The field of anomaly detection and predictive maintenance is moving towards the development of more robust and interpretable methods, leveraging multimodal fusion and neurosymbolic approaches. Recent research has focused on addressing the challenges of limited labeled data and the need for more effective anomaly detection in complex environments, such as assembly pipelines and semiconductor manufacturing. Notable papers include: Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion, which proposes a novel unsupervised anomaly detection framework based on Mahalanobis Support Vector Data Description. NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines, which introduces a neurosymbolic AI and fusion-based approach for multimodal anomaly prediction. SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing, which proposes a neurosymbolic, multiagent CoPilot designed for advanced reasoning and contextual decision-making. Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data, which investigates the use of synthetic data for defect detection in photolithographic patterns.