The field of multimodal intelligence is rapidly advancing, with a focus on developing unified architectures that can efficiently process and generate multiple forms of data, such as vision, speech, and language. Recent innovations have led to the creation of sparse, scalable models that can achieve state-of-the-art performance in various tasks, including speech recognition, image generation, and text-to-image synthesis. These models are designed to be highly efficient, with some architectures achieving significant improvements in computational efficiency while expanding model capacity. Noteworthy papers in this area include: Ming-Flash-Omni, which introduces a sparse, unified architecture for multimodal perception and generation, achieving state-of-the-art results in text-to-image generation and generative segmentation. Emu3.5, which presents a large-scale multimodal world model that natively predicts the next state across vision and language, exhibiting strong native multimodal capabilities and generalizable world-modeling abilities.