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πŸ€”Consider the scenario LLaMA 65B scored 0.637 on HELM's MMLU LLaMA 65B scored 0.488 on lm-eval-harness's MMLU Same model. Same benchmark name. Different prompts, settings, extraction methods. πŸ’‘Which score is right? Both? Neither? We can't compare. 🀷

This has real costs! πŸ”¬ Signal buried in noise, can't tell if differences reflect model capability or just setup πŸ“¦ Evaluation debt piles up silently across the ecosystem πŸ”ŽRedundant re-runs of expensive evaluations 🌟That's where Every Eval Ever comes

What we built: πŸ“‹ Metadata schema for cross-framework comparison πŸ”§ Validation via Hugging Face Jobs πŸ”Œ Converters (Inspect AI, HELM, lm-eval-harness) πŸ“Š Community repo organized by benchmark/model/run ✨ Captures scores AND context: settings, prompts, example-level data

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Thankful to our partners for the feedback: CAISI, AIEleuther, Huggingface, NomaSecurity, TrustibleAI, InspectAI, Meridian, AVERI, CIP, Stanford HELM, Weizenbaum, Evidence Prime, MIT, TUM, IBM Research 🀝