Reasoning Promotes Robustness in Theory of Mind Tasks
Ian B. de Haan, Peter van der Putten, Max van Duijn

TL;DR
This paper investigates how reasoning-focused large language models demonstrate increased robustness in Theory of Mind tasks, suggesting improvements are due to better problem-solving stability rather than new cognitive abilities.
Contribution
It provides evidence that reasoning training enhances robustness in ToM tasks, clarifying that gains are likely due to improved solution stability rather than novel reasoning capabilities.
Findings
Reasoning models show increased robustness to prompt variations.
Gains are attributed to better solution stability, not new ToM reasoning.
Analysis challenges the interpretation of ToM performance as social cognition.
Abstract
Large language models (LLMs) have recently shown strong performance on Theory of Mind (ToM) tests, prompting debate about the nature and true performance of the underlying capabilities. At the same time, reasoning-oriented LLMs trained via reinforcement learning with verifiable rewards (RLVR) have achieved notable improvements across a range of benchmarks. This paper examines the behavior of such reasoning models in ToM tasks, using novel adaptations of machine psychological experiments and results from established benchmarks. We observe that reasoning models consistently exhibit increased robustness to prompt variations and task perturbations. Our analysis indicates that the observed gains are more plausibly attributed to increased robustness in finding the correct solution, rather than to fundamentally new forms of ToM reasoning. We discuss the implications of this interpretation for…
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Taxonomy
TopicsChild and Animal Learning Development · Embodied and Extended Cognition · Action Observation and Synchronization
