? A Benchmark for Contextual Preference Use
arXiv:2609.34526v1 Announce Type: new Abstract: Memory-augmented assistants use retrieved preferences to guide their responses. A small change in the situation can change whether a preference is appropriate while barely…
- arXiv cs.AIagentsThe Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactions
arXiv:2609.34537v1 Announce Type: new Abstract: Large language model (LLM)-based scientific agents are increasingly used for scientific problem solving, yet their robustness to imperfections arising during multi-turn int…
- arXiv cs.AIagentsRemember Before You're Asked: MemDream for Self-Probing Memory Evolution
arXiv:2609.34545v1 Announce Type: new Abstract: Memory is essential for enabling LLM-based agents to maintain coherent, personalized behavior over long-horizon interactions. However, existing memory systems share a funda…
- arXiv cs.AIagentsSGG-ReflAct: Sub-Goal Guided ReflAct with Structured Planning for Reliable Long-Horizon Reasoning
arXiv:2609.34548v1 Announce Type: new Abstract: Recent advances in reasoning backbones have empowered large language model (LLM)agentstotackle complex, multi-step tasks. However, as reasoning horizons grow, inconsistent…
- arXiv cs.AIagentsSkillRubric: Co-Evolving Actor Guidance and Evaluator Rubrics for Multimodal Agents
arXiv:2609.34557v1 Announce Type: new Abstract: Recent work incorporates reusable skills distilled from past interactions into multimodal agent training, providing procedural guidance for long-horizon planning and tool u…
- arXiv cs.AIagentsFlowState: Execution State as Memory for Long-Horizon LLM Agents
arXiv:2609.34565v2 Announce Type: replace Abstract: Long-horizon tasks require LLM agents to continually draw on information from earlier interactions. However, retaining the full history increases context costs, while c…
- arXiv cs.AIagentsAfter the Fix: Transfer of Corrected Agent Experience
arXiv:2609.34603v2 Announce Type: replace Abstract: Does repairing an episode make its experience a better memory for the next task? We transfer the same failed source before and after accepted repair to a fixed target,…
- arXiv cs.AIagentsLLMs for Executable Multi-Agent System Specification Generation
arXiv:2609.34619v1 Announce Type: new Abstract: MAS specifications express the effects of the actions of the agents and their environment, as well as other temporal phenomena, such as the intervals during which an agent…
- arXiv cs.AIagentsBeyond Skill Evolution: Self-Evolving Context Management Policies for Long-Horizon Agent Harnesses
arXiv:2609.34649v1 Announce Type: new Abstract: Harness evolution improves LLM agents by learning from execution trajectories, but existing experience- and skill-based methods are less effective on long-horizon tasks. As…
- arXiv cs.AIagentsJailbreak Context Lingers: Divergent Safety Routing and Its Cross-Task Predictability in Tool Agents
arXiv:2609.34686v1 Announce Type: new Abstract: As large language models increasingly operate as tool-using agents, post-jailbreak safety feedback is often assumed to serve as a reliable safeguard; however, how lingering…
- arXiv cs.AIagentsVCN-Bench: A Video-Contextualized Navigation Benchmark for Spatial Reasoning over Prior Visual Experience
arXiv:2609.34687v1 Announce Type: new Abstract: Spatial reasoning is fundamental to embodied agents, yet it remains unclear whether spatial understanding can be carried forward to guide sequential interactions. Existing…
- arXiv cs.AIagentsResonAct: Streaming Metrics for Runtime Diagnosis and Self-Healing in Multi-Agent Systems
arXiv:2609.34701v1 Announce Type: new Abstract: Multi-agent systems (MAS) are increasingly used to automate enterprise workflows involving multiple specialized agents, external tools, and long-running task execution. Fai…