AI RESEARCH
ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails
arXiv CS.CL
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ArXi:2605.31073v1 Announce Type: new Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do not always lead to faithful enforcement: a model may recognize a harmful intent in its reasoning but still predict a safe label, or issue an unsafe decision without policy-grounded justification. We identify this safety-critical failure mode as the deliberation-to-enforcement gap.