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cs.AI2026

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories

Dongcheng Zhang, Yi Zhang, Yuxin Chen +3

Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe reasoning trajectories under…

cs.AI2026

Internalizing Safety Understanding in Large Reasoning Models via Verification

Yi Zhang, Yuxin Chen, Leheng Sheng +4

While explicit Chain-of-Thought (CoT) empowers large reasoning models (LRMs), it enables the generation of riskier final answers. Current alignment paradigms primarily rely on exte…

cs.AI2026

Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking

Zhida He, Xiaoyu Wen, Han Qi +7

Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based multi-turn jailbreak method…

cs.AI2026

MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety

Xiaoyu Wen, Zhida He, Han Qi +7

Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due to their \textbf{reliance on s…

cs.AI2026

Epistemic Traps: Rational Misalignment Driven by Model Misspecification

Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4

The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral pathologies including sycophancy, ha…