Verified75% confidenceFactExact time
The 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.
3
Sources
75%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
Related Claims
UnverifiedThe operation of large language models does not involve symbolic reasoning, world model construction, or goal-directed intent — only probability distributions encoded in weight matrices.80% similarUnverified自回归语言模型采用严格的从左到右顺序生成,推理阶段必须串行执行,无法并行,构成速度上的天然上限79% similarVerifiedLarge language models are essentially stateless functions whose weights are fixed at inference time and do not learn over time.78% similarUnverified大语言模型采用自回归生成机制,在生成token时使用单向前向注意力,无法进行全局类型校验,易产生API幻觉78% 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.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/585856API
curl https://kongchang.com/api/v1/knowledge/claims/585856MCP
get_claim(id=585856)