Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal
  Evolution of Human States
Authors: name: Yang Xiao, name: Jiashuo Wang, name: Qiancheng Xu, name: Changhe Song, name: Chunpu Xu, name: Yi Cheng, name: Wenjie Li, name: Pengfei Liu

As Large Language Models (LLMs) increasingly participate in human-AI
interactions, evaluating their Theory of Mind (ToM) capabilities - particularly
their ability to track dynamic mental states - becomes crucial. While existing
benchmarks assess basic ToM abilities, they predominantly focus on static
snapshots of mental states, overlooking the temporal evolution that
characterizes real-world social interactions. We present DynToM, a
novel benchmark specifically designed to evaluate LLMs' ability to understand
and track the temporal progression of mental states across interconnected
scenarios. Through a systematic four-step framework, we generate 1,100 social
contexts encompassing 5,500 scenarios and 78,100 questions, each validated for
realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs
reveals that their average performance underperforms humans by 44.7\%, with
performance degrading significantly when tracking and reasoning about the shift
of mental states. This performance gap highlights fundamental limitations in
current LLMs' ability to model the dynamic nature of human mental states.