# INTACT: Inducing Latent Reasoning on a Frozen Backbone

Authors: Kutay Tire, Atula Tejaswi, Haris Vikalo, Sujay Sanghavi.

Trains a small encoder to turn a frozen model's hidden states into successive blocks of continuous thoughts. After this latent prefix, the frozen model writes its remaining reasoning and answer; the encoder learns through distillation and answer-correctness rewards.

First public: 2026-10-14.
ArXiv initial submission: 2026-10-14.

[Original paper](https://arxiv.org/abs/2610.19568) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.19568/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.19568/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.19568/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.19568/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.