Expired50% confidenceFactExact time
During Transformer model inference, the KV Cache (key-value cache) expands continuously as the conversation grows, causing the attention score distribution to become increasingly flat and making it difficult for the model to precisely focus on information relevant to the current task.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Testing Claude Code for WordPress Publishing: Does AI Cut Corners on Batch Writing?
bilibili跨屏互联6/18/2026
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 settings77% similarUnverifiedThe KV Cache, which grows dynamically during inference, continuously occupies VRAM and may suddenly trigger an OOM crash during long conversations.76% similarUnverifiedIn Transformer models, the first and last few layers are more sensitive to quantization, the middle layers have higher redundancy, and attention layers and feed-forward network layers differ in their tolerance to quantization.73% similarUnverifiedCurrent 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.73% similar
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