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.
@article{arxiv.2601.15047,
title = {Game-Theoretic Lens on LLM-based Multi-Agent Systems},
author = {Jianing Hao and Han Ding and Yuanjian Xu and Tianze Sun and Ran Chen and Wanbo Zhang and Guang Zhang and Siguang Li},
journal= {arXiv preprint arXiv:2601.15047},
year = {2026}
}