Theory of Mind (ToM)-the cognitive ability to reason about mental states of
ourselves and others, is the foundation of social interaction. Although ToM
comes naturally to humans, it poses a significant challenge to even the most
advanced Large Language Models (LLMs). Due to the complex logical chains in ToM
reasoning, especially in higher-order ToM questions, simply utilizing reasoning
methods like Chain of Thought (CoT) will not improve the ToM capabilities of
LLMs. We present TimeToM, which constructs a temporal space and uses it as the
foundation to improve the ToM capabilities of LLMs in multiple scenarios.
Specifically, within the temporal space, we construct Temporal Belief State
Chain (TBSC) for each character and inspired by the cognition perspective of
the social world model, we divide TBSC into self-world beliefs and social world
beliefs, aligning with first-order ToM (first-order beliefs) and higher-order
ToM (higher-order beliefs) questions, respectively. Moreover, we design a novel
tool-belief solver that, by considering belief communication between characters
in temporal space, can transform a character's higher-order beliefs into
another character's first-order beliefs under belief communication period.
Experimental results indicate that TimeToM can dramatically improve the
reasoning performance of LLMs on ToM questions while taking a big step towards
coherent and robust ToM reasoning.

TimeToM 使用时间空间构建了 Temporal Belief State Chain (TBSC)，并设计了一个新型的 belief solver，通过考虑角色之间的信仰交流，在信仰交流期间将角色的高阶信念转化为另一个角色的一阶信念，从而显著提高了 LLMs 对 ToM 问题的推理性能。