# Recurrent Looped Transformer

Authors: Yifan Zhang, Jichen Feng, Shihan Qin, Sun-Yuan Kung, Mengdi Wang.

Passes each token's final decoder state to the next token, alongside a causal encoder representation. Tests whether this growing computation path supports state tracking on sequences longer than those seen during training.

First public: 2026-10-06.
ArXiv initial submission: 2026-10-06.

[Original paper](https://arxiv.org/abs/2610.07591) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.07591/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.07591/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.07591/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.07591/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.