Verified65% confidenceFactExact time
Autoregressive generation refers to the model predicting the next token each time based on all previously generated tokens, progressively unfolding until a complete code block is generated
3
Sources
65%
Confidence
Long-term
Relevance
8/12/2026
First Seen
Sources
Related Claims
Unverified大模型推理的本质是自回归生成，即不断预测下一个token80% similarUnverified输出生成是严格串行的自回归过程，每生成一个token都需要一次完整的模型前向传播78% similarUnverifiedTraditional autoregressive methods require N forward passes to generate N tokens, whereas speculative decoding consolidates multiple calls into one through batch verification.76% similarVerifiedSpeculative Decoding uses a small, fast draft model to predict multiple tokens in advance, which are then verified in parallel all at once by the large model.76% similarUnverifiedLLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.75% similar
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