AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling

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

Spotlight, Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

We introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. 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]