# Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models

Authors: Seyedarmin Azizi, Erfan Baghaei Potraghloo, Arya Fayyazi, Saeid Shokoufa, Massoud Pedram.

Studies how reducing recurrent depth changes refusal behavior in two looped language-model families. Uses probes and interventions to distinguish recognizing a harmful request from refusing it, and tests safety training across randomly selected depths.

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

[Original paper](https://arxiv.org/abs/2610.15218) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.15218/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.15218/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.15218/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.15218/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.