To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks

• URL: http://arxiv.org/abs/2602.10625v1
• Date: Wed, 11 Feb 2026 08:16:13 GMT
• Title: To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
• Authors: Nanxu Gong, Haotian Li, Sixun Dong, Jianxun Lian, Yanjie Fu, Xing Xie,
• Abstract summary: Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions.<n>Recent progress in Large Reasoning Models (LRMs) has boosted step-by-step inference in mathematics and coding.<n>We present a systematic study of nine advanced Large Language Models (LLMs) comparing reasoning models with non-reasoning models.
• Score: 56.11584171938381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions, which is essential for natural social interaction. Although recent progress in Large Reasoning Models (LRMs) has boosted step-by-step inference in mathematics and coding, it is still underexplored whether this benefit transfers to socio-cognitive skills. We present a systematic study of nine advanced Large Language Models (LLMs), comparing reasoning models with non-reasoning models on three representative ToM benchmarks. The results show that reasoning models do not consistently outperform non-reasoning models and sometimes perform worse. A fine-grained analysis reveals three insights. First, slow thinking collapses: accuracy significantly drops as responses grow longer, and larger reasoning budgets hurt performance. Second, moderate and adaptive reasoning benefits performance: constraining reasoning length mitigates failure, while distinct success patterns demonstrate the necessity of dynamic adaptation. Third, option matching shortcut: when multiple choice options are removed, reasoning models improve markedly, indicating reliance on option matching rather than genuine deduction. We also design two intervention approaches: Slow-to-Fast (S2F) adaptive reasoning and Think-to-Match (T2M) shortcut prevention to further verify and mitigate the problems. With all results, our study highlights the advancement of LRMs in formal reasoning (e.g., math, code) cannot be fully transferred to ToM, a typical task in social reasoning. We conclude that achieving robust ToM requires developing unique capabilities beyond existing reasoning methods.

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