Unverified50% confidenceFactExact time
Generative modeling of discrete data has long been dominated by two paradigms: GPT-style autoregressive models (generating token by token from left to right) and masked diffusion models (generating in arbitrary order).
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50%
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Long-term
Relevance
10/20/2026
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UnverifiedAutoregressive models (such as the GPT series) are based on the "next token prediction" paradigm, generating text word by word, with a serial inference process, so latency grows linearly when generating long texts.76% similarUnverifiedCausal language models are the foundational architecture of current mainstream large models (such as the GPT series, LLaMA, etc.), predicting the next token step by step from left to right given an existing token sequence, with each step only able to see the left-side context.75% similarUnverifiedDiscrete diffusion models construct complete sequences by iteratively and in parallel resolving multiple tokens across multiple steps, unlike the left-to-right word-by-word generation of autoregressive models.74% similarUnverified扩散模型（如Stable Diffusion）通过逐步去噪还原图像，擅长细节纹理；自回归多模态模型（如GPT Image 2）在理解复杂文字指令和保持角色一致性方面更具优势73% 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72% similar
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