Large language models (LLMs) have demonstrated strong reasoning, planning, and communication abilities, enabling them to operate as autonomous agents in open environments. While single-agent systems remain limited in adaptability and coordination, recent progress has shifted attention toward multi-agent systems (MAS) composed of interacting LLMs that pursue cooperative, competitive, or mixed objectives. This emerging paradigm provides a powerful testbed for studying social dynamics and strategic behaviors among intelligent agents. However, current research remains fragmented and lacks a unifying theoretical foundation. To address this gap, we present a comprehensive survey of LLM-based multi-agent systems through a game-theoretic lens. By organizing existing studies around the four key elements of game theory: players, strategies, payoffs, and information, we establish a systematic framework for understanding, comparing, and guiding future research on the design and analysis of LLM-based MAS.

翻译：大型语言模型（LLM）已展现出强大的推理、规划与交流能力，使其能够在开放环境中作为自主智能体运行。尽管单智能体系统在适应性与协同性方面仍存在局限，但近期研究进展已将关注点转向由相互交互的LLM所构成的多智能体系统（MAS），这些系统旨在实现合作性、竞争性或混合性目标。这一新兴范式为研究智能体间的社会动态与策略行为提供了强大的实验平台。然而，当前研究仍较为零散，缺乏统一的理论基础。为填补这一空白，本文通过博弈论视角对基于LLM的多智能体系统进行全面综述。通过围绕博弈论的四个核心要素——参与者、策略、收益与信息——对现有研究进行系统性梳理，我们建立了一个用于理解、比较和指导未来基于LLM的多智能体系统设计与分析研究的统一框架。