# Compressed Chain of Thought: Efficient Reasoning Through Dense Representations

Authors: Jeffrey Cheng, Benjamin Van Durme.

Compresses verbose reasoning traces into shorter dense representations and evaluates the trade-off between reasoning quality and generated length.

First public: 2024-12-17.
ArXiv initial submission: 2024-12-17.

[Original paper](https://arxiv.org/abs/2412.13171) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2412.13171/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2412.13171/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2412.13171/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2412.13171/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.