AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsMuse Glimmer 30B: An On-Device Agentic Model
arXiv:2610.24628v3 Announce Type: replace-cross Abstract: We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hard…
- arXiv cs.AIagentsDiffuPlan: Editable Planning State for Diffusion Language Model Agents
arXiv:2610.24717v1 Announce Type: cross Abstract: Diffusion language models (dLLMs) are emerging as an alternative to autoregressive (AR) language models, yet dLLM agents largely inherit an interaction protocol designed…
- arXiv cs.AIagentsCooperation Without Shared Rewards via Reciprocal Q-Augmentation
arXiv:2610.24793v1 Announce Type: cross Abstract: General-sum games are more challenging than fully cooperative or adversarial settings because agents may deceive or exploit one another. Achieving high social welfare req…
- arXiv cs.AIagentsPair-Aware Optimization: Coordinating Exploration and Refinement for Jailbreaking LLM Agents
arXiv:2610.24823v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are vulnerable to adversarial inputs that induce attacker-specified malicious behaviors, commonly referred to as jailbreaking. Existing…
- arXiv cs.AIagentsArctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design
arXiv:2610.24914v1 Announce Type: cross Abstract: Long-horizon agent training, including software engineering, is advancing rapidly, but the recipe and infrastructure needed for a strong reinforcement-learning baseline r…
- arXiv cs.AIagentsSame Data, Different Bytes: Serialization-Invariant Integrity Checks for Agent-Published Datasets
arXiv:2610.24916v1 Announce Type: cross Abstract: Agent networks require reliable ways for requesting agents to verify where data is located, how it can be accessed, and whether the data received matches what the data ow…
- arXiv cs.AIagentsWhat Can't Jev Decide? Probing the Limits of One-Pass Decision Models
arXiv:2610.24929v1 Announce Type: cross Abstract: Jev and its open replicas choose among the options a caller supplies with a single forward pass, fast enough to decide inside an agent loop. We ask what such a model cann…
- arXiv cs.AIagentsTrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift
arXiv:2610.24930v2 Announce Type: replace-cross Abstract: Radio frequency fingerprinting (RFF) models can achieve accuracy under source domain conditions but become unreliable when the receiver, distance, collection day,…
- arXiv cs.AIagentsEpi: Towards a Persistent, Self-Improving Agent for Autonomous Research
arXiv:2610.24944v1 Announce Type: cross Abstract: We present \'{E}pi, an autonomous research agent that sustains investigations across model sessions and evolves its methods through accumulated experience. \'{E}pi combin…
- arXiv cs.AIagentsLLM Agents as Suppliers of Physical System Models: Calibration and Acceptance in Modelica
arXiv:2610.25019v1 Announce Type: cross Abstract: To procure a model of a device, a client hands a supplier a description, sometimes with recorded data or a target response, and accepts the model against this handover. W…
- arXiv cs.AIagentsAuto-MD-bench: Evaluating Autoresearch Agents for Real-World Medical Tasks
arXiv:2610.25069v1 Announce Type: cross Abstract: Autonomous agents are increasingly capable of developing machine learning (ML) models end to end. In medical practice, however, strong performance on the target metric do…
- arXiv cs.AIagentsPre-Query Retention with Bounded Bit and Word Retrieval
arXiv:2610.25098v1 Announce Type: cross Abstract: An agent can retain information before receiving a future question and later retrieve evidence from an archive. Evaluating this arrangement requires accounting for both r…
- arXiv cs.AIagentsA learner-free swarm reaches the explore-exploit equilibrium: A self-organized attractor approximating the reinforcement-learning solution
arXiv:2610.25113v3 Announce Type: replace-cross Abstract: The explore-exploit trade-off is the core problem of reinforcement learning (RL). In its multi-agent, resource-sharing form the optimum is not a scalar-return max…
- arXiv cs.AIagentsCompile Once, Run Cheap: Frozen, Portable Skills for Embodied Agents, and What Recursive Self-Improvement Adds
arXiv:2610.25114v1 Announce Type: cross Abstract: Embodied AI lacks the data-rich pretraining that gave language models their agents and skills, so new manipulation capability costs demonstrations per task or a frontier…
- arXiv cs.AIagentsNeuroAct: Advancing EEG Analysis and Decoding via Dual-Loop Autonomous Research
arXiv:2610.25211v1 Announce Type: cross Abstract: EEG analysis and decoding underpin brain-computer interfaces, cognitive assessment, and sleep monitoring. Fixed pipelines and reward-driven search do not explicitly deter…
- arXiv cs.AIagentsAssessing the Empirical Evidence for Loss of Control from Agentic General-Purpose AI
arXiv:2610.25241v1 Announce Type: cross Abstract: Theoretical literature warns that advanced generalist agentic systems might pursue instrumental goals that are misaligned with human preferences, thus posing risks of irr…
- arXiv cs.AIagentsEG-GRPO: Evidence-Guided Step-level Credit Assignment for Deep Search Agents
arXiv:2610.25269v1 Announce Type: cross Abstract: LLM-based search agents demonstrate strong capabilities in long-horizon information seeking and reasoning, typically trained via reinforcement learning. However, existing…
- arXiv cs.AIagentsMemDerive: A Diagnostic Benchmark for Agent Memory over Evolving Data
arXiv:2610.25373v1 Announce Type: cross Abstract: Long-running agents rely on memory of their past interactions for task state, such as what has been completed and which conditions still hold. Such state is usually a der…
- arXiv cs.AIagentsWhen Interpretations Persist: Downstream Influence in Gaze-Aware Assistants
arXiv:2610.25512v1 Announce Type: cross Abstract: AI assistants may carry forward not only observations but also interpretations of what a person is doing or may do next across time. Once retained, these interpretations…
- arXiv cs.AIagentsBenchmark for Structured Retrieval-Augmented Generation in Perovskite Photovoltaics
arXiv:2610.25575v1 Announce Type: cross Abstract: While retrieval-augmented language models increasingly serve as literature assistants in the physical sciences, existing benchmarks evaluate them primarily on qualitative…
- arXiv cs.AIagentsWhen Better Opportunities Backfire: Service Worker Responses to Parallel AI Deployment in Customer Acquisition
arXiv:2610.25603v1 Announce Type: cross Abstract: As AI voice agents get deployed in high-volume contact centers, firms can route low-intent leads to AI and allocate high-intent leads to human workers. Drawing on sequent…
- arXiv cs.AIagentsMapSteal: Map-based Autonomous Knowledge Stealing Attack Against Large Language Models
arXiv:2610.25626v1 Announce Type: cross Abstract: Knowledge acquisition from black-box large language models (LLMs) offers a practical approach to adapting smaller student models. However, many existing methods rely on p…
- arXiv cs.AIagentsSelecting and Composing Agent Design Patterns: Industrial Case Evidence and Controlled Reference-System Experiments
arXiv:2610.25629v1 Announce Type: cross Abstract: Agent pattern catalogs describe architectural alternatives, but engineers also need evidence for selecting and composing them. We extend a cognitive-function--execution-t…
- arXiv cs.AIagentsRemember Less, Adapt Longer: Bounded-State In-Context Reinforcement Learning with Recurrent Summary Memory
arXiv:2610.25758v1 Announce Type: cross Abstract: In-context reinforcement learning (ICRL) enables agents to adapt to new tasks without parameter updates, relying on memory to retain what they learn from experience. For…
- arXiv cs.AIagentsComputation, Not Memory: Chain-of-Thought and State Tracking in LLM Agents
arXiv:2610.25760v1 Announce Type: cross Abstract: Language-model agents often reason before they act, and the value of monitoring that reasoning depends on how it relates to their actions. We ask how an agent's actions d…
- arXiv cs.AIagentsSecure Agentic Coding through Counterexample-Grounded Feedback
arXiv:2610.25916v2 Announce Type: replace-cross Abstract: As coding models and agent harnesses become more capable, reliable security verification is becoming a central bottleneck. Improving secure code generation requir…
- arXiv cs.AIagentsPolicy Search in Code Space for Multi-Agent Pathfinding
arXiv:2610.25946v2 Announce Type: replace-cross Abstract: Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific reinforcement learning.…
- arXiv cs.AIagentsAdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
arXiv:2610.25950v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly deployed in unfamiliar and evolving environments, requiring them to proactively explore, understand, and adapt to envir…
- arXiv cs.AIagentsNIOCIS: Typed-Decision Next-Hop Routing with Node-Conditioned Option Spaces for Tool-Use Agents
arXiv:2610.25985v1 Announce Type: cross Abstract: Tool-use agents pay per decision: at each hop a language model re-reads the state and thinks for seconds, though its actions are finite and decisions recur. We present NI…
- arXiv cs.AIagentsWhen Coding Agents Outpace Review: Evaluating PRD-Driven Development in Mature Enterprise Repositories
arXiv:2610.26394v1 Announce Type: cross Abstract: Coding agents increasingly undertake development tasks in mature enterprise repositories, yet existing benchmarks offer limited insight into whether they fulfill complete…
- arXiv cs.AIagentsFake News, Real Crashes: Information Injection Attacks on LLM Financial Trading Agents
arXiv:2610.26438v1 Announce Type: cross Abstract: Large language model (LLM) agents now trade real capital using news as evidence, yet they cannot reliably tell fabricated headlines from real news. Because they read the…
- arXiv cs.AIagentsRefCraft: Benchmarking Visual-Reference-Conditioned Agentic Generation through Web Development
arXiv:2610.26826v1 Announce Type: cross Abstract: Beyond basic tool use and coding skills, effectively incorporating visual references into agentic generation is equally essential for applying AI agents to real-world pro…
- arXiv cs.AIagentsWeAct: Dynamic Weight Switching as Agent Actions
arXiv:2610.26847v2 Announce Type: replace-cross Abstract: Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previous…
- arXiv cs.AIagentsOpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory
arXiv:2610.26860v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reason…
- arXiv cs.AIagentsGenerative to Agentic AI: Survey, Conceptualization, and Challenges
arXiv:2504.18875v2 Announce Type: replace Abstract: Agentic AI builds upon Generative AI (GenAI). It constitutes the next major step in the evolution of AI with much stronger reasoning, learning, and interaction capabili…
- arXiv cs.AIagentsReflection-Driven Self-Improving Language Model Agents for Adaptive RAN Slicing
arXiv:2603.10564v2 Announce Type: replace Abstract: Adaptive control in AI-native networks remains challenging because tasks such as dynamic Radio Access Network (RAN) slicing require persistent trade-off decisions among…
- arXiv cs.AIagentsVirtual Speech Therapist: A Clinician-in-the-Loop AI Agent for Speech Therapy Planning
arXiv:2605.01101v3 Announce Type: replace Abstract: This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and indi…
- arXiv cs.AIagentsMOIRA: Policy Memory and Belief Revision for Language-Model Agents in Non-Stationary Pair Trading
arXiv:2605.01954v2 Announce Type: replace Abstract: Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambi…
- arXiv cs.AIagentsCan LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations
arXiv:2605.11633v2 Announce Type: replace Abstract: Operational disaster response goes beyond damage assessment, requiring responders to integrate multi-sensor signals, reason over road networks, populations and key faci…
- arXiv cs.AIagentsHerculean: An Agentic Benchmark for Financial Intelligence
arXiv:2605.14355v4 Announce Type: replace Abstract: As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete profe…
- arXiv cs.AIagentsNexus : An Agentic Framework for Context-Aware Time Series Forecasting
arXiv:2605.14389v2 Announce Type: replace Abstract: Language models have emerged as highly capable reasoners, yet their performance in time series forecasting remains uneven across domains and levels of contextual ground…
- arXiv cs.AIagentsConflict-Aware Multi-Agent Reasoning via Signed Graph Modeling
arXiv:2605.19418v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MAS) seek to improve reasoning by combining diverse perspectives from multiple agents. However, aggregation may overlook supportive, cont…
- arXiv cs.AIagentsDiagnosing Task Insensitivity in Language Agents
arXiv:2606.26918v2 Announce Type: replace Abstract: Language agents select actions based on task descriptions, environmental observations, and interaction histories. We investigate whether task requirements reliably cons…
- arXiv cs.AIagentsJanus: a Playground for User-Involved Agentic Permission Management
arXiv:2607.01510v2 Announce Type: replace Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should…
- arXiv cs.AIagentsADIAS: Automated Design of Interactive Agentic Systems
arXiv:2608.06410v2 Announce Type: replace Abstract: Self-evolving agents can improve without updating model weights by iteratively optimizing the harness based on evaluation feedback. Existing methods are largely candida…
- arXiv cs.AIagentsCausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift
arXiv:2608.07809v2 Announce Type: replace Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong. We study both halves of that req…
- arXiv cs.AIagentsToward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
arXiv:2608.14590v3 Announce Type: replace Abstract: Large Language Model (LLM) agents take irreversible real-world actions, yet no system gives formally grounded, task-level safety guarantees for their plans. In a PRISMA…
- arXiv cs.AIagentsBreak It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
arXiv:2608.20274v2 Announce Type: replace Abstract: Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced skills may tran…
- arXiv cs.AIagentsMobilePA-Bench: Benchmarking Mobile Planner Agents on Complex Real-World Tasks
arXiv:2608.23035v3 Announce Type: replace Abstract: Mobile planning requires a central agent to coordinate tools, persistent user context, reusable procedures, and specialized sub-agents while adapting to execution feedb…
- arXiv cs.AIagentsCascade Failure Attribution for Multi-Agent Systems
arXiv:2608.29646v2 Announce Type: replace Abstract: Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to execution failu…