Unverified50% confidenceFactExact time
The token generation process of large language models performs forward propagation of identical depth at every step, and there is no corresponding mechanism for fatigue or resource depletion.
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50%
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Long-term
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9/25/2026
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VerifiedThe inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.74% similarUnverifiedAn agentic workflow refers to having a large language model operate in a looping, multi-step manner rather than generating output in a single pass.68% similarUnverifiedWhen large models generate text, they do not generate an entire sentence at once; instead, they generate tokens one by one, continuously predicting the next token and adding it to the context in a loop.68% similarUnverified大语言模型本身是无状态的，其所有知识编码在训练后固定不变的神经网络权重中，单次推理完成后中间状态归零67% similarVerifiedLarge language models are essentially stateless functions whose weights are fixed at inference time and do not learn over time.67% similar
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