Class Adaptive Conformal Training adaptively shapes class-conditional prediction sets via augmented Lagrangian optimization without distributional assumptions, yielding smaller sets with valid coverage.
A mutualized backbone-equated protocol reveals that sparse internal diffusion snapshots at low noise detect OOD better than full denoising with minimal heads, via encoder-decoder complementarity and diagonal-score separation.