Search: “agents”

arXiv:2610.03620v1 Announce Type: new Abstract: Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their comp

arXiv:2610.01870v1 Announce Type: new Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventio

arXiv:2610.01178v1 Announce Type: new Abstract: Manipulation failures can leave scenes in states from which a task policy cannot recover. Learning corrective behaviors requires scalable failure explor

arXiv:2610.01887v1 Announce Type: cross Abstract: Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastru

arXiv:2609.39045v1 Announce Type: new Abstract: Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a play

arXiv:2602.20144v2 Announce Type: replace-cross Abstract: We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP)

arXiv:2609.37993v1 Announce Type: cross Abstract: The BITEM team entered both subtasks of the NTCIR-19 R2C2 task with a single agentic pipeline, in which a model searches, reads and records evidence o

arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even

arXiv:2609.37501v1 Announce Type: cross Abstract: We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation vali

arXiv:2609.36739v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly structured like organizations, with roles, critics and shared memory, yet they are evaluated on tasks that la

arXiv:2609.37898v1 Announce Type: new Abstract: Reinforcement learning for long-horizon agents typically relies on sparse outcome-based rewards. This leads to a severe cold-start problem, as early-sta

arXiv:2609.35690v1 Announce Type: new Abstract: Robots that learn from a few demonstrations often require two forms of generalization. Compositional generalization recombines skills to solve new tasks

arXiv:2609.34823v1 Announce Type: new Abstract: Augmented dexterity has the potential to reduce the fatigue experienced by surgeons during repetitive surgical tasks. In this paper, we propose the firs

arXiv:2609.32361v1 Announce Type: new Abstract: Debate distillation adapts weaker verifiers using multi-agent debate transcripts to improve their judgement in subsequent debates, but gains on monitore

arXiv:2609.33807v1 Announce Type: new Abstract: How well can general-purpose multimodal models turn visual understanding and reasoning into embodied manipulation via executable code? We introduce Code

arXiv:2609.35082v1 Announce Type: new Abstract: Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon

arXiv:2609.34373v1 Announce Type: new Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should

arXiv:2609.32069v1 Announce Type: new Abstract: Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve fr

arXiv:2609.34679v1 Announce Type: new Abstract: Bipartite matching is a fundamental problem in game theory and market design. Classical approaches such as Gale--Shapley assume complete preferences and

arXiv:2609.33637v1 Announce Type: cross Abstract: In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under com

arXiv:2609.35750v1 Announce Type: new Abstract: Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most po

arXiv:2511.18038v2 Announce Type: replace-cross Abstract: Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a mult

arXiv:2609.34004v1 Announce Type: new Abstract: Equity-relevant news evolves through temporally dependent corporate events, making historical information useful only when event continuity, information

arXiv:2609.31524v1 Announce Type: new Abstract: Surgical scene understanding is critical for computer-assisted intervention, yet laparoscopic cholecystectomy remains challenged by the complex anatomy

arXiv:2609.29528v1 Announce Type: cross Abstract: Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest:

arXiv:2609.29545v1 Announce Type: new Abstract: Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then

arXiv:2609.28061v1 Announce Type: new Abstract: Automated LOD3 building modeling typically relies on purpose-built geometric or learning-based pipelines, limiting flexibility across heterogeneous buil

arXiv:2609.27287v1 Announce Type: new Abstract: Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized bur

arXiv:2609.25578v1 Announce Type: new Abstract: Urban-scale 3D reconstruction from satellite imagery supports disaster response, city monitoring, and geospatial digital twins, yet neural rendering met

arXiv:2508.15526v2 Announce Type: replace Abstract: The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilit

arXiv:2609.25052v1 Announce Type: new Abstract: 'An agent that writes its conclusions into a store it later retrieves from closes a loop usually reported as one-way contamination. Taking the loop to t

arXiv:2609.22882v1 Announce Type: cross Abstract: On June 12, 2026, the U.S. government ordered Anthropic to bar foreign nationals from two of its most capable models within ninety minutes. Unable to

arXiv:2609.22942v1 Announce Type: new Abstract: Generative models are rapidly expanding image quality assessment (IQA) beyond traditional fidelity factors to emerging dimensions such as physical plaus

arXiv:2609.22917v1 Announce Type: new Abstract: Non-technical stakeholders frequently cannot write the SQL needed to extract insights from operational databases. We built and evaluated a Text-to-SQL a

arXiv:2609.24563v1 Announce Type: new Abstract: Adapting visuomotor policies to new manipulation tasks often requires substantial manual engineering or teleoperated data collection. Simulation can pro

arXiv:2609.20586v1 Announce Type: cross Abstract: Referring scene understanding for embodied robots requires grounding object- and relation-centric language queries from a designated viewpoint. While

arXiv:2607.18554v2 Announce Type: replace-cross Abstract: Learning local policies for continuous networked systems requires accounting for the effects of decisions beyond each agent's observation neig

arXiv:2609.20519v1 Announce Type: new Abstract: As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into lon

arXiv:2609.19897v1 Announce Type: new Abstract: Historical archives pose a difficult retrieval problem for retrievalaugmented generation systems: documents are OCR-degraded, heterogeneous across genre

arXiv:2603.28662v2 Announce Type: replace-cross Abstract: Agentic vision-language models increasingly act through extended interactions, but most evaluations still focus on single-image, single-turn c

arXiv:2609.17786v1 Announce Type: new Abstract: Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions bec

arXiv:2609.17247v1 Announce Type: new Abstract: An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a

arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon

arXiv:2609.13247v1 Announce Type: cross Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly

arXiv:2609.12216v1 Announce Type: new Abstract: Self-improving agent workflows create an audit problem when the same controller can change both its behavior and the conditions under which that behavio

arXiv:2603.15976v2 Announce Type: replace Abstract: While LLMs have accelerated scientific code generation, comprehensively evaluating generated code remains challenging. Many benchmarks emphasize fun