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Meta AI Research

Announcing the winners of the 2022 Foundational Integrity Research request for proposals

In September, Meta launched the Foundational Integrity Research request for proposals. Today, we announce the winners of this award.

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OpenAI Blog
May 10, 2021

OpenAI Scholars 2021: Final projects

We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI.

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arXiv AI
Jun 30

Research Entity Extraction and Topic Detection from UKRI Grant Proposals

arXiv:2606. 30304v1 Announce Type: cross Abstract: This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke algorithm, DSIT-Taxonomies, for extracting and classifying research entities from funding proposals.

By Xingran Ruan, Angelo Salatino, Rosa Filgueira, Kara Moraw, Alexandru Marcoci, Gemma Derrick, Sarah Callaghan
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arXiv AI
Jul 15

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

arXiv:2607. 12113v1 Announce Type: cross Abstract: One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions.

By Rafael Ferreira da Silva, Milad Abolhasani, Peter Beaucage, Laura Biven, Michael Bussmann, Kyle Chard, Ryan Coffee, Stephen DeWitt, Sagar Dolas, Carrie Eckert, David Elbert, Ian Foster, Tirthankar Ghosal, Anna Giannakou, Tom Gibbs, Leslie Hamilton, Glenn Lockwood, Theresa Mayer, Ben Mintz, Raffi Nazikian, Sal Nimer, Amanda Randles, Woong Shin, Sreenivas Rangan Sukumar, Fr\'ed\'eric Suter, Mitra Taheri, Michela Taufer, Draguna Vrabie
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arXiv Machine Learning
Sep 2

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

Modelpedia is an automated, LLM-assisted framework that extracts and organizes findings about AI models from published papers into a searchable public catalog. It links each finding to the relevant model, dataset, method, and concept, and has already extracted over a thousand findings from ICLR 2024 and 2025 papers. The authors invite the community to explore, contribute to, and build on this open catalog, positioning model findings as a shared foundation for the meta‑science of AI.

By Franciszek Bernat (Centre for Credible AI, Warsaw University of Technology), Dawid P{\l}udowski (Centre for Credible AI, Warsaw University of Technology), Micha{\l} Jan W{\l}odarczyk (Centre for Credible AI, Warsaw University of Technology), Luca Longo (University College Cork), Jianlong Zhou (University of Technology Sydney), Andreas Holzinger (Human-Centered AI Lab), Riccardo Guidotti (University of Pisa, ISTI-CNR), Wojciech Samek (Technical University of Berlin, Berlin Institute for the Foundations of Learning and Data), Przemys{\l}aw Biecek (Centre for Credible AI, University of Warsaw)
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