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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...
This work studies blackboard intelligence: an inference-time perspective in which a model works on a fixed, revisable canvas and searches over candidate solution states rather than committing to a causal, left-to-right trajectory, and concludes that mean confidence, a simple model-internal quantity available from the s...
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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