Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results

Jan Batzner; Sree Harsha Nelaturu; Damian Stachura; Anastassia Kornilova; Jon Crall; Tommaso Cerruti; Yanan Long; Yifan Mai; Sanchit Ahuja; Asaf Yehudai; Marek Šuppa; John P. Lalor; Oluwagbemike Olowe; Jatin Ganhotra; Brian H. Hu; Eliya Habba; Andrew M. Bean; Chang Liu; Sander Land; Steven Dillmann; Aniketh Garikaparthi; Elron Bandel; Saki Imai; James Edgell; Wm. Matthew Kennedy; Jenny Chim; Patrick Meusling; Asteria Kaeberlein; Venkata Ramachandra Karthik Chundi; Manasi Patwardhan; Martin Ku; Austin Meek; Leon Knauer; Brian Wingenroth; Srishti Yadav; Usman Gohar; Felix Friedrich; Michelle Lin; Jennifer Mickel; Arman Cohan; Stella Biderman; Irene Solaiman; Zeerak Talat; Anka Reuel; Mubashara Akhtar; Gjergji Kasneci; Avijit Ghosh; Leshem Choshen

arXiv · 2606.14516

Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results

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Abstract

AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First, results are saved in incompatible formats, scattered across leaderboards, papers, blog posts, evaluation harness logs, and custom repositories. Second, results are created by different evaluation frameworks, which produce divergent scores for nominally identical evaluations and record metadata inconsistently, hindering comparison, cross-community evaluation science, cost reduction, and reuse. We introduce Every Eval Ever, the first shared schema and community-crowdsourced repository for AI evaluation results. The schema standardizes how evaluations are represented in a unified, single JSON document. It is source-agnostic by design, ingesting results from evaluation harnesses and papers alike, and optionally stores per-instance outputs for fine-grained analysis. We contribute: (i) a community-governed metadata schema with a companion instance-level schema, the first standardization effort of its kind; (ii) automatic converters from popular formats, evaluation harnesses, and leaderboards to the unified schema; and (iii) a crowdsourced community database hosted on Hugging Face, currently spanning to date 22,235 models, 2,273 unique benchmarks, and 31 evaluation formats.

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Jan Batzner, Sree Harsha Nelaturu, Damian Stachura, Anastassia Kornilova, Jon Crall, Tommaso Cerruti, Yanan Long, Yifan Mai, Sanchit Ahuja, Asaf Yehudai, Marek Šuppa, John P. Lalor, Oluwagbemike Olowe, Jatin Ganhotra, Brian H. Hu, Eliya Habba, Andrew M. Bean, Chang Liu, Sander Land, Steven Dillmann, Aniketh Garikaparthi, Elron Bandel, Saki Imai, James Edgell, Wm. Matthew Kennedy, Jenny Chim, Patrick Meusling, Asteria Kaeberlein, Venkata Ramachandra Karthik Chundi, Manasi Patwardhan, Martin Ku, Austin Meek, Leon Knauer, Brian Wingenroth, Srishti Yadav, Usman Gohar, Felix Friedrich, Michelle Lin, Jennifer Mickel, Arman Cohan, Stella Biderman, Irene Solaiman, Zeerak Talat, Anka Reuel, Mubashara Akhtar, Gjergji Kasneci, Avijit Ghosh, Leshem Choshen. 2026-06-12. Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results. https://arxiv.org/abs/2606.14516

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