AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind

Zhining Zhang*, Chuanyang Jin*, Mung Yao Jia*, Tianmin Shu (* equal contribution)

2025

We introduce AutoToM, an automated Bayesian Theory of Mind method for achieving open-ended machine ToM. Leveraging an LLM as the backend, AutoToM combines the robustness of Bayesian models and the open-endedness of Language models, offering a scalable and interpretable approach to machine ToM.

[Arxiv] [Code] [Project Page]

Language models represent beliefs of self and others

Wentao Zhu, Zhining Zhang, Yizhou Wang

International Conference on Machine Learning (ICML) , 2024

We investigate belief representations in LMs: we discover that the belief status of characters in a story is linearly decodable from LM activations. We further propose a way to manipulate LMs through the activations to enhance their Theory of Mind performance.

[Arxiv] [Code] [Project Page]