Verified65% confidenceFactTime unknown
Large models are based on the Transformer architecture; their inference process is stateless, each request is an independent forward propagation computation, and the model itself retains no information between requests.
3
Sources
65%
Confidence
Long-term
Relevance
6/3/2026
First Seen
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Related Claims
VerifiedLarge language models are based on the Transformer architecture; each inference is an independent forward propagation process that retains no session state.80% similarVerifiedThe standard Transformer has no persistent state during inference; every call is a stateless forward computation, and the model itself has no memory capability.78% similarUnverifiedTransformer 架构下模型在推理时仅处理当前输入的 token 序列，不存在跨请求的内部状态持久化75% similarUnverifiedThe Transformer model is a purely sequence-to-sequence mapping function whose parameters are completely static during inference, with no mechanism to actively probe the state of the external environment during inference.75% similarVerifiedModern large models are based on the Transformer architecture, and generating each token requires one full forward pass over all the model's parameters.75% similar
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