AI news archive.
4,966 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsFromPitch2Board: Benchmarking LLM Agents in Long-Horizon Football Management
arXiv:2609.34710v1 Announce Type: new Abstract: Long-horizon agent benchmarks typically report how far an agent progresses, but do not identify whether its performance comes from the foundation model, scaffold, responsib…
- arXiv cs.AIagentsRSI-Router: Evolving Subtask-Level LLM Routing and Skills for Cost-Efficient Agents
arXiv:2609.34712v1 Announce Type: new Abstract: Practical deployment of large language model (LLM) agents requires strong task performance at affordable inference cost. For long-horizon agentic tasks, this performance-co…
- arXiv cs.AIagentsBEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification
arXiv:2609.34785v1 Announce Type: new Abstract: Developing agents for hardware design and verification requires reliable correctness feedback. As a hardware specification may permit correct implementations with different…
- arXiv cs.AIagentsSIPO: Selective-Inference Policy Optimization for Tree-Structured Agentic RL
arXiv:2609.34805v1 Announce Type: new Abstract: Tree-structured reinforcement learning trains search agents by comparing alternative continuations and propagating terminal rewards to intermediate decisions. Adaptive expa…
- arXiv cs.AIagentsUniOPSD: Unifying Outcome and Hindsight Feedback for Agentic Reinforcement Learning
arXiv:2609.34810v1 Announce Type: new Abstract: Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assign…
- arXiv cs.AIagentsNociception as a Control Primitive: Afferent Channels and Nociceptive Memory for Agents Deployed in One Body
arXiv:2609.34840v1 Announce Type: new Abstract: An agent deployed in a single body cannot learn how fast that body wears, because every trial that would reveal its wear resistance wears the body it would protect. We stud…
- arXiv cs.AIagentsOne Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair
arXiv:2609.34879v2 Announce Type: replace Abstract: Tool agents use large language models to act through external tools, yet successfully executed calls can still leave user requests unfulfilled. Tool-agent repair seeks…
- arXiv cs.AIagentsDGF-Bench: A Benchmark for Simulating and Auditing Deception Against Multi-Agent Governance Boards
arXiv:2609.34913v1 Announce Type: new Abstract: Tool-using language-model agents can review enterprise projects as governance boards do: they read the evidence, apply written rules and decide whether the project may proc…
- arXiv cs.AIagentsPDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents
arXiv:2609.34930v1 Announce Type: new Abstract: Large language model (LLM) agents are evolving from tool-calling systems that execute isolated instructions into task-oriented agents that pursue user goals through sustain…
- arXiv cs.AIagentsProactive Dialogue Policy Optimization via Cognitive-State Transition
arXiv:2609.34948v1 Announce Type: new Abstract: Proactive dialogue requires agents to continually adapt their policies to user feedback while progressing toward task objectives over multiple turns. To move beyond imitati…
- arXiv cs.AIagentsAction-Space Shaping for LLM Agents: Measuring and Mitigating Tool-Schema Bias
arXiv:2609.34971v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong performance on tool-use agentic tasks when given a fixed tool schema. Yet a tool schema is not the action space of an age…
- arXiv cs.AIagentsAPEX-Voice: Can Voice Agents Complete Professional Workflows Through Full-Duplex Interaction
arXiv:2609.34973v1 Announce Type: new Abstract: Full-duplex voice agents can now listen, speak, use tools, and act during spoken interactions, but fluent dialogue does not guarantee correct completion of delegated profes…
- arXiv cs.AIagentsBefore Acting, Change the State: Prospective State Intervention for Web Agents under Deceptive Interfaces
arXiv:2609.34974v1 Announce Type: new Abstract: LLM-based Web agents can autonomously complete user tasks, yet deceptive interfaces can steer them toward outcomes that conflict with users' interests. Existing defenses pr…
- arXiv cs.AIagentsEnvironmental requirements for the use of social information by artificial life agents using evolved plastic artificial neural networks
arXiv:2609.35018v1 Announce Type: new Abstract: Evolved Plastic Artificial Neural Networks (EPANNs) consist of two principal processes, the first, evolution, and the second, development and in-life learning. In the conte…
- arXiv cs.AIagentsAutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
arXiv:2609.35025v1 Announce Type: new Abstract: Recent gains in language model capability have come more from data than from architecture. Frontier labs and data companies produce verifiable agentic tasks, which supervis…
- arXiv cs.AIagentsWebPageBench: Event-Level Verification and Controlled UI-Variant Generation for Web Agents
arXiv:2609.35026v1 Announce Type: new Abstract: We present WebPageBench, an open framework for evaluating web agents in which every task is verified from the interface's own event log. Six instrumented mock sites with br…
- arXiv cs.AIagentsJRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments
arXiv:2609.35032v2 Announce Type: replace Abstract: In complex embodied visual reasoning scenarios, an agent often has only a limited field of view, and the evidence needed to answer a question may be distributed across…
- arXiv cs.AIagentsWhen Valid Tool Calls Change Meaning: Formation-Consistent Dispatch for LLM Agents
arXiv:2609.35088v1 Announce Type: new Abstract: Tool-enabled agents form calls from model-visible interfaces, while hosts later select their implementation. Standard dispatch omits the descriptor-handler relation. An unc…
- arXiv cs.AIagentsFrom Migration to Calibration: Aligning Agents with Deployment Requirements
arXiv:2609.35149v3 Announce Type: replace Abstract: Deploying, migrating, or scaling an agent changes its model, support system, application, and users. Calibration should align the agent with destination requirements wi…
- arXiv cs.AIagentsPEARL: Adaptive Prefill-Decode Execution with Elasticity for Agentic Reinforcement Learning
arXiv:2609.35158v1 Announce Type: new Abstract: Multi-turn rollout dominates the cost of agentic reinforcement learning (RL). Asynchronous execution and elastic GPU resources can accelerate this stage, but adding rollout…
- arXiv cs.AIagentsASCT: Attentive Search over Counterfactual Trees for Credit Assignment in Agentic Reinforcement Learning
arXiv:2609.35215v1 Announce Type: new Abstract: Terminal utility evaluates a complete agentic workflow, but learning requires credit for the decisions within it. We introduce Attentive Search over Counterfactual Trees (A…
- arXiv cs.AIagentsEP-Mem: Elastic Privacy Memory for Social Relationship-Aware LLM Agents
arXiv:2609.35233v1 Announce Type: new Abstract: Large language model (LLM) agents face critical privacy risks when acting as delegates in human-agent-human communication. To prevent such breaches, agents must understand…
- arXiv cs.AIagentsTowards Reliable AI Data Scientists: Data Agents with Workflow Harnesses
arXiv:2609.35255v1 Announce Type: new Abstract: Large language model agents are increasingly deployed for data-intensive work, yet reliable data analysis requires more than general-purpose reasoning and ad hoc tool augme…
- arXiv cs.AIagentsEvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
arXiv:2609.35290v2 Announce Type: replace Abstract: Recent work has explored improving agents by jointly evolving their harnesses and models, but often takes a ''potpourri'' approach that bundles together new tools, new…
- arXiv cs.AIagentsReliability Engineering for AI Systems: Challenges, Methods, and Directions
arXiv:2609.35316v1 Announce Type: new Abstract: AI reliability concerns whether an AI system performs its intended function dependably over a stated period and under stated operating conditions, with stated evidence. As…
- arXiv cs.AIagentsHyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero
arXiv:2609.35328v3 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages the learnable algori…
- arXiv cs.AIagentsTMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving
arXiv:2609.35336v1 Announce Type: new Abstract: Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because they require quantitative…
- arXiv cs.AIagentsSelf-Adapting Group of Experts for Multi-Agent Reasoning
arXiv:2609.35412v1 Announce Type: new Abstract: Multi-agent systems bring together language model agents with different roles to propose, review, and refine solutions. Each agent's response depends on its model's capabil…
- arXiv cs.AIagentsAutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain--Computer Interfaces
arXiv:2609.35456v1 Announce Type: new Abstract: EEG-based brain-computer interfaces support a broad range of applications, yet designing decoding architectures that perform well across diverse tasks remains challenging.…
- arXiv cs.AIagentsA.D.A.M.O. (Agent for language-Driven Actions with Multimodal Observations): A Visual-Symbolic Framework for Virtual Humans
arXiv:2609.35463v1 Announce Type: new Abstract: Creating believable vh requires the coherent integration of perception, reasoning, and action mediated by language. A central challenge is to combine these components into…
- arXiv cs.AIagentsSRHarness: A Harness for Agentic Symbolic Regression
arXiv:2609.35501v2 Announce Type: replace Abstract: Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hypotheses over long…
- arXiv cs.AIagentsMechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?
arXiv:2609.35515v1 Announce Type: new Abstract: Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that generate them. Existing benc…
- arXiv cs.AIagentsARISE: Adapting to Evolving Capability Gaps in Agentic Reinforcement Learning
arXiv:2609.35532v1 Announce Type: new Abstract: As a long-horizon agent improves through experience, previously observed weaknesses may recede while new limitations emerge, continually changing what it still needs to lea…
- arXiv cs.AIagentsContinuous Context Management
arXiv:2609.35540v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents commonly retain their complete interaction history until compaction is triggered at a predefined threshold. We study Continuo…
- arXiv cs.AIagentsBaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultation
arXiv:2609.35551v1 Announce Type: new Abstract: In multi-agent systems, reliable consultation is challenging because advisor capabilities vary across tasks, and misleading information can make consultation worse than aut…
- arXiv cs.AIagentsFrom Search to Research: Exploring Search Scaling in Autonomous Quantitative Factor Mining
arXiv:2609.35559v1 Announce Type: new Abstract: Inference scaling has been shown to improve large language model (LLM) performance, and this principle naturally extends to autonomous LLM agents through increased search b…
- arXiv cs.AIagentsRSI-Master: Structuring Experiments to Guide Autonomous Model Improvement
arXiv:2609.35561v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) seeks to enable AI systems to participate in improving their own capabilities. A concrete pathway is autonomous model development, wher…
- arXiv cs.AIagentsShare-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents
arXiv:2609.35576v1 Announce Type: new Abstract: Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and create persistent arti…
- arXiv cs.AIagentsPhoneCLI: From App Interfaces to Callable Commands for Mobile Agents
arXiv:2609.35671v2 Announce Type: replace Abstract: Mobile GUI agents operate through a perception--action loop: at each step they screenshot the device, invoke a vision--language model (VLM), and emit an action. It is s…
- arXiv cs.AIagentsReport: Progressive Disclosure of Agent Skills
arXiv:2609.35692v1 Announce Type: new Abstract: Users of Workday's deployed LLM-based agents often request features which can be addressed by defining named procedures, also known as skills, in the LLM context, effective…
- arXiv cs.AIagentsReinforcing Agentic Creativity in Scientific Ideation with Night Science
arXiv:2609.35706v1 Announce Type: new Abstract: Large language models (LLMs) excel at structured, verifiable tasks, but their low-entropy bias can produce homogeneous and predictable outputs, limiting their utility for o…
- arXiv cs.AIagentsFailure-Transparent Agents: Benchmarking Post-Failure Reporting in Tool-Using Language Models
arXiv:2609.35732v1 Announce Type: new Abstract: Tool-using agents can fail twice: a required tool can fail, and the agent can then report success without the evidence needed to justify it. Existing benchmarks often entan…
- arXiv cs.AIagentsShockingly Simple Self-retrospection Improves Agentic Models Without RL
arXiv:2609.35741v1 Announce Type: new Abstract: People learn not only by repeating successful actions, but also by recounting and explaining their experiences, revising their understanding to guide future behavior. Can a…
- arXiv cs.AIagentsFinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents
arXiv:2609.35744v2 Announce Type: replace Abstract: Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchm…
- arXiv cs.AIagentsTyped Temporal Interaction Features for Simulation-Backed Forecasting of Open-Source Game Release Incidents
arXiv:2609.31647v1 Announce Type: cross Abstract: Open-source video-game quality depends on inter-actions among code, assets, configuration, tests, contributors, and issue workflows, yet conventional defect predictors us…
- arXiv cs.AIagentsActive Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions
arXiv:2609.31675v1 Announce Type: cross Abstract: We introduce the Active Causal Discovery Benchmark (ACDB), an SCM-grounded environment for evaluating whether LLM agents recover causal graph structure from observations…
- arXiv cs.AIagentsA Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset
arXiv:2609.31677v1 Announce Type: cross Abstract: Face recognition is widely deployed in surveillance, access control, and forensic workflows, yet accuracy degrades sharply once the face is occluded by accessories, foreg…
- arXiv cs.AIagentsAutonomous Research Project Management as an Agent Skill: A Case Study in Exact Spectral Spatial Regression
arXiv:2609.31683v1 Announce Type: cross Abstract: This work presents an end-to-end demonstration of autonomous machine learning research conducted by an agent skill on consumer hardware. The demonstration evaluates an FF…
- arXiv cs.AIagentsAgentic Video Understanding: A Survey
arXiv:2609.31713v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is moving beyo…
- arXiv cs.AIagentsMM-VeriRec: Failure-Guided Fusion for Verifiable Agentic Multimodal Recommendation
arXiv:2609.31718v1 Announce Type: cross Abstract: Images often carry recommendation constraints that text metadata only hints at. A movie may need to look dark, a product may need a minimal style, and a visually impossib…