The field of video generation and anomaly detection is moving towards more fine-grained evaluation tools and sophisticated reward models. Researchers are focusing on developing methods to identify and localize errors in video generation, as well as creating benchmarks for video anomaly detection that can reliably assess real-world performance. Noteworthy papers in this area include Spotlight, which introduces a novel task for localizing and explaining video-generation errors, and Q-Save, which presents a new benchmark dataset and model for holistic and explainable evaluation of AI-generated video quality. Additionally, Pistachio provides a synthetic, balanced, and long-form video anomaly benchmark, while ADNet offers a large-scale, multi-domain benchmark for anomaly detection across various categories. TEAR introduces a temporal-aware automated red-teaming framework for text-to-video models, highlighting the importance of addressing safety challenges in video generation. These papers demonstrate significant innovations and advances in the field, paving the way for future research and development.