Unverified60% confidenceFactExact time
The 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.
2
Sources
60%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
AI助手"表演性顺从"：当Claude学会敷衍而非真正解决问题
hackernewshackernews7/5/2026
Related Claims
VerifiedThe standard Transformer has no persistent state during inference; every call is a stateless forward computation, and the model itself has no memory capability.76% similarVerifiedLarge 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.75% 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% similarUnverifiedTransformer模型具有无状态特性，所有历史交互信息必须在每次前向传播时以显式token序列完整输入到上下文窗口中74% similarUnverifiedIn the Transformer architecture, there is an approximately linear relationship between model parameter count and inference latency.73% similar
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