Unverified50% confidenceFactExact time
Discrete-space diffusion defines the diffusion process directly on token sequences by introducing a [MASK] token; the forward process progressively replaces real tokens with masks, and the reverse process learns to predict the original masked tokens.
1
Sources
50%
Confidence
Long-term
Relevance
9/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBuilding a minimum viable diffusion language model can adopt the discrete mask diffusion approach, referencing the MDLM or D3PM framework, where the forward process replaces tokens with [MASK] at timestep-dependent probabilities via a transition matrix.64% similarUnverifiedMasked Diffusion defines the noise-adding operation as randomly replacing tokens with [MASK] tokens, with denoising corresponding to predicting the original tokens at the masked positions64% similarUnverified文本扩散模型主要通过两条路径解决离散token问题：在连续嵌入空间执行扩散，或定义离散马尔可夫转移矩阵直接在token空间操作63% similarUnverifiedBERT's MLM performs one-shot prediction and is primarily used for representation learning, whereas discrete diffusion models treat the masking ratio as a continuous variable from 0% to 100% to construct a complete Markov chain, achieving progressive generation through multi-step iteration.61% similarUnverified扩散模型在连续像素空间中学习概率分布，而非在离散符号空间中建立语义映射，与GPT系列语言模型的Token化处理路径存在架构分歧60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/837834API
curl https://kongchang.com/api/v1/knowledge/claims/837834MCP
get_claim(id=837834)