SOURCE-LINKED INTELLIGENCE
Visual Compliance via Executable Safety Rule Entailment
Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns. However, as risk patterns continue to evolve and safety rules become more complex, existing training-based end-to-end safeguards face persistent challenges in adaptability and explainable reasoning over complex safety rules. To address these challenges, we propose GuardEn (Guarding by Safety Rule Entailment), an executable safeguard framework that decomposes safety policies into atomic propositions through Safety-Rule Compilation, modeling thei
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T08:53:09.000Z
First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.