The recent advancements in video generation and autoregressive modeling have significantly pushed the boundaries of computational efficiency and output quality. Researchers are focusing on developing frameworks that reduce computational complexity while maintaining or enhancing the richness of generated content. Key innovations include the introduction of multi-scale causal attention mechanisms, linear computational complexity models, and efficient temporal attention blocks. These approaches not only streamline the computational demands but also enable the generation of longer, higher-resolution videos on resource-constrained devices. Additionally, the exploration of scaling laws in motion generation and the development of non-quantized autoregressive models highlight a shift towards more generalized and efficient modeling techniques. Notably, some papers stand out for their groundbreaking contributions: 'LinGen' introduces a linear-complexity framework for high-resolution, minute-length video generation, and 'NOVA' presents a novel autoregressive model without vector quantization, demonstrating superior performance across various tasks.