The field of anomaly detection is moving towards more generalizable and adaptable models, with a focus on overcoming the limitations of specialized methods. Researchers are exploring the use of mixture-of-experts architectures and adaptive thresholding frameworks to improve the detection of anomalies in various domains, including visual, video, and time series data. Additionally, there is a growing interest in developing frameworks that can learn from limited labeled data and adapt to changing environments. Noteworthy papers in this area include AnomalyMoE, which proposes a novel anomaly detection framework based on a mixture-of-experts architecture, and GS-MoE, which introduces a Gaussian splatting-guided mixture of experts approach for weakly-supervised video anomaly detection. Other notable works include Segmented Confidence Sequences and Multi-Scale Adaptive Confidence Segments for anomaly detection in nonstationary time series, ALFred, an active learning framework for real-world semi-supervised anomaly detection, and ConGCD, a consensus-aware paradigm for generalized category discovery.