Verified65% confidenceFactTime unknown
Current large models do not have a true persistent memory mechanism, and as the number of conversation turns increases, the weight of early requirement descriptions in the attention mechanism gradually decays.
3
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65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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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% similarVerifiedAs the number of conversation turns increases, the weight of early key constraint information in the context window is diluted (attention drift), causing the quality of AI output to gradually decline.77% similarUnverifiedThe limitation of this prompt design is that the model has no true persistent memory; numerical states exist only within the current context window and will be automatically truncated once the context length is exceeded.77% similar
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