Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2606. 03137v1 Announce Type: new Abstract: LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics.
arXiv:2606. 03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another.
arXiv:2606. 03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science.
arXiv:2606. 03303v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean.
arXiv:2606. 02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue.
arXiv:2507. 21638v2 Announce Type: replace Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks.
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
arXiv:2605. 31514v2 Announce Type: replace-cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows.
arXiv:2606. 02800v1 Announce Type: cross Abstract: We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture.
arXiv:2606. 02866v1 Announce Type: new Abstract: When does multi-agent debate help data cleaning, and when does it hurt?
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
arXiv:2606. 03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone.
arXiv:2605. 03353v3 Announce Type: replace-cross Abstract: LLM agents increasingly rely on reusable skills (e.
arXiv:2606. 03083v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions.
arXiv:2606. 03103v1 Announce Type: new Abstract: Real-world professional desktop workflows in specialized creative and engineering software unfold over long horizons and often require human-in-the-loop coordination, where agents proactively seek necessary information and users provide additional instructions, clarifications, feedback, or corrections as the task progresses.
arXiv:2606. 03463v1 Announce Type: new Abstract: Conversational AI agents require memory systems that are both scalable and semantically coherent across long interaction horizons.
arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.
arXiv:2510. 17149v3 Announce Type: replace Abstract: As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability.
arXiv:2511. 02304v2 Announce Type: replace-cross Abstract: We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution.
arXiv:2606. 03118v1 Announce Type: new Abstract: Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer.