通过递归构建角色视角，让大模型更准确地推理复杂心理状态。
Mind the Perspective: Let's Reason Recursively for Theory of Mind
- 用递归方式逐层构建角色视角，模拟多层心理信念。
- 在Hi-ToM等基准上达到100%准确率，显著超越现有方法。
- 适合需要高阶心智理论推理的研究与应用，如对话系统。
心智理论（ToM）推理需从部分且不对称的观察中推断代理者的信念，这对大语言模型仍是开放挑战。现有基于提示的方法通过可观测事件过滤或时间信念链提升表现，但未显式建模嵌套信念。本文提出RecToM，一种推理时框架，通过递归视角构建来建模嵌套信念。RecToM沿问题指定的角色链，从前一个角色视角构建每个角色视角，将高阶信念问题转化为最终构建视角中的现实世界问题。我们进一步提供KD45分析，表明RecToM的视角构建生成了超出简单事件过滤的合理信念模态。在多个大模型骨干上，对Hi-ToM、Big-ToM和FanToM等ToM基准的实验显示，RecToM持续优于近期先进方法，达到领先性能。值得注意的是，使用GPT-5.4和Qwen3.5时，RecToM在要求高阶心智理论推理的Hi-ToM上实现100%准确率。
原文摘要 · Abstract (English)
Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Existing prompting-based approaches improve ToM reasoning through observable-event filtering or temporal belief chains, without explicitly modeling nested beliefs. We introduce RecToM, an inference-time framework for ToM reasoning that models nested beliefs via recursive perspective construction. RecToM constructs each character perspective from the preceding character perspective along the character chain specified by the question, reducing higher-order belief questions to actual-world questions within the final constructed perspective. We further provide a KD45 analysis showing that RecToM's perspective construction induces a well-formed belief modality beyond simple event filtering. Experiments on ToM benchmarks, including Hi-ToM, Big-ToM, and FanToM, across multiple LLM backbones show that RecToM consistently outperforms recent advanced approaches, achieving state-of-the-art performance. Notably, RecToM reaches 100\% accuracy on Hi-ToM with GPT-5.4 and Qwen3.5, a benchmark requiring higher-order ToM reasoning.
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