Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming environments, these agents often face situations without established coordination protocols, requiring them to make intelligent inferences about teammates from limited data. This problem motivates the area of ad hoc teamwork, in which an agent may potentially cooperate with a variety of teammates to achieve a shared goal. Our study focuses on the ad hoc teamwork problem where the agent operates in an environment driven by natural language. Our findings reveal the potential of LLM agents in team collaboration, highlighting issues related to hallucinations in communication. To address this issue, we develop CodeAct, a general agent that equips LLM with enhanced memory and code-driven reasoning, enabling the repurposing of partial information for rapid adaptation to new teammates.

翻译：基于大语言模型（LLMs）的多智能体协作在基础任务中展现出熟练能力，但其在更复杂场景下的效率仍待探索。在游戏环境中，这些智能体常面临缺乏既定协调协议的状况，需要根据有限数据对队友做出智能推断。这一问题催生了临时团队合作领域的研究，即智能体可能通过与多样化的队友协作实现共同目标。本研究聚焦于自然语言驱动环境中的临时团队合作问题。我们的发现揭示了LLM智能体在团队协作中的潜力，同时指出了通信中存在的幻觉问题。为解决该问题，我们开发了CodeAct——一种为LLM配备增强记忆与代码驱动推理能力的通用智能体，使其能够重新利用部分信息以快速适应新队友。