Unverified70% confidenceFactExact time
LLMs generate text in an autoregressive manner, producing only one token at a time, and the generation of the next token must wait for the previous token to be completed.
2
Sources
70%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLMs 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.78% similarVerifiedLLMs generate text in an autoregressive manner based on the Transformer architecture, predicting only one token per forward pass and then appending that token to the input sequence before predicting the next token.76% similarUnverifiedLLMs generate text in an autoregressive manner, generating only one token at a time and appending it to the input sequence, requiring 100 complete forward passes through the model to generate 100 tokens.73% similarUnverifiedDuring each inference, an LLM only processes the token sequence of the current input and does not retain persistent state across sessions (statelessness).71% similarUnverifiedLLM inference is autoregressive: every token generated requires a full forward pass.71% similar
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