GUM (General User Models)
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.
Documentation
Please go here for documentation on setting up and using GUMs: https://generalusermodels.github.io/gum/
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT License
Citation and Paper
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},
}
1 maintainer
Metadata
1 maintainer
Release files for gum-ai 0.1.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| gum_ai-0.1.11.tar.gz | 27.5 kB | Details |
Built distribution (wheel)
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|---|---|---|---|---|
| gum_ai-0.1.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 57.3 kB
Release files / gum_ai-0.1.11.tar.gz
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