A novel unsupervised ensemble method, SEAD, was presented at ICML 2025, offering a breakthrough in real-time anomaly detection for streaming data. This innovative work introduces an approach that intelligently weights diverse anomaly detectors, dynamically adapting to changing data distributions without requiring labeled examples. Leveraging a Multiplicative Weights Update mechanism, SEAD consistently outperforms existing methods across various tasks, with an optimized variant, SEAD++, further enhancing computational efficiency. This advancement provides a robust and generalizable solution critical for industrial and online applications where traditional supervised methods are insufficient.