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COMET is a coordinated multi-layer runtime monitoring framework designed for human-supervised AI-enabled safety systems. It employs three monitoring layers—signal integrity, physics consistency, and the AI system’s competence envelope—whose outputs are combined and displayed via a traffic-light interface. Evaluated on a compressed‑air leak dataset, COMET eliminates the AI’s silent failures under covariate shift and most sensor faults, while maintaining a low false‑alarm rate.
arXiv:2608. 06657v1 Announce Type: new Abstract: Modern cyber-physical and AI-assisted systems couple human operators, AI decision modules, and automated controllers in a single control loop, so trustworthiness depends on the whole loop, not any one model.
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.
arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.
The paper introduces SPAR, a closed‑loop simulation platform that couples real‑time AUV control software with a higher‑level orchestration layer to evaluate large language models (LLMs) for fault diagnosis and recovery. It demonstrates that a frontier LLM outperforms locally deployable models in identifying a mass‑shift fault, and shows that successful diagnosis depends on following a complete diagnostic procedure rather than premature conclusions. The study provides an architecture and ensemble evaluation methodology for LLM‑assisted mission management on low‑power autonomous underwater vehicles.