# ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces

Authors: Xin Xu, Tong Yu, Xiang Chen, Haoliang Wang, Julian McAuley, Saayan Mitra.

Routes intermediate computation between latent and discrete representations to trade off reasoning quality, readability, and token cost.

First public: 2026-02-12.
ArXiv initial submission: 2026-02-12.

[Original paper](https://arxiv.org/abs/2602.11683) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2602.11683/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2602.11683/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2602.11683/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2602.11683/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
- [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.