Jun 2026· arXiv.org· Vol abs/2606.11724· 2 citations· 34 references
Computer Science
TL;DR
RecToM is introduced, an inference-time framework for ToM reasoning that models nested beliefs via recursive perspective construction that consistently outperforms recent advanced approaches, achieving state-of-the-art performance.
Abstract
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. Our code is available at https://github.com/you68681/rectom.
Theory of mind (ToM), the ability to reason about others'mental states, underlies effective communication and coordination. Its higher-order form enables reasoning over nested perspectives in strategic interactions. However, existing evaluations often equate belief-nesting depth with reasoning order, allowing nominally...
Shan-Shan You, Zheng-Qi Wang, Hao-Tian Liu et al.· 0 citations
This work generates concise rules that express relations between perception to action and uses Group Relative Policy Optimization (GRPO) to train Qwen2.5 and Llama3 models across four ToM benchmarks, suggesting that explicit rules help models bridge the reasoning gap in ToM and support knowledge transfer across models.
Soyoung Oh, Nursulu Sagimbayeva, N. Jennings et al.· 0 citations
State of Thought is proposed, a new reasoning paradigm that enables endogenous reasoning in LLMs, with the model's internal reasoning state governing how reasoning unfolds, and provides a generalizable and efficient alternative to existing test-time reasoning paradigms.
Z. Gong, Yi-Kun Hou, Zi-Hao Zeng et al.· 0 citations
Dialogue and collaborative assistants repeatedly judge what a partner knows, believes, or intends. Typed decision models offer a potential alternative to generative large language models for bounded Theory of Mind (ToM) selection. We present Jev-ToM, comparing five typed decision models and seven generative baselines o...
Ka-Po Chung, Chunkit Chan, Yuxuan Liu et al.· 0 citations
This work extends the mechanistic study of ICL to strategic multi-agent settings, introduces REE as a diagnostic tool for distinguishing reasoning from extrapolation, and provides a reusable framework for probing the boundaries of LLM reasoning in recursive belief tasks.
Y. Liu, Wen-Wen Li, Yi-Fan Dou et al.· 0 citations
Large foundation models have accelerated progress toward general-purpose agents that interact with humans and other agents through language and multimodal signals. However, robust multi-agent decision-making requires reasoning about what other agents know, intend, and are likely to do under partial observability. Curre...
Hanan Gani, Lu-Lu Shao, M. Chandraker· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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