Unverified50% confidenceFactExact time
LLM 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
1
Sources
50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
UnverifiedThe autoregressive generation characteristic of LLMs determines that their output is streamed token by token, stemming from the decoding strategy of the Transformer architecture.81% similarUnverifiedLLMs 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.73% similarUnverifiedLLM 在处理复杂任务时容易出现幻觉和逻辑漂移，这与其自回归逐 token 预测机制有关，早期错误会被后续内容放大70% similarUnverifiedLLM 的输出非确定性根源于自回归生成机制，即使输入相同，Temperature、Top-p 等采样参数也会导致不同输出69% similarVerifiedThe output of LLMs is inherently non-deterministic; even with the same Prompt as input, the model may return results with different formats, content, or even logic at different times.68% similar
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