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We argue that the dichotomy is false because it conflates model family, data representation, training objective, and inference procedure. Autoregression is an inference procedure that expands a sequence through normalized conditional draws, while diffusion is a refinement procedure that repeatedly revises an existing state. The more useful contrast is therefore not autoregressive versus diffusion, but discrete tokens learned with cross-entropy versus continuous tokens learned with diffusion-style objectives, together with the inference algorithms used to sample from them. From this perspective, algorithmic progress should prioritize inference-time efficiency along two axes: sequence expansion and state refinement. 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