# Register Tokens for Bounded-State Reasoning in Diffusion Language Models

Authors: Albert Ge, Chandan Singh, Yufan Zhuang, Xiaodong Liu, Jianfeng Gao, Frederic Sala.

Trains a diffusion language model to carry a fixed number of continuous register states between generated chunks. The model still writes text within each chunk, but clears earlier text and continues from the prompt and retained registers.

First public: 2026-09-14.
ArXiv initial submission: 2026-09-14.

[Original paper](https://arxiv.org/abs/2609.16372) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.16372/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.16372/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.16372/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.16372/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.