CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis

Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving downstream performance. However, existing methods either rely on scarce real anomaly references or auxiliary data, while lacking complex reasoning and