# Cord: Confidence-Anchored Order Residual Decoding for Masked Diffusion Language Models

Authors: Chengwei Zhou, Zhaoyan Jia, Massoud Pedram, Gourav Datta.

Learns which position a diffusion language model should reveal next while keeping the model fixed. A small policy adjusts confidence-based decoding to favor local continuation, distant starting points, or filling gaps, using whole-answer rewards from reasoning and code tasks.

First public: 2026-10-29.
ArXiv initial submission: 2026-10-29.

[Original paper](https://arxiv.org/abs/2610.39372) · [PDF](https://arxiv.org/pdf/2610.39372) · [HTML](https://arxiv.org/html/2610.39372v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.39372/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.39372/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.39372/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.39372/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. Some produce written chains of thought.

Reading-list version 0.12.0; literature checked through 2026-11-02.