arXiv Paper Proposes Safety Cage Framework for Bounding ML Model Operational Range in Spectroscopy

A new arXiv preprint introduces a framework aimed at keeping black-box machine learning models within validated operational bounds, motivated by safety-critical space missions where ground truth is often unavailable. The approach is applied to spectroscopy, framing reliability as a matter of constraining where a model's predictions can be trusted. The work targets validation gaps that arise when labeled data for verification is scarce.