Verified75% confidenceConceptExact time
The context window refers to the total number of tokens a model can see in a single inference, including input and output; the computational cost of the attention mechanism grows quadratically with sequence length.
3
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75%
Confidence
Long-term
Relevance
10/15/2026
First Seen
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Related Claims
UnverifiedWhen a large language model processes input, it computes Key and Value vectors in the attention mechanism for each token, and these vectors are reused repeatedly when generating replies.77% similarUnverifiedThe context window refers to the total amount of information a model can process simultaneously in a single conversation, usually calculated in tokens; as the number of conversation turns increases, earlier content gradually exceeds the window range, causing the model to "forget" earlier discussion.77% similarVerifiedThe memory of LLMs depends on the context window, which is the maximum number of tokens the model can process in a single inference.75% similarUnverifiedIn the attention mechanism of current large models, the query vector is indirectly shaped by the task context and objective function, making it closer to the top-down mode.75% similarUnverifiedThe context window is the maximum number of tokens a time-series foundation model (and all Transformer-based models) can process in a single forward computation, jointly determined by the positional encoding in the model architecture and the complexity of the attention mechanism; the window size is a hyperparameter fixed at training time and cannot be extended at inference.75% similar
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