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

Paper | Project Page | Tweet

We propose AutoToM, an automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind.

Example Usage

To run AutoToM on MMToM-QA, with the default settings of reduced hypotheses and backwards inference:

python ProbSolver.py --automated --dataset_name "MMToM-QA"

To run AutoToM on ToMi-1st with a specified model input:

python ProbSolver.py --dataset_name "ToMi-1st" --assigned_model "['State', 'Observation', 'Belief']"

Requirements

• Install relevant packages:

  • run pip install -r requirements.txt

• Set your OPENAI_API_KEY:

  • On macOS and Linux: export OPENAI_API_KEY='your-api-key'

  • On Windows: set OPENAI_API_KEY='your-api-key'

Testing AutoToM with customized questions

Please check out playground.ipynb. Simply replace the story and choices with your customized input to see how AutoToM discover Bayesian models and conduct inverse planning!

Citation

Please cite the paper and star this repo if you find it useful, thanks!

@article{zhang2025autotom,
  title={AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind},
  author={Zhang, Zhining and Jin, Chuanyang and Jia, Mung Yao and Shu, Tianmin},
  journal={arXiv preprint arXiv:2502.15676},
  year={2025}
}