AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915728721969152 |
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| author | Zhang, Zhining Jin, Chuanyang Jia, Mung Yao Zhang, Shunchi Shu, Tianmin |
| author_facet | Zhang, Zhining Jin, Chuanyang Jia, Mung Yao Zhang, Shunchi Shu, Tianmin |
| contents | Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, AutoToM first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, AutoToM outperforms existing ToM methods and even large reasoning models. Additionally, we show that AutoToM can produce human-like confidence estimates and enable online mental inference for embodied decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling Zhang, Zhining Jin, Chuanyang Jia, Mung Yao Zhang, Shunchi Shu, Tianmin Artificial Intelligence Computation and Language Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, AutoToM first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, AutoToM outperforms existing ToM methods and even large reasoning models. Additionally, we show that AutoToM can produce human-like confidence estimates and enable online mental inference for embodied decision-making. |
| title | AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2502.15676 |