Runtime Monitoring

Runtime monitoring focuses on detecting and responding to errors or anomalies in dynamic systems, particularly those employing machine learning models, during their operation. Current research emphasizes developing robust monitoring frameworks using diverse techniques, including statistical methods, vision-language models, and neural network-based approaches that analyze activation patterns or model outputs to identify failures. This field is crucial for ensuring the safety and reliability of AI-powered systems in critical applications like robotics, autonomous driving, and healthcare, where failures can have significant consequences. The development of efficient and accurate runtime monitoring techniques is essential for the wider adoption and trust in these technologies.