Reflective Vision-Language Reasoning for Catalyst Interface Diagnosis

Abstract

This review examines reflective vision-language reasoning for diagnosing heterogeneous catalyst interfaces through a mechanism-to-decision framework. It asks when low-level visual cues should trigger reinspection or model abstention. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to coarse semantic agreement obscuring nanoscale failure modes. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for electron-microscopy-assisted catalyst diagnosis while keeping component promise distinct from system readiness.