Diffusion 一定比自回归更有机会实现大一统吗？

Group 1 - The article discusses the potential of Diffusion models to achieve a unified architecture in AI, suggesting that they may surpass autoregressive (AR) models in this regard [7][8][9] - It highlights the importance of multimodal capabilities in AI development, emphasizing that a unified model is crucial for understanding and generating heterogeneous data types [8][9] - The article notes that while AR architectures have dominated the field, recent breakthroughs in Diffusion Language Models (DLM) in natural language processing (NLP) are prompting a reevaluation of Diffusion's potential [8][9][10] Group 2 - The article explains that Diffusion models support parallel generation and fine-grained control, which are capabilities that AR models struggle to achieve [9][10] - It outlines the fundamental differences between AR and Diffusion architectures, indicating that Diffusion serves as a powerful compression framework with inherent support for multiple compression modes [11]