Verified70% confidenceFactExact time
The standard Transformer has no persistent state during inference; every call is a stateless forward computation, and the model itself has no memory capability.
4
Sources
70%
Confidence
Long-term
Relevance
7/9/2026
First Seen
Sources
LangChain Hands-On Guide: From LLM Calls to Agent Development
bilibili今天AI了吗7/8/2026
Related Claims
UnverifiedTransformer 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78% 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.78% 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.76% similarUnverifiedThe statelessness of LLMs is a fundamental characteristic of the Transformer architecture; the model stores no state after a session ends or the context is cleared.75% similarUnverified该模型完全不使用MLP、Transformer、注意力层，甚至没有独立的输出矩阵，仅用极简动力学系统学习词表示74% similar
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