Unverified50% confidenceFactExact time
Diffusion language models still need to process all sequence positions at each denoising step, so parallel generation does not mean the end of computational waste.
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10/28/2026
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VerifiedDiffusion language models can simultaneously perform parallel prediction and iterative refinement on all tokens in the entire sequence, rather than outputting them one by one.78% similarUnverifiedAfter removing the acoustic tokenizer, the model must model directly in a continuous acoustic space, typically relying on continuous generation paradigms such as diffusion models or flow matching, which shortens the inference pipeline.74% similarUnverifiedThe token generation process of large language models performs forward propagation of identical depth at every step, and there is no corresponding mechanism for fatigue or resource depletion.74% similarVerifiedLarge language models are based on the Transformer architecture; each inference is an independent forward propagation process that retains no session state.74% similar
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