CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models

Masked diffusion language models (MDLMs) decode by repeatedly committing tokens to masked positions, but these commitments are usually irreversible. A token chosen under sparse, partial context is kept fixed, even when later context no longer supports it.