A physics-grounded dilution-fridge simulator with LLM agents achieves supervised-ML parity on cryogenic fault classification using six demonstrations and no training, validated on simulated telemetry plus a real-hardware false-alarm check.
Ma- chine Learning Framework for Anomaly Detection and Maintenance Optimization in Large-Scale Cryogenic Systems,
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Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
A physics-grounded dilution-fridge simulator with LLM agents achieves supervised-ML parity on cryogenic fault classification using six demonstrations and no training, validated on simulated telemetry plus a real-hardware false-alarm check.