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šŸ‘„ Multi-Agent

šŸ“· CVPR2025 Ā· 3 paper notes

šŸ“Œ Same area in other venues: šŸ“· CVPR2026 (2) Ā· šŸ”¬ ICLR2026 (47) Ā· šŸ’¬ ACL2026 (40) Ā· šŸ§Ŗ ICML2026 (24) Ā· šŸ¤– AAAI2026 (26) Ā· šŸ§  NeurIPS2025 (17)

šŸ”„ Top topics: Agents Ɨ2

Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration

This paper proposes the Cooperative Tree Search (CoTS) framework, which integrates a modified Monte Carlo Tree Search with an LLM-driven reward function to guide multiple embodied agents in long-term strategic planning and highly efficient collaboration. By incorporating a plan evaluation module to prevent action confusion caused by frequent plan updates, CoTS significantly outperforms existing methods in both CWAH and TDW-MAT environments.

ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems

ComfyBench proposes the first comprehensive benchmark (200 tasks, 3205 node documentations, and 20 curriculum workflows) to evaluate the capability of LLM-based agents to autonomously design collaborative AI systems in ComfyUI. It also introduces the ComfyAgent framework, which leverages code-based workflow representation and multi-agent collaboration to achieve a resolve rate comparable to o1-preview. However, it resolves only 15% of helper creative tasks, highlighting a significant gap in autonomous system design for LLM agents.

NADER: Neural Architecture Design via Multi-Agent Collaboration

NADER models neural architecture design as a multi-LLM-agent collaborative task: a Reader extracts knowledge from papers, a Proposer generates improvement plans, a Modifier implements modifications using Directed Acyclic Graphs (DAGs), and a Reflector learns from failures. With only 10 trials, it surpasses the accuracy upper bound of the NAS-Bench-201 search space, achieving 74.51% on CIFAR-100 (compared to the best in-space search result of 73.51%).