# One Model to Loop Them All: Elastic Latent Recurrence for Language Models

Authors: Faisal Ladhak, Shantanu Acharya, Santiago Akle Serano, Simeng Sun, Boris Ginsburg, Krishna C Puvvada.

Trains one language model to operate at different loop counts by mixing depths during training and conditioning each pass on its index and budget. A learned gate controls each recurrent update.

First public: 2026-10-08.
ArXiv initial submission: 2026-10-08.

[Original paper](https://arxiv.org/abs/2610.12582) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.12582/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.12582/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.12582/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.12582/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.