Unverified50% confidenceFactExact time
Transformer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
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
Codex超高推理模式隐藏成本：一个任务耗尽70%额度的真相
redditr/OpenAI7/12/2026
Related Claims
UnverifiedThe 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.74% similarUnverifiedTransformer-based LLMs perform an independent forward-pass computation for each inference, with model weights fixed during the inference phase; what is referred to as context memory is essentially the re-injection of historical conversation text into the prompt.73% similarUnverifiedIn standard Transformer inference, generating each new token requires accessing the key-value vectors of all historical tokens, and the memory footprint of the KV cache grows linearly with sequence length.73% similarPartially VerifiedThrough the self-attention mechanism, Transformer allows the model to dynamically attend to all other tokens in the sequence when processing each token, while enabling fully parallelized training.72% similarVerifiedThe key innovation of the Transformer is the self-attention mechanism, which allows the model to simultaneously attend to information from all other tokens in the input sequence when processing a single token.71% similar
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