Evaluating Theory-of-Mind in Large Language Models Through Opponent Modeling

Theory-of-Mind (ToM), the ability to infer the mental states, goals, and preferences of others - is a core component of human social intelligence. In this work, we investigate whether Large Language Models (LLMs) exhibit ToM capabilities in the context of strategic interaction. We frame opponent modeling in negotiation as a grounded and interpretable ToM task, where a model must infer an agent's preferences by observing offer exchanges during the negotiation. We guide LLMs to interpret offer histories and infer latent utility representations, including issue and value weights. We conduct a comprehensive evaluation of state-of-the-art LLMs across multiple negotiation domains. Our results show that LLMs can successfully recover opponents unknown preferences and in some cases even outperform classical opponent modeling base-lines, even without task-specific training. These findings offer new evidence of LLMs' emerging capacity for social reasoning and position opponent modeling as a practical benchmark for evaluating Theory-of-Mind in foundation models.