@inproceedings{tong-etal-2026-cogtom,
    title = "{C}og{T}o{M}: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models",
    author = "Tong, Haibo  and
      Yue, Zeyang  and
      Zhao, Feifei  and
      Lin, Erliang  and
      Jia, Lu  and
      Chen, Ruolin  and
      Sun, Yinqian  and
      Zhang, Qian  and
      Zeng, Yi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1448/",
    pages = "31383--31425",
    ISBN = "979-8-89176-390-6",
    abstract = "Whether Large Language Models (LLMs) truly possess human-like Theory of Mind (ToM) capabilities has garnered increasing attention. However, existing benchmarks remain largely restricted to narrow paradigms like false belief tasks, failing to capture the full spectrum of human cognitive mechanisms. We introduce **CogToM**, a comprehensive, theoretically grounded benchmark comprising over 8000 bilingual instances across 46 paradigms, validated by 49 human annotators. A systematic evaluation of 22 representative models, including frontier models like GPT-5.1 and Qwen3-Max, reveals significant performance heterogeneities and highlights persistent bottlenecks in specific dimensions. Further analysis based on human cognitive patterns suggests potential divergences between LLM and human cognitive structures. CogToM offers a robust instrument and perspective for investigating the evolving cognitive boundaries of LLMs. We release our code and data at https://github.com/Beijing-AISI/CogToM."
}