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

Reported by arXiv cs.CR (Yi Wang, Xiuyuan Qi, Dongqi Han, Dongsheng Li, Wenjie Wang)

Summary by Cyber Security News9 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.10625

arXiv:2610.10625v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) provide a parameter efficient approach to scaling model capabilities through repeated use of shared parameters across recurrent steps. Since each recurrent depth can be read out independently, a single LoopLM exposes a broader output space across inference depths, raising an important question: whether safety is preserved throughout recurrent computation.

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