Unverified50% confidenceFactExact time
Masked diffusion models can decode in parallel, support generation in arbitrary order, and are naturally suited to handling discrete representations of multiple modalities.
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Long-term
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10/14/2026
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UnverifiedDiscrete 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.76% similarVerifiedDiffusion language models can simultaneously perform parallel prediction and iterative refinement on all tokens in the entire sequence, rather than outputting them one by one.75% similarUnverifiedUnifying generation and editing into a single set of weights typically follows two technical approaches: a conditional generation framework based on diffusion models, and a paradigm drawing on instruction fine-tuning.74% similarUnverified多模态模型将不同模态数据通过各自编码器转换为统一向量表示，再送入同一Transformer架构进行联合推理73% similarUnverifiedHybrid patterns have deterministic code handle the heavy lifting and return only concise output to the model, saving tokens while improving result reliability.72% similar
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