The field of anomaly detection and model repair is moving towards more robust and effective methods. Researchers are exploring new approaches to address the challenges of detecting anomalies in complex datasets and repairing models that exhibit systematic errors. One notable direction is the use of large language models and vision-language models to improve anomaly detection and model robustness. Another area of focus is the development of methods that can handle partial or noisy data, such as membership inference attacks with partial features and anomaly detection in the presence of data manipulation attacks. Additionally, there is a growing interest in using synthetic data generation and data augmentation techniques to improve model performance in safety-critical applications. Noteworthy papers in this area include: Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models, which proposes a novel unsupervised framework for detecting jailbreak attacks. SafeFix: Targeted Model Repair via Controlled Image Generation, which introduces a model repair module that uses a conditional text-to-image model to generate semantically faithful and targeted images for failure cases. SynSpill: Improved Industrial Spill Detection With Synthetic Data, which demonstrates the effectiveness of using synthetic data generation and parameter-efficient fine-tuning of vision-language models for industrial spill detection.