Unverified50% confidenceFactExact time
NCM 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
Related Entities
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.68% similarVerified扩散模型的核心原理分为前向扩散过程（逐步添加高斯噪声）和反向去噪过程（从噪声中还原图像）两个阶段67% similarUnverifiedNormalizing Trajectory Models (NTM) model each step in the reverse process as an expressive conditional normalizing flow, rather than Gaussian denoising66% similarUnverifiedClassical diffusion models (such as DDPM and Score-Based Generative Model) rely on Brownian motion driven by Gaussian noise as the forward corruption process.64% similarUnverifiedThe technical foundations of diffusion models originate from stochastic processes in non-equilibrium thermodynamics, comprising two stages: a forward diffusion process and a reverse denoising process.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1088754API
curl https://kongchang.com/api/v1/knowledge/claims/1088754MCP
get_claim(id=1088754)