DeepMind's double-blind evals stop models from peeking at the test
Benchmark contamination quietly poisons AI scores: if a model has seen the test questions, its numbers prove nothing. DeepMind just ran the first double-blind evaluation of a proprietary frontier model, sealing external test prompts in a cryptographic enclave that even Google can't read. A Gemini Flash Lite model took confidential benchmarks alongside the Singapore AI Safety Institute, OpenMined, AVERI and MLCommons. For the first time, evaluators' prompts and the provider's weights stay private at once.