GuardEn is an executable safeguard framework that transforms complex safety policies into atomic propositions via Safety‑Rule Compilation and models their composition as executable code. At inference, Scene‑Grounded Execution uses visual scene graphs to instantiate these propositions, allowing rule‑grounded and interpretable safety reasoning. Experiments on SafetyVisionBench show GuardEn improves F1 by an average of 9.8 points over the strongest baseline.
By Jisoo Kim (Sungkyunkwan University), TaeYoon Kwack (Sungkyunkwan University), Jinwoo Jang (Sungkyunkwan University), Honguk Woo (Sungkyunkwan University)
arXiv:2606. 03954v1 Announce Type: cross Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not.
By Hanjiang Hu, Yiyuan Pan, Jiaxing Li, Xusheng Luo, Alexander Robey, Na Li, Yebin Wang, Changliu Liu
As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not. We introduce the Vision-Language Embodied Safety Agent (VLESA), a framework that monitors human activities from egocentric video and triggers real-time safety interventions when dangerous actions are predicted.
arXiv:2606. 05290v1 Announce Type: cross Abstract: Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interventions for each new architecture.
By Tobia Poppi, Silvia Cappelletti, Sara Sarto, Florian Schiffers, Garin Kessler, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
arXiv:2610.09517v1 Announce Type: cross
Abstract: Frozen vision-language models increasingly provide safety signals for reinforcement learning. Their use assumes that similarity to language describin...
By Samuel Tetteh, Cody Fleming
Decoupled Safety Control (DSC) is a plug‑in algorithm that improves training‑free safety guidance for diffusion models by separating the unsafe condition from the intervention process. Unlike existing coupled safety‑control chains, DSC determines the intervention based on the current generation state, reducing residual unsafe content and preserving benign‑generation quality. Experiments on standard safety benchmarks demonstrate that DSC achieves stronger unsafe‑content suppression while maintaining efficient inference.
By Huilin Zhou, Ruoxi Cheng, Yuhang Wang, Yuming Liu, Minghao Sun, Ruolong Ma, Lan Zhang