Logical anomaly detection stops rewriting only the question: CASCADE++ scaffolds the image per constraint and beats full-shot training with three references
Large-vision-language-model logical anomaly detection splits each constraint into a textual checklist, yet every check still reads the same raw image, so the model must pull different kinds of visual evidence from one input. CASCADE extends the decomposition to the image side, overlaying per-instance color for binding constraints or an object-anchored grid for calibration constraints, and CASCADE++ derives each scaffold's configuration from the constraint structure and the reference distribution. On MVTec-LOCO, CASCADE++ with three references and no training exceeds the strongest full-shot method and transfers across several vision-language backbones.