Unverified50% confidenceFactExact time
Traditional discrete diffusion (masked diffusion and uniform diffusion) completely erases token identity in a single corruption event, so the noisy state cannot express partial uncertainty (e.g., multiple candidates still being possible).
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50%
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Long-term
Relevance
10/21/2026
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Related Claims
UnverifiedContinuous-space diffusion first maps discrete tokens to a continuous embedding space to perform Gaussian noise diffusion and denoising, then maps back to the discrete vocabulary, but the discretization step may introduce error accumulation.67% similarUnverifiedAnnotation noise typically exhibits random characteristics, whereas meaningful ambiguity tends to display systematic patterns, with annotator groups of different backgrounds forming distributions that are internally consistent but divergent across groups.67% similarUnverifiedWhen diffusion models directly infer at higher resolutions, the receptive field of the denoising network cannot cover the global structure, leading to issues such as multiple heads, limb misalignment, or texture repetition.66% similarUnverifiedThe fatal weakness of token overlap rate is that monotonicity is uncontrollable: the same semantic unit yields vastly different scores under different tokenizers and different truncation strategies, while threshold selection is often based on intuition rather than principle.66% similar
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