Unverified50% confidenceFactExact time
NCM's parallel prediction is a form of hierarchical conditional non-autoregressive generation: parallel within the same level, while conditional dependencies remain between levels.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/23/2026
First Seen
Valid until: 1/21/2027
Sources
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Related Claims
UnverifiedThe second stage of NCM uses a Transformer to predict the discrete codes at each level in parallel, and the prediction at each level is conditioned on the previous level.77% similarUnverifiedNon-autoregressive generative models assume that positions or levels are conditionally independent to some degree, so that multiple outputs can be produced at once or hierarchically in parallel, but they often lose fluency because dependencies between tokens are ignored.72% similarUnverifiedThe NCM (Next-Concept Modeling) approach was proposed to achieve parallel generation through hierarchical latent representations, seeking a balance between efficiency and information retention.70% similarUnverifiedNCM is similar to diffusion models in that both abandon strict left-to-right token-by-token generation; the difference is that NCM organizes the generation process with an explicit multi-granularity discrete codebook and a clearer hierarchical structure, whereas diffusion models implicitly control generation granularity through noise levels.68% similarUnverifiedDuring MDM generation, the conditional distribution at each position can be computed simultaneously, which naturally supports parallel decoding, in sharp contrast to the way autoregressive models generate tokens one by one.66% similar
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