# Safe at One Loop, Risky at Another: Aligning Safety Across Recurrent Depths in Looped Language Models

Authors: Yi Wang, Xiuyuan Qi, Dongqi Han, Dongsheng Li, Wenjie Wang.

Evaluates harmful-query and jailbreak behavior at different recurrent depths of looped language models. Proposes depth-specific control, state sharing, and joint supervision to align safety behavior across depths.

First public: 2026-10-07.
ArXiv initial submission: 2026-10-07.

[Original paper](https://arxiv.org/abs/2610.10625) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.10625/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.10625/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.10625/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.10625/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.