Unverified50% confidenceFactExact time
The complexity of local interactions (such as window attention or neighborhood convolution) typically scales linearly with the number of tokens, at a cost far lower than global attention.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
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Related Claims
VerifiedThe computational complexity of standard self-attention is quadratic with respect to sequence length, and achieving a context of one million tokens requires introducing techniques such as sparse attention, sliding window attention, and KV Cache compression.72% similarUnverifiedMulti-head attention performs multiple attention operations in parallel across different low-dimensional subspaces, typically with 8 or 16 heads.71% similarUnverified自注意力使任意两个位置之间的信息交互只需一步计算，但显存占用随序列长度平方增长71% similarVerified自注意力机制的计算复杂度随序列长度呈近似二次方增长，序列长度为n时注意力矩阵计算量正比于n²71% similarUnverifiedExisting mainstream sparse attention methods filter interactions by retaining entries with larger values or high-quality regions in the attention matrix.70% similar
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