Search: “agents”

arXiv:2606.04743v1 Announce Type: cross Abstract: Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface on

arXiv:2606.02618v1 Announce Type: cross Abstract: We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-

arXiv:2606.03118v1 Announce Type: cross Abstract: Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach t

arXiv:2606.01494v1 Announce Type: cross Abstract: Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from bo

arXiv:2606.00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI

arXiv:2606.00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events t

arXiv:2502.15224v2 Announce Type: replace-cross Abstract: Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in nois

arXiv:2602.12386v2 Announce Type: replace-cross Abstract: Learning stationary policies in infinite-horizon general-sum Markov games (MGs) remains a fundamental open problem in Multi-Agent Reinforcemen

arXiv:2605.30454v1 Announce Type: cross Abstract: Tool-augmented LLM agents are vulnerable to prompt injection: a third party who controls part of the agent's context can plant instructions that the a

arXiv:2602.06361v2 Announce Type: replace-cross Abstract: We introduce a problem of fairly allocating indivisible goods (items) in which the agents' valuations cannot be observed directly, but instead

arXiv:2605.29532v1 Announce Type: cross Abstract: Exploratory GUI testing is a particularly demanding setting for MLLM agents: without predefined test scripts, an agent must autonomously navigate an a

arXiv:2605.28807v1 Announce Type: new Abstract: Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain mea

arXiv:2605.28699v1 Announce Type: new Abstract: Large language models increasingly rely on either reinforcement learning or multi-agent prompting to improve reasoning, yet these two paradigms remain d

arXiv:2605.27582v1 Announce Type: cross Abstract: Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environme

arXiv:2605.26286v1 Announce Type: cross Abstract: Real-world multi-agent reinforcement learning (MARL) systems must often operate under stale observations, stochastic communication delays, and intermi

arXiv:2602.12833v2 Announce Type: replace-cross Abstract: Longitudinal clinical reasoning over electronic health records requires tracking evolving physiological measurements, laboratory results, and

arXiv:2602.02843v3 Announce Type: replace Abstract: When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicativ

arXiv:2605.24248v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a h

arXiv:2605.25620v1 Announce Type: new Abstract: World models enable agents to predict future dynamics conditioned on actions, making the choice of latent representation central to planning and control

arXiv:2605.25389v1 Announce Type: cross Abstract: While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specializ

arXiv:2508.19113v3 Announce Type: replace Abstract: Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning w

arXiv:2605.24352v1 Announce Type: new Abstract: Multi-agent collaboration, especially in human-AI teaming, requires agents that can adapt to novel partners with diverse and dynamic behaviors. Conventi

arXiv:2605.23930v1 Announce Type: new Abstract: We introduce Quantum Frog, a two-player cooperative game built on a novel quantized-time mechanic in which the environment advances only when a player a

arXiv:2605.24238v1 Announce Type: new Abstract: In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer mode

arXiv:2510.01499v2 Announce Type: replace-cross Abstract: With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerg

arXiv:2605.18858v1 Announce Type: cross Abstract: Probabilistic prediction systems often aggregate probability estimates from multiple models into a single decision. A common assumption is that if eac

arXiv:2605.18727v1 Announce Type: cross Abstract: Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene,

arXiv:2605.04375v2 Announce Type: replace-cross Abstract: To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control

arXiv:2605.16899v1 Announce Type: new Abstract: A fundamental challenge in embodied AI is verifying if agents build internal models of spatial structure or merely learn to mimic task-specific expert t

arXiv:2605.18407v1 Announce Type: cross Abstract: While modern Large Language Models (LLMs) and agentic artificial intelligence (AI) have demonstrated transformative capabilities in digital domains, t

arXiv:2511.19399v3 Announce Type: replace-cross Abstract: Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are tr

arXiv:2501.19128v5 Announce Type: replace-cross Abstract: In many real-world scenarios, reward signal for agents are exceedingly sparse, making it challenging to learn an effective reward function for

arXiv:2605.15964v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D enviro

arXiv:2605.15505v1 Announce Type: new Abstract: In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication

arXiv:2601.15620v2 Announce Type: replace Abstract: The 1-identification problem is a fundamental pure-exploration problem in multi-armed bandits. An agent aims to determine whether there exists an ar

arXiv:2605.14261v1 Announce Type: new Abstract: How should an agent's performance in a multiagent environment be evaluated when there is a limited sample size or a high cost of running a trial? The AI

arXiv:2605.14612v1 Announce Type: cross Abstract: AI-enabled features built on LLMs and agentic workflows are difficult to test, debug, and reproduce, especially for product-focused software engineers

arXiv:2605.13131v1 Announce Type: new Abstract: Multi-Agent Proximal Policy Optimization (MAPPO) is a variant of the Proximal Policy Optimization (PPO) algorithm, specifically tailored for multi-agent

arXiv:2605.13740v1 Announce Type: new Abstract: Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model

arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr

arXiv:2605.11534v1 Announce Type: new Abstract: When an LLM-based embodied agent fails at a household task, the culprit could be misidentified objects, forgotten sub-goals, or poor action sequencing -

arXiv:2605.08401v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capabilities, largely stimulated by Reinforcement Learning with

arXiv:2605.08722v1 Announce Type: new Abstract: Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. Coord

arXiv:2502.13451v5 Announce Type: replace Abstract: Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring agents to navigate diverse and unseen environments while following natu