# What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling

Authors: Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov, Inessa Fedorova, Dmitry Bocharov, Ivan Oseledets, Valerii Ternovskii.

Compares recursive reasoning models under a common training and evaluation setup. Isolates the effects of gradient propagation, batch size, state updates, and hierarchical structure on algorithmic tasks and generalization.

First public: 2026-09-30.
ArXiv initial submission: 2026-09-30.

[Original paper](https://arxiv.org/abs/2609.39967) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.39967/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.39967/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.39967/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.39967/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.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.