ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
Ensuring the safety of reasoning large language models (LLMs) across languages is essential for their reliable deployment. However, when exposed to jailbreak attacks in non-high-resource languages, these models may generate unsafe responses even when their reasoning traces identify safety risks. To address this issue, we propose aligning cross-lingual thoughts and responses (ACTR), a framework that improves multilingual safety alignment by strengthening the use of existing safety reasoning. Specifically, we first present the think gap score (TGS) to compare the normalized contributions of reasoning traces to attention outputs during response generation across languages, and use reasoning-...
| Authors | Xianhui Zhang, Jian Yu, Chengyu Xie et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 September 2026 |
| Last updated at source | 30 September 2026 |
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