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Modelpedia: A Catalog of Model Findings for the Meta-Science of AI — arXiv:2609.01090

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01090
License (as archived): CC0-1.0
Archived: 2026-10-17T06:52:17.880Z
SHA-256 of the archived text: aee6221c7e41a3c715f29ed4eb675fadd2d560b0715b98f77709bef53b63d602
Stable URL of this page: https://metraipolis.com/documento/dc_ddfd3c652f01

How to cite

Modelpedia: A Catalog of Model Findings for the Meta-Science of AI — arXiv:2609.01090 — arXiv (en), https://arxiv.org/abs/2609.01090 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ddfd3c652f01 (Metraipolis archive dc_ddfd3c652f01, sha256 aee6221c7e41a3c715f29ed4eb675fadd2d560b0715b98f77709bef53b63d602)

BibTeX

@misc{dcddfd3c652f01,
  title = {Modelpedia: A Catalog of Model Findings for the Meta-Science of AI — arXiv:2609.01090},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01090},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ddfd3c652f01; sha256 aee6221c7e41a3c715f29ed4eb675fadd2d560b0715b98f77709bef53b63d602},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01090. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.01090 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T06:41:55.834Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).