# How UK AISI and EvalEval Are Making Benchmark Results Reproducible

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/how-uk-aisi-and-evaleval-are-making-benchmark-results-reproducible
> **Original Source**: [Hugging Face](https://huggingface.co/blog/evaleval-aisi)
> **Author**: Hugging Face Staff (undefined)
> **Published Date**: Tuesday, September 22, 2026
> **Category**: RESEARCH
> **Tags**: AI, Hugging Face, research

## Executive Summary
How UK AISI and EvalEval Are Making Benchmark Results Reproducible



## Full Coverage
Back to Articles a]:hidden"> How UK AISI and EvalEval Are Making Benchmark Results Reproducible Published September 22, 2026 Update on GitHub Upvote 28 +22 Avijit Ghosh evijit Follow evaleval Jenny Chim j-chim Follow evaleval Deep Joshi deeplumiere Follow evaleval Srishti srishtiy Follow evaleval Matt Kennedy wmmkennedy Follow evaleval Irene Solaiman irenesolaiman Follow evaleval Jessica McFadyen mcfadyen-aisi Follow ai-safety-institute Lynn Tan lynn-aisi Follow ai-safety-institute Coz coz-aisi Follow ai-safety-institute :last-child]:mb-0"> Why reproducible evaluation reporting matters What AISI is sharing Contribute to the shared mission About the EvalEval Coalition About the UK AI Security Institute Further reading The EvalEval Coalition is thrilled to share that the UK AI Security Institute (AISI) is using EvalEval's infrastructure to openly share evaluation results, supporting more reproducible and verifiable evaluation science

AISI and EvalEval have previously collaborated on research that began at a joint workshop alongside NeurIPS 2025 , and feedback from the Institute has helped shape the Every Eval Ever (EEE) schema . This next phase of the collaboration puts that shared infrastructure into practice

Why reproducible evaluation reporting matters As AI deployment accelerates, evaluations are becoming increasingly important sources of evidence about model and system performance. Yet results are reported across many formats, platforms, and outlets, often without enough information to reproduce them. Running the evaluations again may itself be prohibitively expensive

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