Unverified50% confidenceFactExact time
The 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.
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
UnverifiedNCM's parallel prediction is a form of hierarchical conditional non-autoregressive generation: parallel within the same level, while conditional dependencies remain between levels.77% similarUnverified在Transformer架构中，对于拥有N个参数的模型，生成每个token大约需要2N次浮点运算73% similarUnverifiedTransformer inference is divided into two phases: a parallel compute-intensive prefill phase and a sequential memory-bandwidth-intensive decode phase.69% similarVerifiedEach additional token generated by an autoregressive model provides one more opportunity for a Transformer forward pass computation, trading sequence length for computational depth.69% similarUnverifiedIn the Transformer architecture, feed-forward network (FFN) layers typically account for more than two-thirds of the model's parameters.69% similar
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