The significant advancements in large language models (LLMs) have presented
novel opportunities for tackling planning and decision-making within
multi-agent systems. However, as the number of agents increases, the issues of
hallucination in LLMs and coordination in multi-agent systems (MAS) have become
increasingly pronounced. Additionally, the efficient utilization of tokens
becomes a critical consideration when employing LLMs to facilitate the
interactions of large numbers of agents. In this paper, we present a novel
framework aimed at enhancing coordination and decision-making capabilities of
LLMs within large-scale multi-agent environments. Our approach draws
inspiration from the actor-critic framework employed in multi-agent
reinforcement learning, and we develop a modular and token-efficient solution
that effectively addresses challenges presented by LLMs and MAS. Through
evaluations conducted in experiments involving system resource allocation and
robot grid transportation, we demonstrate the considerable advantages afforded
by our proposed approach.

本研究论文提出了一种新的框架，旨在提高大规模多智能体环境中大型语言模型的协调和决策能力，通过实验证明了我们提出的方法在系统资源分配和机器人网格运输方面具有显著优势。