# LooM: Loop-Invariant Memory for Causal Encoder-Decoder Language Models

Authors: Lele Yang, Muxi Diao, Zhonghao Yan, Jiankang Chen, Bin Wen, Tingting Gao, Wentao Zhang, Kongming Liang, Zhanyu Ma, Weiran Xu.

Reuses a fixed memory bank while decoder layers repeat their reads. Each token writes its local cache once, allowing different recurrent-depth budgets without increasing the stored cache size.

First public: 2026-11-04.
ArXiv initial submission: 2026-11-04.

[Original paper](https://arxiv.org/abs/2611.06835) · [PDF](https://arxiv.org/pdf/2611.06835) · [HTML](https://arxiv.org/html/2611.06835v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.06835/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.06835/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.06835/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.06835/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.