# Structured Sparse Memory for Recurrent Reasoning

Authors: Zixuan Zhao, Samuel Wheeler, Neil Getty, Xiaotian Duan, Rick Stevens, Fangfang Xia.

Combines a recurrent reasoner with sparse, compositional task embeddings for ARC puzzles. Studies how task-memory size, recurrent depth, and training horizon affect performance.

First public: 2026-09-27.
ArXiv initial submission: 2026-09-27.

[Original paper](https://arxiv.org/abs/2609.33270) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.33270/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.33270/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.33270/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.33270/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.