# Large Language Continuous Diffusion Models

Authors: Zhihan Yang, Wei Guo, Jean-Marie Lemercier, Simon Welker, Yonggan Fu, Mohammad Mahdi Kamani, Sajad Norouzi, Julius Berner, Tomas Geffner, Karsten Kreis, Yongxin Chen, Molei Tao, John Thickstun, Pavlo Molchanov, Ante Jukić, Arash Vahdat, Morteza Mardani.

Sigma denoises learned continuous token embeddings in blocks, starting from autoregressive pretrained weights. Studies how guidance and temperature affect mathematical reasoning, coding, output diversity, and performance when fewer refinement steps are used.

First public: 2026-10-02.
ArXiv initial submission: 2026-10-02.

[Original paper](https://arxiv.org/abs/2610.02665) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.02665/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.02665/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.02665/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.02665/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.
- [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.