Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
Franciszek Bernat (Centre for Credible AIWarsaw University of Technology)Dawid P{\l}udowski (Centre for Credible AIWarsaw University of Technology)Micha{\l} Jan W{\l}odarczyk (Centre for Credible AIWarsaw University of Technology)Luca Longo (University College Cork)Jianlong Zhou (University of Technology Sydney)Andreas Holzinger (Human-Centered AI Lab)Riccardo Guidotti (University of PisaISTI-CNR)Wojciech Samek (Technical University of BerlinBerlin Institute for the Foundations of Learning and Data)Przemys{\l}aw Biecek (Centre for Credible AIUniversity of Warsaw)
Sep 2026
Machine Learning
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.
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