# SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers

Authors: Shaowen Wang, Ge Zhang, Kairong Luo, Yuhao Wu, Shaofan Liu, Jiaheng Liu, Wenhao Huang, Shen Yan, Jian Li.

Loops the middle half of a mixture-of-experts transformer twice while matching an unlooped baseline on computation, parameter count, and KV-cache size. Studies how the comparison changes with model scale and examines the attention patterns produced by revisiting layers.

First public: 2026-09-01.
ArXiv initial submission: 2026-09-01.

[Original paper](https://arxiv.org/abs/2609.01343) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.01343/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.01343/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.01343/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.01343/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.
- [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.