The field of data fusion and anomaly detection is moving towards innovative applications of diffusion and flow models, enabling the integration of multi-source information to enhance data quality and insights. Recent developments have focused on leveraging these models for tasks such as satellite remote sensing data fusion and anomaly detection in complex, high-dimensional data. Notably, the use of diffusion models has shown promise in preserving fine-grained spatial details and generating high-fidelity fused images. Furthermore, novel frameworks have been proposed for anomaly detection, incorporating techniques such as graph neural networks and reinforced exponential moving average. These advancements have the potential to transform safety-critical applications, including industrial monitoring and autonomous vehicle systems. Noteworthy papers include: Diffuse to Detect, which proposes a generalizable framework for anomaly detection with diffusion models, and GRAD, which introduces a real-time gated recurrent anomaly detection method for autonomous vehicle sensors.