Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
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Computer Science > Machine Learning
arXiv:2609.01090 (cs)
[Submitted on 1 Sep 2026]
Title:Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
Authors:Franciszek Bernat (1 and 2), Dawid Płudowski (1 and 2), Michał Jan Włodarczyk (1 and 2), Luca Longo (3), Jianlong Zhou (4), Andreas Holzinger (5), Riccardo Guidotti (6 and 7), Wojciech Samek (8, 9 and 10), Przemysław Biecek (1, 2 and 11) ((1) Centre for Credible AI, (2) Warsaw University of Technology, (3) University College Cork, (4) University of Technology Sydney, (5) Human-Centered AI Lab, (6) University of Pisa, (7) ISTI-CNR, (8) Technical University of Berlin, (9) Fraunhofer Heinrich Hertz Institute, (10) Berlin Institute for the Foundations of Learning and Data, (11) University of Warsaw)
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Abstract:Scientific knowledge about AI models is produced faster than the community can organize it. Every few months a new foundation model reshapes the field and hundreds of papers, blogs, and technical reports document how each behaves or fails. Yet, these findings remain scattered and effectively unretrievable. To address this gap we present Modelpedia, an automated, LLM-assisted framework that extracts findings about models from published papers, links it to the model, dataset, method, and concept it concerns, and aggregates the result into a searchable public catalog. Applying the prototype to accepted ICLR 2024 and 2025 papers, we extract over a thousand findings and, treating the catalog itself as an object of study, run a meta-analysis of how the community investigates models. Now, we invite the community to explore, contribute to, and build on the open catalog, and to help establish model findings as a shared foundation for the meta-science of AI.
| Comments: | For the website, see: this https URL For the codebase, see: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.01090 [cs.LG] |
| (or arXiv:2609.01090v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01090
arXiv-issued DOI via DataCite (pending registration)
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