General User Models learn about you by observing any interaction you have with your computer. The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture the user's knowledge and preferences. GUMs introduce an architecture that infers new propositions about a user from multimodal observations, retrieves related propositions for context, and continuously revises existing propositions.

Please go here for documentation on setting up and using GUMs: https://generalusermodels.github.io/gum/docs/

Contributions are welcome! Please feel free to submit a Pull Request.

MIT License

If you're interested in reading more, please check out our paper!

Creating General User Models from Computer Use

@misc{shaikh2025creatinggeneralusermodels,
    title={Creating General User Models from Computer Use}, 
    author={Omar Shaikh and Shardul Sapkota and Shan Rizvi and Eric Horvitz and Joon Sung Park and Diyi Yang and Michael S. Bernstein},
    year={2025},
    eprint={2505.10831},
    archivePrefix={arXiv},
    primaryClass={cs.HC},
    url={https://arxiv.org/abs/2505.10831}, 
}