New Research: Confident Models Go Miscalibrated as Knowledge Grows, Persistent Calibration Has Big Headroom
EliasEskin · x · 2026-10-01
Researchers introduce "persistent calibration": a trustworthy continually learning model should track its own knowledge growth without repeated re-calibration.
- They evaluate confidence estimators trained on earlier checkpoints of open models and test whether they generalize to later checkpoints whose knowledge has changed
- Calibration is measured on knowledge contrast sets: questions one checkpoint answers correctly and another incorrectly
- Key finding: even confidence estimators that look well-calibrated at a single point in time leave substantial headroom; both inference-time and fine-tuning methods fall short
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