Internalizing Safety Understanding in Large Reasoning Models via Verification
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 externally enforced compliance, optimizing models to detect malicious prompts rather than evaluating the safety of their own outputs. We argue that this approach remains largely behavioral: our empirical analysis reveals that ostensibly aligned models lack intrinsic safety understanding, often failing to verify their own response safety and remaining vulnerable to adversarial jailbreaks. To address this fundamental limitation, we propose Safety Internal (SInternal), a framework that internalizes safety specification...
| Authors | Yi Zhang, Yuxin Chen, Leheng Sheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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