Solving Repeated Games with Large Language Model

Sequentialreasoningisafundamentalyetchallengingcapabilityfor intelligent agents, requiring Large Language Model (LLM) agents to anticipate others’ beliefs and dynamically adapt their strategies in repeated multi-agent interactions. However, existing LLM approaches often lack a reasoning framework that jointly supports opponent modeling and effective adaptation, limiting their robustness in dynamic and complex games. To address this gap, we introduce the Reflective Hypothetical Mind (RHM) framework, inspired by the Hypothetical Mind architecture [6]. RHM maintains multiple hypothetical minds to represent evolving opponent strategies and, crucially, integrates an explicit adaptation module that translates these belief updates into adaptive decision-making. This design enables LLM agents not only to model changing behaviors but also to respond with strategically effective adaptations. Empirical results across diverse repeated games demonstrate that RHM outperforms baseline LLMs by achieving stronger coordination and adaptability across diverse repeated games, highlighting the effectiveness of unifying opponent modeling with explicit policy adaptation.

Authors

• Naming Liu Shanghai Jiao Tong University
• Youzhi Zhang CAIR, Hong Kong Institute of Science & Innovation, CAS
• Ying Wen Shanghai Jiao Tong University

Keywords

• Large Language Model
• Repeated Games