# Going Deeper With Memories

Authors: Afshin Khadangi.

Compresses byte sequences into a hierarchy while preserving exact byte information in a separate stream. A shared memory module refines the deepest hidden representation before a causal decoder restores byte-level predictions.

First public: 2026-11-03.
ArXiv initial submission: 2026-11-03.

[Original paper](https://arxiv.org/abs/2611.05255) · [PDF](https://arxiv.org/pdf/2611.05255) · [HTML](https://arxiv.org/html/2611.05255v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.05255/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.05255/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.05255/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.05255/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 0.15.2; literature checked through 2026-11-03.