LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game

• URL: http://arxiv.org/abs/2510.04980v1
• Date: Mon, 06 Oct 2025 16:17:24 GMT
• Title: LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game
• Authors: Fangzhou Liang, Tianshi Zheng, Chunkit Chan, Yauwai Yim, Yangqiu Song,
• Abstract summary: This study introduces LLM-Hanabi, a novel benchmark that uses the cooperative game Hanabi to evaluate the rationale inference and ToM.<n>Across a range of models, we find a significant positive correlation between ToM and in-game success.<n>We conclude that prioritizing first-order ToM is a promising direction for enhancing the collaborative capabilities of future models.
• Score: 47.019077016616144
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Effective multi-agent collaboration requires agents to infer the rationale behind others' actions, a capability rooted in Theory-of-Mind (ToM). While recent Large Language Models (LLMs) excel at logical inference, their ability to infer rationale in dynamic, collaborative settings remains under-explored. This study introduces LLM-Hanabi, a novel benchmark that uses the cooperative game Hanabi to evaluate the rationale inference and ToM of LLMs. Our framework features an automated evaluation system that measures both game performance and ToM proficiency. Across a range of models, we find a significant positive correlation between ToM and in-game success. Notably, first-order ToM (interpreting others' intent) correlates more strongly with performance than second-order ToM (predicting others' interpretations). These findings highlight that for effective AI collaboration, the ability to accurately interpret a partner's rationale is more critical than higher-order reasoning. We conclude that prioritizing first-order ToM is a promising direction for enhancing the collaborative capabilities of future models.

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