distributional-calibration

SAGE proposes a group-level uncertainty target that constructs an answer-conditioned uncertainty geometry over sampled responses to improve verbal uncertainty alignment in LLMs, and introduces GUPO for training. Experiments across reasoning tasks show improved uncertainty ranking and reduced overconfidence.