arXiv AI
Aug 5
Shielding for Higher-Order Safety
arXiv:2608. 03662v1 Announce Type: new Abstract: Safety shields are runtime enforcement mechanisms that restrict the actions of a controller to guarantee safety.
Efficient Dynamic Shielding for Parametric Safety Specifications
arXiv:2505. 22104v2 Announce Type: replace Abstract: Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems.
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arXiv AI
Jun 2
Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
arXiv:2606. 02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate.
arXiv AI
Jul 15
Real-Time Model Checking for Closed-Loop Robot Reactive Planning
arXiv:2508. 19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i.
arXiv AI
Jun 12
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
arXiv:2606. 13621v1 Announce Type: new Abstract: Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions.
arXiv Machine Learning
Jul 7
CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environments
arXiv:2603. 06921v2 Announce Type: replace-cross Abstract: Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments.
arXiv AI
Jun 2
Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
arXiv:2606. 00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions.
arXiv Machine Learning
Jun 9
Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
arXiv:2606. 09749v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks.
Hugging Face Trending Papers
Jun 11
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the wrong product.
arXiv Machine Learning
Jun 25
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
arXiv AI
3d ago
Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes
arXiv:2608. 14481v1 Announce Type: cross Abstract: As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challenge.
arXiv Machine Learning
Jul 14
Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation
arXiv:2607. 10014v1 Announce Type: cross Abstract: Learning-based separation assurance for small Unmanned Aircraft Systems (sUAS) achieves near-zero collision rates in simulation, but assumes accurate position and velocity information from Global Navigation Satellite Systems (GNSS).
arXiv AI
Jul 2
Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems
arXiv:2607. 00334v1 Announce Type: new Abstract: Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous human oversight: safety violations from unverified actions, behavioral instability from unconstrained loops, and continuity loss from unhandled error states.