用动态认知逻辑提升大模型心理理论推理能力，不改架构只靠推理时扩展。
DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic
- 将心理理论任务分解为基于动态认知逻辑的信念更新序列。
- 通过自动生成数据训练验证器，推理时选出最优信念路径，性能显著提升。
- 适合需要可验证推理过程的智能体、对话系统等场景。
心理理论（ToM）任务对大语言模型（LLMs）构成独特挑战，因其缺乏动态逻辑推理能力。本文提出DEL-ToM框架，通过推理时扩展而非架构修改，提升可验证的心理理论推理能力。该方法将ToM任务分解为基于动态认知逻辑（DEL）的信念更新序列，实现结构化且可验证的动态逻辑推理。我们利用DEL模拟器自动生成数据，训练一个称为过程信念模型（PBM）的验证器，用于评分每一步信念更新。推理时，PBM评估LLM生成的候选信念轨迹，并选择得分最高的路径。这使LLM能在推理阶段分配额外计算资源，获得更透明的推理过程。跨不同模型规模和基准测试的实验表明，DEL-ToM持续提升性能，证明可验证信念监督能有效增强LLM的心理理论能力，且无需重新训练。代码已开源：https://github.com/joel-wu/DEL-ToM。
原文摘要 · Abstract (English)
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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