The field of robotics and video generation is rapidly advancing, with a focus on creating more realistic and interactive simulations. Researchers are exploring new methods for generating simulations, such as using inverse design and large language models to create plausible scenarios and environments. Additionally, there is a growing interest in developing frameworks that can integrate multiple modalities, such as vision, language, and physics, to create more comprehensive and realistic simulations. These advancements have the potential to improve the validation of robot policies, enhance data or simulation augmentation, and unlock new opportunities for scalable and data-efficient robot learning. Noteworthy papers in this area include ReGen, which introduces a generative simulation framework that automates simulation design via inverse design, and Isaac Lab, which presents a GPU-accelerated simulation framework for multi-modal robot learning. Other notable papers include UniVA, which introduces a universal video agent towards open-source next-generation video generalist, and PAN, which presents a world model for general, interactable, and long-horizon world simulation.