arXiv AI By Zhipeng Cao, Taoran Wu, Weiru Cui, Luke Ong, Bai Xue

Anytime-Valid Runtime Monitoring of Learned Barrier Certificates

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arXiv Machine Learning
Sep 22

Information-Geometric First-Passage Monitoring of Distributional Stability in Stochastic Systems

The paper presents a runtime monitoring framework for stochastic systems that distinguishes normal distributional relaxation from regime changes while limiting false alarms. It combines relative‑entropy dissipation, information geometry, and sequential inference within a bounded first‑passage architecture, employing Gaussian window surrogates, covariance shrinkage, and conformal ranking aggregated by a mixture power‑martingale. Validation on Ornstein–Uhlenbeck dynamics and network intrusion datasets (NSL‑KDD, UNSW‑NB15) shows high detection rates with low false positives, highlighting calibration transport as a key deployment challenge.

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arXiv AI
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Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination

The paper introduces a finite‑sample probabilistic safety certification framework for black‑box AI decision models used in closed‑loop grid operation. It transforms the AI‑grid evaluation into a binary unsafe outcome under a safety specification and applies exact binomial inference to provide a tight one‑sided upper bound on the unsafe operation probability, using held‑out calibration scenarios. The framework also incorporates physically interpretable sample‑space adversarial attacks to address distribution shifts and is validated through case studies involving 1,000‑agent AI models for grid‑edge flexibility coordination.

By Yihong Zhou, Hanbin Yang, Thomas Morstyn