# Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals

Authors: Pengxiang Li, Dilxat Muhtar, Di He, Guinan Su, Lu Yin, Shiwei Liu.

Adds a learned running summary of previous recurrent states so that each update can retain useful earlier work. Uses content-based weighting and temporal decay with constant aggregation memory, and tests whether puzzle-solving improves over much longer inference runs.

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

[Original paper](https://arxiv.org/abs/2610.11570) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.11570/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.11570/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.11570/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.11570/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.