# Anchors Enhance Controllability and Reasoning of Diffusion Language Models

Authors: Feiyu Qu, Zheng Huang, Andrea Zanette.

Trains an anchor generator to choose a short text fragment and its position in a frozen diffusion model's output. These visible control tokens guide subsequent denoising, with rewards for correct answers and satisfied constraints.

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

[Original paper](https://arxiv.org/abs/2610.15097) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.15097/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.15097/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.15097/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.15097/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. 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.