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

Authors: Yue Wu, Qinghe Zhang, Yu Zhang, Jian Huang.

Checks whether tokens committed earlier in diffusion decoding become less plausible as context fills in. CoDR remasks and regenerates those positions, testing targeted revision against extra-compute controls on reasoning and code tasks.

First public: 2026-09-28.
ArXiv initial submission: 2026-09-28.

[Original paper](https://arxiv.org/abs/2610.08833) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.08833/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.08833/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.08833/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.08833/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.