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

• URL: http://arxiv.org/abs/2505.17348v2
• Date: Sun, 28 Sep 2025 16:36:39 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 large language models.<n>We propose DEL-ToM, a framework that improves verifiable ToM reasoning through inference-time scaling.
• Score: 34.90622503586192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theory-of-Mind (ToM) tasks pose a unique challenge for large language models (LLMs), which often lack the capability for dynamic logical reasoning. In this work, we propose DEL-ToM, a framework that improves verifiable 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 verifiable dynamic logical reasoning. We use data generated automatically via a DEL simulator to train a verifier, which we call the Process Belief Model (PBM), to score each belief update step. During inference, the PBM evaluates candidate belief traces from the LLM and selects the highest-scoring one. This allows LLMs to allocate extra inference-time compute to yield more transparent reasoning. Experiments across model scales and benchmarks show that DEL-ToM consistently improves performance, demonstrating that verifiable belief supervision significantly enhances LLMs' ToM capabilities without retraining. Code is available at https://github.com/joel-wu/DEL-ToM.

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