# StateLoop: Persistent State for Looped Transformers

Authors: Feichen Song, Boyi Zeng, He Li, Shixiang Song, Yiqin Hao, Zitong Wang, Yizhou Yang, Yifan Liu, Ziwei He, Zhouhan Lin.

Gives each token a persistent state alongside the output of a shared transformer backbone. Learned gates control what the state retains across loops, and separate readouts feed it into later loops and the final prediction.

First public: 2026-10-26.
ArXiv initial submission: 2026-10-26.

[Original paper](https://arxiv.org/abs/2610.35544) · [PDF](https://arxiv.org/pdf/2610.35544) · [HTML](https://arxiv.org/html/2610.35544v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.35544/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.35544/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.35544/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.35544/record.json)

## Approaches

- [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.

Reading-list version 0.12.0; literature checked through 2026-11-02.