UK AISI shares benchmark results via EvalEval's Evaluation Cards

The collaboration between the UK AI Security Institute and the EvalEval Coalition began at a joint workshop alongside NeurIPS 2025, and feedback from the Institute helped shape the Every Eval Ever schema. This new phase moves that shared infrastructure into practice, with AISI releasing evaluation data through Evaluation Cards. The release includes results for five benchmarks and six frontier models, alongside a paper titled How Inference Compute Shapes Frontier LLM Evaluation, which examines how benchmark performance depends on inference-time compute and evaluation protocol. AISI has also worked on making evaluation more efficient through OptStop and more statistically rigorous through HiBayES.

The problem both groups are addressing is that evaluation results are reported across many formats and platforms, often without enough information to reproduce them, and re-running evaluations can be prohibitively expensive. By providing verified results, context, and configuration information, this release may offer a reference point for interpreting evaluations in context, for example by helping researchers understand how setup choices influence reported performance. As more evaluators adopt the Every Eval Ever schema, open comparisons like these could support broader meta-research.

The stakes hinge on whether other evaluators follow suit in sharing setup details, since the value of such reference points depends on broad participation. For now, researchers and policy analysts gain a concrete set of verified data to examine individual studies more closely and compare findings across the wider ecosystem.