Unverified50% confidenceFactExact time
Autoregressive models generate token by token from left to right, whereas diffusion models produce text in parallel through stepwise denoising of noise.
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50%
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Long-term
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10/20/2026
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UnverifiedContinuous diffusion language models map discrete tokens to a continuous embedding space, perform the noising and denoising process in that space, and finally decode back to discrete tokens.74% similarVerifiedDiffusion language models adopt a parallel denoising approach, which can simultaneously optimize outputs at multiple positions in a single forward pass and then iteratively converge to the final text through multiple rounds.72% similarUnverifiedSplicing autoregressive text generation together with diffusion-based iterative denoising introduces a fundamental asymmetry in computational paradigms.71% similarUnverifiedMidjourney uses CLIP's text encoder to convert prompts into conditioning vectors that guide the denoising process of the diffusion model.71% similarUnverified扩散语言模型借鉴图像扩散模型思路，通过迭代去噪过程并行生成整段文本，与GPT系列自回归生成不同67% similar
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