DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic

• URL: http://arxiv.org/abs/2505.17348v1
• Date: Thu, 22 May 2025 23:52:56 GMT
• Title: DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic
• Authors: Yuheng Wu, Jianwen Xie, Denghui Zhang, Zhaozhuo Xu,
• Abstract summary: Theory-of-Mind (ToM) tasks pose a unique challenge for small language models (SLMs) with limited scale.<n>We propose DEL-ToM, a framework that improves ToM reasoning through inference-time scaling.
• Score: 28.54147281933252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theory-of-Mind (ToM) tasks pose a unique challenge for small language models (SLMs) with limited scale, which often lack the capacity to perform deep social reasoning. In this work, we propose DEL-ToM, a framework that improves ToM reasoning through inference-time scaling rather than architectural changes. Our approach decomposes ToM tasks into a sequence of belief updates grounded in Dynamic Epistemic Logic (DEL), enabling structured and transparent reasoning. We train a verifier, called the Process Belief Model (PBM), to score each belief update step using labels generated automatically via a DEL simulator. During inference, candidate belief traces generated by a language model are evaluated by the PBM, and the highest-scoring trace is selected. This allows SLMs to emulate more deliberate reasoning by allocating additional compute at test time. Experiments across multiple model scales and benchmarks show that DEL-ToM consistently improves performance, demonstrating that verifiable belief supervision can significantly enhance ToM abilities of SLMs without retraining.

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