Beyond Next Token Prediction: Diffusion and Flow Models for Next-Generation Decoding

While autoregressive (AR) decoding remains the dominant paradigm for sequential data generation, it is constrained by the inherent limitations of next-token prediction. To move beyond this, discrete diffusion and flow-based generative models have emerged as compelling non-causal alternatives that enable parallel generation across a wide array of discrete domains, spanning from natural language to complex scientific data. However, transitioning to these paradigms presents a broad spectrum of open challenges, ranging from establishing theoretical frameworks and efficient algorithms to building scalable systems, and developing benchmarks and datasets for novel applications. The goal of this workshop is to bring together researchers with diverse backgrounds from both academia and industry to provide the community with a deeper understanding of the opportunities and limitations of discrete diffusion and flow models, highlight recent breakthroughs, and outline the future of next-generation decoding.