Unverified50% confidenceFactExact time
As the number of tokens in the window grows, attention weights must be distributed among these tokens, and the average attention each individual token receives declines accordingly.
1
Sources
50%
Confidence
Long-term
Relevance
10/25/2026
First Seen
Sources
Larger Context Windows Make AI Agents Less Reliable? 4 Failure Modes and How to Fix Them
bilibilicode炀10/24/2026
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Unverified标准Attention的显存消耗随序列长度平方增长，Flash Attention将其压缩到线性增长80% similarUnverifiedThe computational cost of standard self-attention grows as O(n²) with sequence length, limiting the scale of context windows79% similarUnverifiedMulti-head attention performs multiple attention operations in parallel across different low-dimensional subspaces, typically with 8 or 16 heads.78% similarVerifiedIn the attention mechanism of the Transformer architecture, as sequence length increases, the attention scores of early tokens are diluted by subsequent tokens after softmax normalization, forming an attention sinking effect.77% similarUnverifiedAfter removing residual connections, pure attention undergoes double-exponential degradation with depth.76% similar
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