Piloting the world's first double-blind AI evaluations
August 27, 2026 Responsibility & Safety
William Isaac, Sol Messing and Kristian Lum
Building trust in proprietary model benchmarks using cryptographically secure environments
Imagine a student is set to take a high-stakes exam. If they accidentally peek at the test questions in advance, achieving a perfect score is influenced by this knowledge, making it a meaningless accomplishment. To truly measure what they know, they must have no visibility of the test questions until it's time to take the exam. That is the exact challenge the industry faces when evaluating advanced AI models. If a model has already seen the test questions - a problem known as benchmark contamination - the results can only be trusted to an extent.
Today, we’re introducing the world’s first double-blind evaluation of a proprietary, frontier class AI model,which keeps external evaluations confined to a cryptographic “box” where they can’t be used by models later...
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