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arXiv AI By Xuanfa Jin, Xinyu Cui, Zhijian Ma, Xue Yan, Jian Zhao, Haifeng Zhang, Jun Wang

EpiAgent: Epistemic Self-Play for LLM Agents

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EpiAgent introduces an explicit, learnable epistemic layer for large language model agents, enabling them to maintain and update beliefs about other agents’ private information. The framework splits the policy into Epistemic Update and Action Planning stages, optimized via a bi‑level Epistemic Self‑Play algorithm that rewards accurate belief predictions. Experiments in mixed and competitive settings show EpiAgent outperforms prompting and RL baselines, highlighting the benefits of dedicated epistemic training for belief inference and action planning.

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