Verified70% confidenceFactTime unknown
Even models with a million-token context face an attention dilution problem when handling large codebases—when the context is too long, the model's attention to early content drops significantly.
4
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
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Related Claims
VerifiedThe attention mechanism of the Transformer architecture significantly reduces its focus on early content when processing very long sequences, causing the model to forget earlier settings78% similarUnverifiedWhen prompts are too long, large models experience an 'attention explosion' phenomenon, where excessively long inputs cause effective information to be lost.77% similar
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