# Trading Depth for Time in Recurrent Transformers

Authors: Zeyi Huang, Xuehai He, Yelong Shen, Yong Jae Lee.

Inserts a hidden-state refinement step between consecutive text tokens in a recurrent transformer. Compares this extra thinking time with adding physical layers or transformer loops under matched computation budgets.

First public: 2026-09-18.
ArXiv initial submission: 2026-09-18.

[Original paper](https://arxiv.org/abs/2609.21605) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.21605/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.21605/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.21605/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.21605/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
- [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.