AutoToM is an automated agent modeling method for scalable, robust, and interpretable mental inference. It achieves SOTA on five benchmarks, produces human-like confidence estimates, and supports embodied decision-making.

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']"

• 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'

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

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

To evaluate AutoToM on the cognitive experiments (Food truck scenarios (Desire and belief inference) / Online goal inference):

cd experiment_2
cd food_truck_scenarios # or, cd online_goal_inference
python eval_AutoToM.py

The final results will be printed at the end of the evaluation.

The analysis code is in analysis.ipynb under the folder corresponding to each task.

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!

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}
}