AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind
Keywords: Theory of Mind, Bayesian inference, large language models, inverse planning
TL;DR: We propose AutoToM, a method that automates inverse planning and model discovery to achieve open-ended Theory of Mind.
Abstract: Theory of Mind (ToM), the ability to understand people's mental variables based on their behavior, is key to developing socially intelligent agents. Current approaches to Theory of Mind reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use rigid, handcrafted Bayesian Theory of Mind (BToM) models, which are more robust but cannot generalize across different domains. In this work, we introduce *AutoToM*, an automated Bayesian Theory of Mind method for achieving open-ended machine Theory of Mind. *AutoToM* can operate in any domain, infer any mental variable, and conduct robust Theory of Mind reasoning of any order. Given a Theory of Mind inference problem, *AutoToM* first proposes an initial BToM model. It then conducts automated Bayesian inverse planning based on the proposed model, leveraging an LLM as the backend. Based on the uncertainty of the inference, it iteratively refines the model, by introducing additional mental variables and/or incorporating more timesteps in the context. Empirical evaluations across multiple Theory of Mind benchmarks demonstrate that *AutoToM* consistently achieves state-of-the-art performance, offering a scalable, robust, and interpretable approach to machine Theory of Mind.
Submission Number: 86
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