Yifan Wu

CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference

This work proposes CORA-Diff, a training-free method that preserves the original transfer rule and applies confidence-and-persistence gating only to positions that rule leaves unresolved, and shows that native confidence and persistence enable reliable residual acceptance, reducing repeated denoising computation while preserving task quality.