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Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment
2026Yavuz Faruk Bakman, D. Yaldiz et al.
[2]
RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards
2025Jingnan Zheng, Xiangtian Ji et al.
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Safety Alignment Can Be Not Superficial With Explicit Safety Signals
2025Jianwei Li, Jungeum Kim
[4]
Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression
2025Jingyu Peng, Maolin Wang et al.
[5]
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
2025Zihan Guan, Mengxuan Hu et al.
[6]
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2025Marah Abdin, Sahaj Agarwal et al.
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2025Yutao Mou, Yuxiao Luo et al.
[8]
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
2025Qiguang Chen, Libo Qin et al.
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2025Yuyou Zhang, Miao Li et al.
[10]
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2025Fengqing Jiang, Zhangchen Xu et al.
[11]
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2025Fengwei Teng, Zhaoyang Yu et al.
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2025Ang Li, Yichuan Mo et al.
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2025Yichi Zhang, Siyuan Zhang et al.
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2025Xinzhe Li
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2025Adam Suma, Samuel Dauncey
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Showing 20 of 56 references

Founder's Pitch

"Develop a reasoning-enhanced safety alignment method for LLMs to better handle deceptive jailbreak attacks."

LLM Safety AlignmentScore: 6View PDF ↗

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5

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7.5

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2.5

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