Verified65% confidenceFactTime unknown
The inference process of large language models is autoregressive, and the number of output Tokens is almost linearly correlated with response latency.
3
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65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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Related Claims
UnverifiedA reasoning trace is the sequence of intermediate thinking tokens that a large language model outputs step by step before generating a final answer; it improves accuracy on complex tasks but consumes a large number of additional tokens.80% similarUnverifiedThe inference time and cost of large language models are approximately linearly correlated with the number of input tokens.79% similarUnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.78% similarVerifiedThe 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.77% similarUnverifiedIn AI decision-making scenarios dominated by large language models, the model's reasoning process is distributed across the activation states of billions of parameters, making it difficult to establish a complete causal chain from input to output.75% similar
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