Most CBRNE-adjacent evals test whether a model can answer textbook questions, but real uplift comes from tacit knowledge, troubleshooting, and lab technique — things benchmarks barely touch. A model that scores 90% on a bio quiz might still be useless for actual harm, or vice versa. Are we optimizing evals for measurability over meaning? What would a genuinely decision-relevant capability eval look like?