runtime-monitoring

LabGuard introduces a framework that translates natural-language laboratory safety rules into executable runtime monitors for embodied agents, achieving a reduction in unsafe events from 39.5% to 23.8% while maintaining task success.

This paper studies how reasoning exchange among multiple AI agents can improve accuracy but also risk error propagation, proposing a runtime monitoring framework to prevent such propagation.

Hide-and-Seek is a framework that detects robot execution failures in VLA models by localizing failure-indicative actions through contrastive learning without step-level annotations, achieving state-of-the-art multi-task failure detection.

Proposes CPSS, a runtime safety mechanism that converts cumulative cost constraints into adaptive state-level thresholds for safe reinforcement learning in nonstationary environments, demonstrating reduced violations in highway merging scenarios.

This paper proposes reusable certified runtime monitors for past-time signal temporal logic (ptSTL) that use semantic latent representations to evaluate varying specifications without retraining, validated on pedestrian-crossroad and Waymo driving data.

This paper proposes Embedding Temporal Logic (ETL), a temporal logic that monitors perception-based autonomous systems directly in learned embedding spaces, enabling specification of high-level perceptual concepts and achieving strong empirical agreement with ground-truth semantics.