# LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning

Authors: Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki, Navdeep Jaitly, Yi-An Ma, Lianhui Qin.

Uses a variational autoencoder to compress textual reasoning steps into blocks of continuous states. A diffusion model repeatedly revises each block before generating the next, then produces a textual answer; diversity guidance encourages alternative reasoning trajectories.

First public: 2025-10-06.
ArXiv initial submission: 2025-10-06.

[Original paper](https://arxiv.org/abs/2510.04573) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2510.04573/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2510.04573/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2510.04573/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2510.04573/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.