The field of video generation and editing is rapidly advancing, with a focus on improving efficiency and reducing computational costs. Recent developments have led to the creation of more effective and scalable models, such as those using diffusion transformers and sparse attention mechanisms. These models have achieved state-of-the-art performance in various tasks, including video generation, editing, and summarization. Notably, techniques like test-time training, domain adaptation, and dynamic sparsity have been employed to enhance model performance and efficiency. Furthermore, novel approaches like grafting and content-aware video generation have shown promise in exploring new architecture designs and improving training efficiency. Overall, the field is moving towards more efficient, flexible, and high-quality video generation and editing capabilities. Noteworthy papers include: Test-Time Training Done Right, which improves hardware utilization and state capacity; Interactive Video Generation via Domain Adaptation, which enhances perceptual quality and trajectory control; and Flexiffusion, which achieves efficient neural architecture search for diffusion models.