CAAD: A Cross-Modal Autoregressive Diffusion Approach for Anomaly Detection in Complex Industrial Processes

Accurate anomaly detection is essential for the safe and stable operation of complex industrial processes, such as magnesium smelting. Although diffusion models have shown strong performance in industrial anomaly detection, thanks to their powerful distribution modeling, traditional diffusion models often suffer from identity mapping under global optimization, causing local high-frequency anomalous textures to be preserved rather than repaired. Moreover, existing cross-modal mechanisms typically assume reliable auxiliary sensor signals, leading to conditional misguidance when sensors fail. To address these issues, we propose a cross-modal autoregressive diffusion framework (CAAD). By adopting an autoregressive diffusion strategy, global denoising is decomposed into local sequence generation, with mandatory local perception constraints effectively preventing anomalous shortcut learning. In addition, we propose a dynamic-gated cross-modal alignment mechanism that adaptively modulates the injection strength of physical context, automatically suppressing unreliable signals during multimodal inconsistencies. Extensive experiments on magnesium smelting benchmarks demonstrate that CAAD achieves competitive performance in both detection accuracy and robustness.