# Learning to Couple: Context-Adaptive Joint Denoising for Diffusion Language Models

Authors: Zilei Shao, Ian Li, Heng Zhao, Kewei Lian, Guy Van den Broeck, Zhe Zeng, Anji Liu.

Combines a diffusion language model with a probabilistic circuit that models dependencies among tokens proposed together. Learns context-dependent token coupling and uses it to decide which discrete tokens to commit in parallel on math and code tasks.

First public: 2026-10-19.
ArXiv initial submission: 2026-10-19.

[Original paper](https://arxiv.org/abs/2610.25723) · [PDF](https://arxiv.org/pdf/2610.25723) · [HTML](https://arxiv.org/html/2610.25723v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.25723/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.25723/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.25723/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.25723/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 0.11.0; literature checked through 2026-10-26.