# Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning

Authors: Jingjie Qiu, Sheldon Yu, Xunyi Jiang, Jiajin Liu, Zihan Huang, Zhendong Chu, Tong Yu, Julian McAuley, Junda Wu.

Uses chain deletion, content replacement, probes, and trajectory analysis across 21 model-task configurations. Separates what continuous states encode from whether the model uses their content, finding that dependence on the chain varies with task and training recipe.

First public: 2026-10-15.
ArXiv initial submission: 2026-10-15.

[Original paper](https://arxiv.org/abs/2610.21182) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.21182/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.21182/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.21182/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.21182/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.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.