# Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding

Authors: Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo.

Trains one model to compress written reasoning into special-token representations, then trains another to answer using those representations.

First public: 2024-09-13.
ArXiv initial submission: 2024-09-13.

[Original paper](https://arxiv.org/abs/2409.08561) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2409.08561/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2409.08561/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2409.08561/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2409.08561/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.