# Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation

Authors: Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, Se-Young Yun.

Learns token-level routing across recursive depths so different positions can receive different amounts of internal computation.

First public: 2025-07-14.
ArXiv initial submission: 2025-07-14.

[Original paper](https://arxiv.org/abs/2507.10524) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2507.10524/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2507.10524/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2507.10524/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2507.10524/record.json)
[Code](https://github.com/raymin0223/mixture_of_recursions)

## 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.