Game-Theoretic Lens on LLM-based Multi-Agent Systems
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866909996885737472 |
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| author | Hao, Jianing Ding, Han Xu, Yuanjian Sun, Tianze Chen, Ran Zhang, Wanbo Zhang, Guang Li, Siguang |
| author_facet | Hao, Jianing Ding, Han Xu, Yuanjian Sun, Tianze Chen, Ran Zhang, Wanbo Zhang, Guang Li, Siguang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15047 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Game-Theoretic Lens on LLM-based Multi-Agent Systems Hao, Jianing Ding, Han Xu, Yuanjian Sun, Tianze Chen, Ran Zhang, Wanbo Zhang, Guang Li, Siguang Multiagent Systems Computer Science and Game Theory 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. |
| title | Game-Theoretic Lens on LLM-based Multi-Agent Systems |
| topic | Multiagent Systems Computer Science and Game Theory |
| url | https://arxiv.org/abs/2601.15047 |