# DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning

Authors: Hengyu Fu, Tianyu Guo, Zixuan Wang, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Stuart Russell, Song Mei.

Feeds both token embeddings and hidden states back through repeated transformer layers to connect successive steps of multi-hop reasoning.

First public: 2026-07-01.
ArXiv initial submission: 2026-07-01.

[Original paper](https://arxiv.org/abs/2607.00341) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2607.00341/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2607.00341/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2607.00341/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2607.00341/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.
- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.

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.