# P2N: Reusing Deep Representations for Greater Effective Depth

Authors: Yiqin Hao, Boyi Zeng, Zitong Wang, He Li, Shixiang Song, Feichen Song, Yifan Liu, Ran Tao, Bryan Dai, Zhouhan Lin.

Feeds the previous token’s deep hidden state into earlier layers for the next token. A shared middle block and parallel Jacobi updates train this recurrence without serializing all training tokens.

First public: 2026-10-18.
ArXiv initial submission: 2026-10-18.

[Original paper](https://arxiv.org/abs/2610.25076) · [PDF](https://arxiv.org/pdf/2610.25076) · [HTML](https://arxiv.org/html/2610.25076v2) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.25076/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.25076/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.25076/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.25076/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.

Reading-list version 0.12.0; literature checked through 2026-11-02.