# D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding

Authors: Baoyou Chen, Ruiqiao Mei, Guancong Lin, Tao Song, Weihao Yuan, Qun Liu, Siyu Zhu.

Trains a standalone diffusion model to draft several tokens at once, then prunes it using verification acceptance as the criterion. Prepares continuation branches while the target model checks the current draft, reusing a branch when its prefix matches.

First public: 2026-11-04.
ArXiv initial submission: 2026-11-04.

[Original paper](https://arxiv.org/abs/2611.06425) · [PDF](https://arxiv.org/pdf/2611.06425) · [HTML](https://arxiv.org/html/2611.06425v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.06425/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.06425/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.06425/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.06425/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 0.15.2; literature checked through 2026-11-03.