AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- NVIDIA BlogagentsNVIDIA Agent Toolkit Expands, Grounding Enterprise Agents in Company Data
Agents are only as useful as what they know, and only as trustworthy as the boundaries they run within. Leading enterprises are using the open source NVIDIA Agent Toolkit — now expanded with new tools — to customize and…
- NVIDIA Developer BlogagentsHow NVIDIA OpenShell Reference Architecture Secures and Governs AI Agent Workloads at Scale
Autonomous agents can work for hours on complex tasks, accessing files, tools, the internet, and spawning other agents to collaborate with. Executing complex...
- NVIDIA Developer BlogagentsDeveloping an AI Factory Digital Twin with an Agent-Assisted Workflow
Building an AI factory digital twin requires developers to bring site plans, equipment assets, and engineering requirements into a consistent model that teams...
- Engadget AIagentsClaude Duet is Anthropic's answer to Meta Muse and OpenAI Dots
Anthropic subscribers in the US can join a waitlist to try the company's new agent.
- The DecoderagentsOpenAI acquires Orchestra Bio to build AI agents that speed up drug development
OpenAI is acquiring startup Orchestra Bio and plans to merge it with its life sciences AI platform Rosalind. The goal is to build AI agents that help pharma teams coordinate drug development, from tracking trials to spot…
- Latent SpaceagentsAI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess
Listen now (44 mins) | Paying for outcomes, building with agents, and the last mile of enterprise AI adoption
- Latent SpaceagentsAI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess
Paying for outcomes, building with agents, and the last mile of enterprise AI adoption
- arXiv cs.AIagentsNavalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
arXiv:2610.26887v1 Announce Type: new Abstract: Large language model (LLM) agents are evolving from conversational assistants into Cowork Agents capable of autonomous planning, tool use, and complex task execution. Finan…
- arXiv cs.AIagentsLarge language model and reinforcement learning based autonomous construction and progress updates
arXiv:2610.26899v1 Announce Type: new Abstract: Due to the rapid growth of the global population, traditional construction methods are gradually unable to meet the current housing demand. The advancement of machine learn…
- arXiv cs.AIagentsMedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
arXiv:2610.26913v1 Announce Type: new Abstract: The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring hi…
- arXiv cs.AIagentsDataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
arXiv:2610.26924v2 Announce Type: replace Abstract: General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evide…
- arXiv cs.AIagentsIndustrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing
arXiv:2610.26941v1 Announce Type: new Abstract: Large language models (LLMs) are enabling agents to move beyond predefined workflows toward autonomous planning and execution, creating new opportunities for smart manufact…
- arXiv cs.AIagentsFix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 4
arXiv:2610.26942v1 Announce Type: new Abstract: Automated theorem provers often rely on sampling language models, but proof assistants like Lean reject each failed step with a precise error message --- information that p…
- arXiv cs.AIagentsMAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning
arXiv:2610.26943v1 Announce Type: new Abstract: Mathematical reasoning is a fundamental capability for educational applications of Large Language Models (LLMs), where reliable problem solving requires not only correct an…
- arXiv cs.AIagentsSPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
arXiv:2610.26995v1 Announce Type: new Abstract: LLM-based agents increasingly rely on reusable skills. However, scaling skill libraries makes efficient and accurate skill use difficult. Full skill loading incurs high tok…
- arXiv cs.AIagentsDoes Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
arXiv:2610.27027v1 Announce Type: new Abstract: Multi-agent LLM systems improve performance on clinical tasks, and that improvement is often read as evidence of a more trustworthy system. However, the two are not the sam…
- arXiv cs.AIagentsTeaching AI Agent to Play Poker Strategically Through Preference Optimization
arXiv:2610.27028v1 Announce Type: new Abstract: A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI agents can exhibit strategic behavior under hidden information and wh…
- arXiv cs.AIagentsM-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement
arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, part…
- arXiv cs.AIagentsReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems
arXiv:2610.27046v1 Announce Type: new Abstract: As AI systems begin to resolve open research problems, reliable verification becomes increasingly important. Formal mathematics provides a trusted framework for mathematica…
- arXiv cs.AIagentsPROVISO: Provenance-Informed Action Selection for Secure LLM Agents
arXiv:2610.27115v1 Announce Type: new Abstract: LLM agents act on the external world through tools and third-party skills, so a single executed action can cause irreversible harm. Existing defenses judge a proposed actio…
- arXiv cs.AIagentsPORA: Learning When More Reasoning Pays Off for LLM Agents
arXiv:2610.27119v1 Announce Type: new Abstract: LLM agents perform tasks over multiple steps and revise their actions based on feedback. As the total reasoning budget is often limited and must support future steps, the a…
- arXiv cs.AIagentsMagnolia: A Persistent-Memory Agent Framework for Computational Chemistry
arXiv:2610.27230v1 Announce Type: new Abstract: Chemistry has recently entered a high-data regime as molecular databases from high-throughput platforms or computational studies have become available. Increasing automatio…
- arXiv cs.AIagentsEGI-BENCH: An Enterprise General Intelligence Benchmark Suite
arXiv:2610.27234v1 Announce Type: new Abstract: Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data…
- arXiv cs.AIagentsA latent control model for realistic rodent motion
arXiv:2610.27279v1 Announce Type: new Abstract: The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between…
- arXiv cs.AIagentsTEX: Test-Time Scaling Testing Agents via Execution-based Cross-Validation
arXiv:2610.27290v1 Announce Type: new Abstract: Test-case generation is critical but remains challenging for language models. Existing test-time scaling methods either isolate candidates or aggregate traces, which do not…
- arXiv cs.AIagentsVulnerability Analysis for Safe Multi-Agent Reinforcement Learning
arXiv:2610.27293v2 Announce Type: replace Abstract: Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints. Al- though existing SMARL methods focus…
- arXiv cs.AIagentsEvolving Self-tuning Benchmarks for Long-Horizon Tool Use
arXiv:2610.27307v2 Announce Type: replace Abstract: For AI assistants to move beyond simple chat interfaces and reliably assist users with applications like Gmail, Venmo, and Slack, we need to improve their tool-use capa…
- arXiv cs.AIagentsUnsupervised Tool Evolution for Coding Agents in Data-Lake Analytics
arXiv:2610.27327v1 Announce Type: new Abstract: Coding agents can answer analytical questions over a data lake by discovering, cleaning, and joining the relevant data. However, they discard this work after each question…
- arXiv cs.AIagentsAn LLM-Based Agent for Legal Compliance Assessment: Architecture, Memory, and Evaluation in a Canadian and Quebec Context
arXiv:2610.27347v1 Announce Type: new Abstract: Checking whether a text complies with a body of law is slow when done by hand and fragile when done with keyword rules. This paper presents ComplianceAgent, the legal compl…
- arXiv cs.AIagentsREM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents
arXiv:2610.27377v1 Announce Type: new Abstract: Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience eac…
- arXiv cs.AIagentsProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions
arXiv:2610.27400v1 Announce Type: new Abstract: Large language model (LLM) agents are moving beyond reactive responses toward proactive assistance that anticipates user needs without explicit requests. However, existing…
- arXiv cs.AIagentsMore Memory, Worse Agents: Error Reproduction and Anti-Persistence in LLM Agents
arXiv:2610.27406v1 Announce Type: new Abstract: LLM agents can improve across tasks without updating model parameters by storing experience from their own executions in external memory and reusing it to guide later decis…
- arXiv cs.AIagentsKPHarness: Co-Evolving Knowledge and Probing for Long-Horizon Gaming Harnesses
arXiv:2610.27417v1 Announce Type: new Abstract: A failed gaming trajectory reveals that an agent needs intervention, but not necessarily how its execution harness should change. Effective harness adaptation therefore req…
- arXiv cs.AIagentsHarness Self-Evolution for Long-Horizon Terminal Agents
arXiv:2610.27435v1 Announce Type: new Abstract: Long-horizon terminal agents may solve a task in one run yet fail across repeated runs. Stochastic errors can propagate through actions and context, creating a gap between…
- arXiv cs.AIagentsRecoverability and Verifier Strength: Two Measured Preconditions for Unattended Agent Self-Improvement
arXiv:2610.27465v1 Announce Type: new Abstract: Agent platforms are starting to improve their own agents: they read a deployed agent's execution traces, derive a change, have an automated verifier approve it, and release…
- arXiv cs.AIagentsRuntime Control for AI Agents: Closing the Evaluation--Execution Gap in Operational Workflows
arXiv:2610.27480v1 Announce Type: new Abstract: Language-model agents increasingly participate in operational workflows where approvals, binds, and payments create commitments. An approval valid when issued may no longer…
- arXiv cs.AIagentsSecuring Agentic Commerce Through Behavior-Aware Fraud Detection and Cybersecurity for Trusted AI-Agent Payment Authorization
arXiv:2610.27483v1 Announce Type: new Abstract: The growing use of autonomous artificial intelligence agents in digital commerce creates new security concerns involving agent impersonation, unauthorized payments, comprom…
- arXiv cs.AIagentsThe More the Better? A Comparison of Single- and Multi-Agent LLM Setups on a Financial Review Task
arXiv:2610.27488v1 Announce Type: new Abstract: Practitioner guidance holds that a large language model reviews a document better when the work is divided among agents. We test a narrow form of it in a controlled compari…
- arXiv cs.AIagentsWhen Health Assistants Agree Too Readily: A Benchmark of Time-Critical Self-Diagnosis
arXiv:2610.27518v1 Announce Type: new Abstract: Every day, more than 40 million people ask ChatGPT a health question, such as whether laptop posture explains a stiff neck and days of pounding headache. Posture is plausib…
- arXiv cs.AIagentsMemDev-Bench: Does Memory Help Code Agents?Benchmarking Memory Systems over Real Repository Histories
arXiv:2610.27524v1 Announce Type: new Abstract: Code agents increasingly work on evolving real repositories, and memory systems aim to help them reuse earlier experience. However, such experience is diverse and evolving,…
- arXiv cs.AIagentsAutomated Multi-Agent Organization Design: From Architecture Search to Organizational Learning
arXiv:2610.27531v1 Announce Type: new Abstract: Agentic system design is increasingly shifting from hand-crafted prompts and workflows toward automated search over agentic programs and interaction structures, with multi-…
- arXiv cs.AIagentsAct on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment
arXiv:2610.27548v2 Announce Type: replace Abstract: When an LLM agent acts on behalf of a user, it is expected to act within the authorization scope the user grants. Existing agent evaluations largely measure task comple…
- arXiv cs.AIagentsRaspAssistant as an Agentic Framework for Automating Adsorption Simulation Research in Porous Materials
arXiv:2610.27572v1 Announce Type: new Abstract: Porous-material adsorption studies require coordinated structure preparation, force-field selection, simulation, validation, and interpretation, making end-to-end automatio…
- arXiv cs.AIagentsThe Show-Up Attack: When Subagents Win Over Their Supervisor
arXiv:2610.27574v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed to coordinate multiple large language model (LLM) agents. However, this also creates new attack surfaces: malicious agents may…
- arXiv cs.AIagentsDuplexLedger: Benchmarking Transactional Memory in Full-Duplex Speech Agents
arXiv:2610.27581v1 Announce Type: new Abstract: Full-duplex speech agents must update persistent memory while listening and speaking, yet it remains unclear which events should be stored, revised, or excluded. Existing b…
- arXiv cs.AIagentsBeyond Experience: Knowledge-Guided Evolution for NPU Kernel Generation
arXiv:2610.27588v1 Announce Type: new Abstract: LLM agents have become capable kernel engineers on mainstream GPU stacks, but they struggle on emerging accelerators such as Ascend NPUs, whose programming model is barely…
- arXiv cs.AIagentsCUA-Recovery: Benchmarking Error Recovery in Long-Horizon Computer-Use Agents
arXiv:2610.27645v1 Announce Type: new Abstract: As computer-use agents (CUAs) move to long-horizon workflows spanning multiple applications, undetected errors can silently corrupt persistent state and cause cascading fai…
- arXiv cs.AIagentsDoes Multi-Agent Collaboration Pay When Agents Serve Users? Testing Agent Scaling in Dual-Control Settings
arXiv:2610.27655v1 Announce Type: new Abstract: Multi-agent systems (MAS) built from language models are deployed on the premise that collaboration beats a single-agent system (SAS), yet when it pays is unclear. The most…
- arXiv cs.AIagentsReusing Multimodal Agents: Efficient Evaluation of Sound Designs in Games
arXiv:2610.27670v1 Announce Type: new Abstract: This paper introduces a novel framework for efficiently evaluating in-game sound designs using multimodal learning and reinforcement learning. Sound design significantly in…
- arXiv cs.AIagentsWhen Does Experience Memory Help Closed-Loop LLM Driving Agents? A Controlled Evaluation with Resampled Warmup Pools, Token-Matched Fillers, and Audited Baselines
arXiv:2610.27714v1 Announce Type: new Abstract: When does experience memory help a closed-loop LLM driving agent? Published gains cannot say, because the two factors we find to matter most, which warmup experiences the p…