Detecting Silent Pipeline Drift in Materials Informatics

How can anomaly models distinguish a new material signal from a changed workflow? This review answers by treating pipeline drift as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is repeated analysis across instruments and code versions, where a plausible conclusion may depend on an unrecorded stress state, preprocessing choice, or alternative structural interpretation. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of instrument traces, diffraction and spectroscopy products, simulation outputs, laboratory records, and analysis repositories; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.

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