Learning Dynamic Belief Graphs for Theory-of-mind Reasoning

Theory of Mind (ToM) reasoning in highly uncertain environments often involves complex, dynamic, and interdependent belief evolution that jointly shape the process of information seeking and sequential decision-making – especially in high-stakes settings such as disaster response, emergency medicine, and human-in-the-loop autonomy. However, prior approaches for ToM reasoning in LLMs often treat beliefs as static and independent, 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 dependencies, and linking belief evolution to information seeking and decisions. Our model contributes (i) a novel projection from textualized probabilistic statements to consistent probabilistic graphical model updates, (ii) an energy-based factor graph representation of belief interdependencies, and (iii) an ELBO-based objective that captures belief accumulation and delayed decisions. Across multiple real-world disaster evacuation datasets, our model significantly improves action prediction and recovers interpretable belief trajectories consistent with human reasoning, providing a principled module to augment LLMs using ToM reasoning with dynamic and interdependent beliefs in highly uncertain environment.