Unverified50% confidenceFactExact time
Continuous 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.
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9/23/2026
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Unverified早期的离散扩散语言模型如 MDLM、SEDD 通过在 Token 嵌入空间或掩码空间中定义扩散过程来解决文本离散性挑战74% similarUnverifiedContinuous Diffusion Language Models (CDLM) map text into a continuous embedding space to perform the diffusion process and then decode back to discrete text, rather than performing diffusion directly on discrete tokens.72% similarUnverifiedEven when large language models support a 200K token context, they exhibit the Lost in the Middle phenomenon when processing large codebases, meaning their attention to earlier information in the context decreases progressively.70% similarUnverified掩码扩散框架将加噪重新定义为token掩码替换，使扩散过程在离散文本域得以自洽70% similarUnverifiedDiffusion 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.69% similar
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