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Diffusion and flow matching are now dominant frameworks for generative modeling in continuous domains, but they typically require multiple network evaluations at inference. This has motivated extensive research on one-step generative modeling primarily built on diffusion and flow, though existing approaches often rely...
Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on problems governed by complex global constraints. In this work, we focus on this regime and ask whether some of these limitations arise from the inference interface induced by...
This work introduces block-hybrid attention, which retains exact softmax attention within the active denoising block while applying linear attention over previous blocks, and shows that pretrained dLLMs can be efficiently linearized for faster inference.
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