Learning Dynamic Belief Graphs for Theory-of-mind Reasoning
Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under uncertainty -- especially in high-stakes settings such as disaster response, emergency medicine, and human-in-the-loop autonomy. Prior approaches either prompt LLMs directly or use latent-state models that treat beliefs as static and independent, often producing incoherent mental models over time and weak reasoning in dynamic contexts. We introduce a structured cognitive trajectory model for LLM-based ToM that represents mental state as a dynamic belief graph, jointly inferring latent beliefs, learning their time-varying de...
| Authors | Ruxiao Chen, Xilei Zhao, Thomas J. Cova et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 28 September 2026 |
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