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cs.AI2026
Revisiting Over-refusal in Safety-aligned Large Language Models: A Representation and Optimization Perspective
Chao Chen, Pengyang Shao, Wei Qin +3
Over-refusal is a critical side effect of safety alignment in large language models (LLMs), causing harmless but superficially risky queries to be mistakenly rejected. Despite the…
cs.AI2026
ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
Xianhui Zhang, Jian Yu, Chengyu Xie +6
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-re…
cs.AI2026
Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways
Shuyi Miao, Wangjie Qiu, Pengyang Shao +4
Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Currently, mech…