Unverified50% confidenceFactExact time
LLMs generate text in an autoregressive manner, producing only one token at a time, requiring N forward inference passes to generate N tokens, with latency scaling linearly with output length.
1
Sources
50%
Confidence
Long-term
Relevance
8/8/2026
First Seen
Sources
Model Council频繁报错：多模型协作为何不稳定
Related Claims
Unverified大模型推理的自回归生成需要逐Token串行输出，生成N个Token需要N次前向传播78% similarUnverifiedLLM 的自回归生成特性决定了输出是逐 Token 的流式传输，在前端产生流式接收、增量 DOM 更新、不完整 Markdown 容错解析等工程需求73% similarVerifiedThe token generation process of large language models is serial, based on an autoregressive mechanism, where each token's generation depends on the previous token.71% similarUnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.71% similarUnverifiedDuring each inference, an LLM only processes the token sequence of the current input and does not retain persistent state across sessions (statelessness).71% similar
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