Skip to content
arXiv· arXiv:2605.31073· Yan Wang, Zhixuan Chu, Zihao Xue, Zhen Bi, Bingyu Zhu, YueFeng Chen, Zeyu Yang, Jungang Lou, Longtao Huang, Ningyu Zhang, Kui Ren, Hui Xue·Indexed by this site · Originally published

ConsisGuard: aligning safety deliberation and policy enforcement in LLM guardrails

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails

AI summary

The ConsisGuard framework aligns safety deliberation and policy enforcement in reasoning-based LLM guardrails, bridging the "deliberation-execution gap" in which harmful intent identified in the model's reasoning is ultimately still judged safe. The framework uses policy-to-decision trajectory distillation and functional coupling alignment so that safety deliberation is grounded in safety policy and the final decision is derived from the deliberation. On prompt and response harmfulness detection benchmarks, ConsisGuard improves detection performance and reduces policy enforcement failures.

Read originalarxiv.org