The EvalEval Coalition is excited to announce a collaborative milestone with the UK AI Security Institute (AISI): the utilization of EvalEval’s advanced infrastructure to openly share evaluation results. This initiative underscores a commitment to fostering reproducible and verifiable evaluation science, a crucial pillar in the rapidly advancing field of artificial intelligence (AI).
Why Reproducible Evaluation Reporting Matters
In recent years, the deployment of AI technologies has surged. With this acceleration, the importance of rigorous evaluations as reliable evidence of model and system performance has become clear. Unfortunately, evaluation results are often reported in a diverse array of formats, platforms, and outlets, leading to inconsistencies. When insufficiently detailed, these reports can hinder replication efforts, and recreating evaluations can be prohibitively expensive.
EvalEval’s mission focuses on reshaping this fragmented ecosystem. The newly developed reporting schema, Every Eval Ever (EEE), and the open platform, Evaluation Cards, collectively aim to standardize evaluation results, making it easier for researchers and practitioners to interpret and apply findings effectively. AISI’s existing initiatives, like OptStop for efficiency and HiBayES for statistical rigor, complement EvalEval’s contributions by standardizing practices across various evaluation domains.
What AISI Is Sharing
One of the central tenets of meaningful AI evaluation is transcript-level transparency. This transparency serves not only to ensure reproducibility but also to facilitate robust analysis and diagnosis. As the collaboration moves forward, AISI is making its evaluation methods and findings available through Evaluation Cards, enhancing public accessibility and accountability.
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The current package includes verified results and contextual setup information for five significant benchmarks utilized in their main experimental analyses:
- HealthBench
- FrontierMath
- Humanity’s Last Exam
- SWE-Bench Pro
- Terminal-Bench 2.0
Moreover, AISI is sharing results from six cutting-edge models: Claude Opus 4, 4.5, 4.6, GPT-5, GPT-5.2, and GPT-5.4. Additional cyber evaluations—Cyber CTFs and The Last Ones—are also included, employing a partially overlapping set of models. This release coincides with AISI’s paper, How Inference Compute Shapes Frontier LLM Evaluation, an insightful examination of how benchmark performance correlates with inference-time compute and evaluation protocols.
This visualization illustrates how performance on Humanity’s Last Exam varies with different evaluation protocols and inference compute setups—highlighting model behavior as more tokens are used.
The transparency of setup information enables researchers to scrutinize individual studies and facilitate cross-comparison across the industry. In environments where many reports lack essential details, AISI’s approach provides a robust reference point for understanding evaluation context. Such insights equip researchers with a clearer understanding of how different setup choices influence reported performance metrics. As more evaluators adopt the EEE model, it can significantly improve the rigor of meta-research, fostering an environment where knowledge can be built collaboratively.
This visualization presents Terminal-Bench 2.0 results in conjunction with other evaluations for the same models under varying setups, providing a contextual overview of performance.
Contribute to the Shared Mission
The success of this initiative is not solely reliant on the partnership between AISI and EvalEval. The broader research community is encouraged to contribute to this shared mission. Your insights into reproducible evaluation science help pave the way for a more transparent AI future, promoting practices that benefit the entire ecosystem.
About the EvalEval Coalition
The EvalEval Coalition stands as a pioneering research community devoted to establishing scientifically grounded practices and robust infrastructure for AI evaluation. Its core aim is to enhance evaluation science, address discrepancies in documenting evaluation applicability, and broaden the scope of impactful metrics for scientific research and policy analysis.
Flagship projects like Every Eval Ever and Evaluation Cards form the backbone of this initiative. These platforms streamline access to evaluation results, integrating benchmark metadata and model documentation into cohesive and interpretable records, thereby bridging the gaps that have traditionally hindered cross-study comparisons.
About the UK AI Security Institute
The UK AI Security Institute operates within the UK’s Department for Science, Innovation, and Technology. Its primary mission revolves around equipping governments with comprehensive scientific insights into the complexities and risks associated with advanced AI technologies. Through rigorous research and substantial infrastructure development efforts, AISI aims to better understand AI capabilities, impacts, and safeguarding measures, providing essential guidance for policymaking.
Further Reading
For anyone interested in engaging with this exciting domain of AI evaluation and infrastructure development, staying informed through continual research, collaboration, and innovation is key. The pathways set forth by AISI and EvalEval mark significant advancements towards a more transparent and effective evaluation science landscape, one that is eager for contributions from all quarters. Stay tuned for additional insights and developments in this transformative field.
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