Search: “agents”

arXiv:2608.13564v1 Announce Type: new Abstract: Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable e

arXiv:2608.11895v1 Announce Type: new Abstract: Cave networks, pipe systems, and similar maze-like environments pose significant challenges for multi-agent navigation in unknown settings with limited

arXiv:2608.10906v1 Announce Type: cross Abstract: An agent skill is a folder containing a SKILL.md file with instructions for a language-model agent, optionally accompanied by scripts and reference fi

arXiv:2608.10108v1 Announce Type: new Abstract: Long-horizon agents accumulate trajectories spanning hundreds of interleaved reasoning, action, and observation steps, where answering a query may depen

arXiv:2608.10224v1 Announce Type: new Abstract: Enterprise support agents operate in rapidly changing environments where policies, product capabilities, and knowledge bases evolve continuously, making

arXiv:2608.10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSec

arXiv:2608.08600v1 Announce Type: cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environment

arXiv:2608.06699v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environm

arXiv:2608.03728v1 Announce Type: new Abstract: Onboard satellite intelligence requires a task layer that translates mission intent into local tool calls, exposes execution state, and returns machine-

arXiv:2505.08630v2 Announce Type: replace Abstract: Training cooperative agents in sparse-reward scenarios poses significant challenges for multi-agent reinforcement learning (MARL). Without clear fee

arXiv:2607.29055v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly deployed to solve complex tasks. In case of incorrect or unsatisfactory outputs, users have to manually loc

arXiv:2607.27773v1 Announce Type: new Abstract: LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are de

arXiv:2607.26773v1 Announce Type: new Abstract: Latent communication in large language model (LLM)-based multi-agent systems (MAS) transmits continuous internal representations instead of text, but gr

arXiv:2607.24223v1 Announce Type: new Abstract: Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top

arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with expla

arXiv:2607.23955v1 Announce Type: new Abstract: Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no c

arXiv:2607.14178v2 Announce Type: replace Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated resea

arXiv:2607.22562v1 Announce Type: new Abstract: Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning.

arXiv:2607.21594v1 Announce Type: new Abstract: Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evo

arXiv:2507.19593v3 Announce Type: replace Abstract: Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often

arXiv:2607.14004v1 Announce Type: new Abstract: Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is repor

arXiv:2601.21523v2 Announce Type: replace Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards th

arXiv:2607.02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation c

arXiv:2507.23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observabil

arXiv:2606.28781v1 Announce Type: new Abstract: Every existing vector database and agent memory framework treats memory as passive storage that agents query explicitly. No system propagates knowledge

arXiv:2606.30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to exe

arXiv:2606.23664v1 Announce Type: new Abstract: Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a positi

arXiv:2606.10064v1 Announce Type: cross Abstract: Small-model agentic post-training is bottlenecked less by the algorithm than by the trajectory substrate it consumes. Leading recipes (RLVR, group-rel

arXiv:2606.08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observabili

arXiv:2512.05013v2 Announce Type: replace Abstract: Generative models augmented with external tools and update mechanisms (or extit{agents}) have demonstrated capabilities beyond intelligent prompting

arXiv:2606.01255v1 Announce Type: new Abstract: Recent text-clustering methods use large language models to propose a cluster taxonomy from a corpus and then assign each text to it. These pipelines ar

arXiv:2605.24486v1 Announce Type: new Abstract: Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more ef

arXiv:2509.09215v2 Announce Type: replace Abstract: Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent c

arXiv:2605.16300v1 Announce Type: cross Abstract: Robotic systems are moving from isolated platforms to interconnected multi-agent ecosystems that operate in human environments. This shift raises a go

arXiv:2507.21159v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have proven effective in artificial intelligence, where the multi-agent system (MAS) holds considerable promise f

arXiv:2605.08460v1 Announce Type: cross Abstract: Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems th

arXiv:2605.06933v1 Announce Type: new Abstract: Our computing ecosystem is being transformed by two emerging paradigms: the increased deployment of agentic AI systems and advancements in quantum compu

arXiv:2306.04498v3 Announce Type: replace Abstract: We study fair multi-agent multi-armed bandit learning under collision-only coordination. Agents cannot communicate explicitly during learning and ob

arXiv:2510.05174v4 Announce Type: replace-cross Abstract: When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We intr

arXiv:2604.25850v1 Announce Type: new Abstract: Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environment

arXiv:2604.25318v1 Announce Type: cross Abstract: Cutscenes are carefully choreographed cinematic sequences embedded in video games and interactive media, serving as the primary vehicle for narrative

arXiv:2510.18289v2 Announce Type: replace Abstract: Food insecurity remains a persistent public health emergency in the United States, tightly interwoven with chronic disease, mental illness, and opio

arXiv:2604.23626v1 Announce Type: new Abstract: LLM routing has achieved promising results in integrating the strengths of diverse models while balancing efficiency and performance. However, to suppor

arXiv:2508.01495v2 Announce Type: replace Abstract: Planning collision-free paths for a large group of agents is a challenging problem in many real-world applications. While recent advances in Multi-A

arXiv:2604.22080v1 Announce Type: new Abstract: LLM-based agents are rapidly being adopted for scientific data analysis, automating tasks once limited by human time and expertise. This capability is o

arXiv:2604.20261v1 Announce Type: new Abstract: Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable