While Large Language Models (LLMs) have demonstrated impressive
accomplishments in both reasoning and planning, their abilities in multi-agent
collaborations remains largely unexplored. This study evaluates LLM-based
agents in a multi-agent cooperative text game with Theory of Mind (ToM)
inference tasks, comparing their performance with Multi-Agent Reinforcement
Learning (MARL) and planning-based baselines. We observed evidence of emergent
collaborative behaviors and high-order Theory of Mind capabilities among
LLM-based agents. Our results reveal limitations in LLM-based agents' planning
optimization due to systematic failures in managing long-horizon contexts and
hallucination about the task state. We explore the use of explicit belief state
representations to mitigate these issues, finding that it enhances task
performance and the accuracy of ToM inferences for LLM-based agents.

评估了基于大型语言模型的多智能体协作文本游戏中的理论推理任务，并与多智能体强化学习和基于计划的基准进行比较。研究发现基于大型语言模型的智能体表现出紧密合作行为和高级理论推理能力，但也存在在管理长期视野环境和任务状态幻觉方面的规划优化限制。通过利用显式信念状态表示来减轻这些问题，改善了基于大型语言模型的智能体的任务表现和理论推理准确性。