# How to Loop MoE: Flatten the Experts, Untie the Attention

Authors: Shouren Wang, Chuang Ma, Mohsen Hariri, Debargha Ganguly, Wang Yang, Xiaoqing Tong, Qianying Liu, Xiaotian Han, Vipin Chaudhary.

Builds a looped mixture-of-experts model by pooling experts across layers and revisiting them. Shares experts and routing across passes while giving each pass its own attention parameters.

First public: 2026-09-28.
ArXiv initial submission: 2026-09-28.

[Original paper](https://arxiv.org/abs/2609.35751) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.35751/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.35751/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.35751/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.35751/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.

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.