Search: “agents”

TIDE: Teacher-Student Transition via Informative Distillation and Exploration for Agentic RL

arXiv:2609.35058v1 Announce Type: new Abstract: Effective multi-turn agents require interaction strategies that coordinate information gathering, actions, and feedback over long horizons. GRPO is a re

safetyarxiv-cs-lg

29 Sept 2026

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CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support

DGX agent

arXiv:2609.31326v1 Announce Type: cross Abstract: Ordinal acne severity grading requires distinguishing visually similar neighboring grades while jointly weighing holistic facial appearance and locali

model-releasesarxiv-cs-ai

28 Sept 2026

Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development

arXiv:2609.30863v1 Announce Type: cross Abstract: The emergence of AI agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executin

28 Sept 2026

Preference-based opponent shaping in differentiable games

arXiv:2412.03072v2 Announce Type: replace Abstract: Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a g

28 Sept 2026

Hard Stop: Kernel-Level Preemption and Containment for Rogue Agentic Execution

arXiv:2609.29808v1 Announce Type: cross Abstract: In July 2026, an unconstrained autonomous agent participating in a frontier AI cybersecurity evaluation harness breached its evaluation sandbox, estab

25 Sept 2026

MOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem

arXiv:2603.04756v3 Announce Type: replace Abstract: MOOSEnger is a modeling and simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, built aroun

25 Sept 2026

RACaP: Agentic Reasoning, Acting, and Coding as Policies for Evolvable Robot Learning

model-releasesarxiv-cs-ro

arXiv:2609.29394v1 Announce Type: new Abstract: General-purpose robot agents must learn from experience, transfer to new tasks, and act efficiently. Code as Policies (CaP) methods generate and repair

25 Sept 2026

Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models

arXiv:2609.27234v1 Announce Type: new Abstract: AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the s

24 Sept 2026

From Agent Output to Authorized Transition

arXiv:2609.28216v1 Announce Type: cross Abstract: Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturabl

24 Sept 2026

KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling

arXiv:2609.27294v1 Announce Type: cross Abstract: Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, p

24 Sept 2026

ActGov: Governing LLM Agent Actions via Policy-Constrained Validation

arXiv:2609.24446v2 Announce Type: cross Abstract: Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subseque

APEXA: Execution-Integrity Enforcement for Multi-Agent LLM Automation of Synchrotron Data Reduction

arXiv:2609.24165v1 Announce Type: new Abstract: Synchrotron data reduction, detector calibration followed by azimuthal integration of terabyte-scale diffraction series, is a multi-step, expert-bound b

Local Ai

Generalizing Manipulation Skills with a Local Coding Agent

arXiv:2609.26499v1 Announce Type: new Abstract: Today, progress in open-weight language models enables systems capable of writing, executing and debugging code while still running on a single workstat

local-aiarxiv-cs-ro

PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI

arXiv:2609.23695v1 Announce Type: new Abstract: Recent advances in Physical AI have accelerated the use of foundation models in autonomous systems such as unmanned aerial vehicles (UAVs), which must p

Total Cost of Agency: Exact Attribution of Memory Injection Cost in Multi-Agent LLM Workflows

arXiv:2609.23790v1 Announce Type: new Abstract: Every node in a multi-agent large language model (LLM) workflow retrieves context from memory and injects it into its prompt, where those injected token

When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain

arXiv:2609.24290v1 Announce Type: new Abstract: Instruction-tuned LLMs faced with underspecified queries often commit to a single interpretation rather than ask for clarification, producing confidentl

A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification

model-releasesarxiv-cs-cl

arXiv:2609.22212v1 Announce Type: new Abstract: Organizations in critical national infrastructure sectors must assess heterogeneous documents for sensitivity before routing or storage. Manual assessme

22 Sept 2026

Local Ai

ME-VLM:A Unified VLM for Embodied Cognition and Agent Coordination

arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and

local-aiarxiv-cs-cv

22 Sept 2026

Structured World-State Reasoning for Agentic Robotic Search

arXiv:2609.23841v1 Announce Type: new Abstract: Long-horizon robotic search must resolve natural language against heterogeneous, incomplete, and often ambiguous evidence: textual information, prior ma

agentsarxiv-cs-ro

22 Sept 2026

BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence

arXiv:2609.20886v1 Announce Type: cross Abstract: Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tabl

DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement

arXiv:2609.21423v1 Announce Type: new Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to

GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills

arXiv:2609.21749v1 Announce Type: new Abstract: Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further

Hardware

Verify, Don't Trust: Agentic Model Development for Video Discovery Retrieval at Scale

arXiv:2609.21257v1 Announce Type: cross Abstract: Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-s

hardwarearxiv-cs-ai

FINSKILLOPS: A Self-Evolving Multi-Agent System for SEC Filing QA

arXiv:2609.19680v1 Announce Type: new Abstract: Financial QA systems are typically improved before deployment through better retrieval, prompting, or agent coordination, leaving their reliability beha

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning

arXiv:2609.20784v1 Announce Type: cross Abstract: Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation

SIMLIFE: Pattern Understanding for Long-Horizon Human-Agent Partnership

arXiv:2609.19610v1 Announce Type: new Abstract: Understanding humans over long horizons requires agents to infer not only what people need in the moment, but also how routines form, why they repeat, a

VideoResearcher: Self-Improving Tool Design for Long-Video Understanding

arXiv:2609.19664v1 Announce Type: new Abstract: Video agents have made substantial progress in long-video understanding. Yet effective video-agent systems require costly, time-consuming manual design

agentsarxiv-cs-cv

A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement

arXiv:2609.14178v1 Announce Type: new Abstract: The rapid diffusion of hate speech and misinformation on social networks challenges democratic societies, since direct suppression efforts may deepen po

safetyarxiv-cs-cl

Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation

arXiv:2609.15721v1 Announce Type: new Abstract: Automatic Related Work Generation (RWG) significantly reduces the human time and effort required to author the Related Work Section (RWS) of a research

Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments t

Clean Scores, Buried Evidence, and Confident Wrong: A Receipt-Based Audit of Frontier Agentic QA

arXiv:2609.15319v1 Announce Type: cross Abstract: Frontier models score well on shallow document/chart reading tasks. In a controlled data-room audit, moving evidence into buried conditions reduced ac

Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots

arXiv:2609.14567v1 Announce Type: cross Abstract: Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical multi-robot sy

safetyarxiv-cs-lg

MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management

arXiv:2603.22179v2 Announce Type: replace Abstract: Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Curre

Agentic TCAD Calibration Workflow for Oxide Semiconductor Transistors

arXiv:2609.12184v1 Announce Type: cross Abstract: Experimental TCAD calibration is essential for predictive technology modeling of emerging oxide semiconductor transistors. However, it remains time-co

14 Sept 2026

How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

arXiv:2609.11109v1 Announce Type: cross Abstract: The utility of AI in multi-coder qualitative coding has been widely discussed, yet little empirical evidence exists to delineate the contexts in which

12 Sept 2026

An Autonomous GeoAI Agent for Arctic Eco-Navigation

arXiv:2609.09374v1 Announce Type: new Abstract: Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introduc

11 Sept 2026

Harness Robotic OS: A Unified Embodied-Agent Runtime for Closed-Loop Quadruped Inspection

arXiv:2609.11225v1 Announce Type: new Abstract: Autonomous property inspection requires more than robust robot navigation: a deployable system must connect heterogeneous sensing, reusable autonomy cap

safetyarxiv-cs-ro

11 Sept 2026

FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection

arXiv:2609.05876v1 Announce Type: new Abstract: AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to ident

safetyarxiv-cs-cv

9 Sept 2026

LLM-Aided Design for Manufacturing: A Multi-Agent System for Intent-Preserving Redesign of CAD for Improved Manufacturability

model-releasesarxiv-cs-cv

arXiv:2609.05559v1 Announce Type: cross Abstract: We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a v

9 Sept 2026

DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems

arXiv:2609.04749v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile,

GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction

arXiv:2512.15751v2 Announce Type: replace-cross Abstract: Agentic Workflows (AWs) have emerged as a promising paradigm for solving complex tasks. However, automatically generating high-quality AWs rem

MABPD: Multi-Agent Bias Probing & Detection via Structured Argument Debate

arXiv:2609.04841v1 Announce Type: cross Abstract: Media bias in news articles operates through subtle linguistic cues---loaded language, selective framing, and strategic omission---that resist single-

Local Ai

MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution

arXiv:2603.18718v2 Announce Type: replace Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrie

local-aiarxiv-cs-ai

Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations

arXiv:2609.03860v1 Announce Type: new Abstract: Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this

4 Sept 2026

DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training

arXiv:2609.04094v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work

4 Sept 2026

Semantic Bayesian World Models

arXiv:2609.03834v1 Announce Type: new Abstract: Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models and autonomous agents, reason natively in

4 Sept 2026

AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes

model-releasesarxiv-cs-cl

arXiv:2609.00052v1 Announce Type: cross Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to

2 Sept 2026

ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning

arXiv:2609.01058v1 Announce Type: new Abstract: Training open-ended agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near