# CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression

Authors: Zhaoyang Wei, Bowen Jiang, Yanchao Hao, Wenchao Ding, Zheng Wei, Shaocheng Wu, Zhenjun Han, Jianbin Jiao.

Compresses images of written derivations into continuous states, training both the answers they support and the reasoning that can be recovered from them. Gradually shifts from generating latent blocks in parallel to generating them in sequence.

First public: 2026-10-05.
ArXiv initial submission: 2026-10-05.

[Original paper](https://arxiv.org/abs/2610.07175) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.07175/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.07175/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.07175/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.07175/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.

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.