Large Language Models (LLMs) have developed rapidly and are widely applied to both general-purpose and professional tasks to assist human users. However, they still struggle to comprehend and respond to the true user needs when intentions and instructions are imprecisely conveyed, leading to a divergence between subjective user believes and true environment states. Resolving this epistemic divergence requires Theory of Mind (ToM), yet existing ToM evaluations for LLMs primarily focus on isolated belief inference, overlooking its functional utility in real-world interaction. To this end, we formalize ToM for LLMs as a mechanism for epistemic divergence detection and resolution, and propose a benchmark, \benchname, to assess how models reconcile user beliefs and profiles in practice. Results across 11 leading models reveal a significant limitation to identify underlying cognitive gaps that impede task success. To bridge this gap, we further curate a trajectory-based ToM dataset linking belief tracking with task-related state inference. The model trained on this data via reinforcement learning shows consistent improvement in reasoning about user mental states, leading to enhanced downstream performance. Our work highlights the practical value of ToM as an essential interaction-level mechanism rather than as a standalone reasoning skill.

翻译：大型语言模型（LLMs）发展迅速，已广泛应用于通用及专业任务以辅助人类用户。然而，当用户意图与指令表述不精确时，模型仍难以理解并回应用户的真实需求，导致用户主观信念与真实环境状态之间产生分歧。解决此类认知分歧需要借助心智理论（Theory of Mind, ToM），但现有针对LLMs的ToM评估主要集中于孤立的信念推断，忽视了其在真实交互中的功能效用。为此，我们将LLMs的ToM形式化为一种认知分歧检测与解决机制，并提出基准测试\benchname，以评估模型在实践中如何协调用户信念与用户画像。在11个主流模型上的测试结果表明，当前模型在识别阻碍任务成功的深层认知差距方面存在显著局限。为弥合这一差距，我们进一步构建了一个基于轨迹的ToM数据集，将信念追踪与任务相关状态推断相联结。通过强化学习在该数据上训练的模型，在推理用户心理状态方面表现出持续改进，并提升了下游任务性能。本研究揭示了ToM作为交互层面核心机制（而非独立推理技能）的实用价值。