# T²MLR: Transformer with Temporal Middle-Layer Recurrence

Authors: Ziyang Cai, Xingyu Zhu, Yihe Dong, Yinghui He, Sanjeev Arora.

Carries a cached middle-layer state from one token position into an earlier layer at the next position, providing localized temporal recurrence that can also be retrofitted into pretrained transformers.

First public: 2026-07-16.
ArXiv initial submission: 2026-07-16.

[Original paper](https://arxiv.org/abs/2607.15178) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2607.15178/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2607.15178/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2607.15178/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2607.15178/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.