Evaluating Frontier Models: A Practical Guide
How institutions and technical teams should test large, general-purpose AI models — rigorously, adversarially, and continuously — before and after they are deployed.
Listen
Evaluating Frontier Models: Rigor, Risk, and the Instrument Panel
A ~14-minute audio interview about the report with Dr. Krzysztof Pietroszek, President of FAIR Labs. Produced with www.allais.com.
Summary
The bottom line for decision-makers
You cannot manage what you have not measured, and you cannot trust a measurement you did not design to resist gaming. Evaluating a frontier model well means asking the right questions—what can it do, where does it fail, can it be pushed into harm, does it know what it does not know—and answering them with methods robust to contamination, to spec-gaming, and to the model's own fluency. This report supplies a taxonomy for organizing those questions, protocols for answering them adversarially, and a pipeline for turning the answers into a defensible deploy-or-hold decision that is then re-opened, on schedule, for as long as the model is in service.
How institutions and technical teams should test large, general-purpose AI models — rigorously, adversarially, and continuously — before and after they are deployed.
Key findings
- Measure constructs, not scores. Decide what real-world capability or hazard a test is meant to stand for, then defend the claim that the test measures it. A number without a validated construct behind it is theater.
- Assume the benchmark is contaminated. Treat public benchmark scores as an upper bound corrupted by training-data leakage. Weight held-out, private, and freshly authored evaluations far more heavily in any decision that matters.
How to cite this report
Evaluating Frontier Models: A Practical Guide. FAIR Labs Technical Analysis. Fair Artificial Intelligence Research Labs, 2026. Available at https://fairlabs.ai/research/evaluating-frontier-models
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