# Confidence-Drift Remasking

Masked diffusion language models decode block by block and normally never revisit a committed token, so choices made under sparse context stay fixed. CoDR compares each committed token's confidence at commit time with its confidence under denser later context, remasks only those whose confidence dropped past a threshold, and lets the original sampler refill them, estimating drift for all committed positions in k forward passes via k-partition probing. Across LLaDA-8B and Dream-7B, three base samplers and four reasoning and coding tasks, adding CoDR raises the four-task average accuracy of every evaluated model–sampler configuration, by 0.85, 1.06 and 1.44 points for top1, fd and eb on LLaDA.

Masked diffusion language models decode block by block and normally never revisit a committed token, so choices made under sparse context stay fixed. CoDR compares each committed token's confidence at commit time with its confidence under denser later context, remasks only those whose confidence dropped past a threshold, and lets the original sampler refill them, estimating drift for all committed positions in k forward passes via k-partition probing. Across LLaDA-8B and Dream-7B, three base samplers and four reasoning and coding tasks, adding CoDR raises the four-task average accuracy of every evaluated model–sampler configuration, by 0.85, 1.06 and 1.44 points for top1, fd and eb on LLaDA.