Unverified50% confidenceFactExact time
Dense attention layers require a complete KV Cache, whereas linear layers rely on hidden states.
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Long-term
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9/11/2026
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UnverifiedThe 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.73% similarUnverified研究表明注意力层主要负责信息的路由和聚合，而MLP层更像是知识的存储库，负责非线性变换和信息的深度加工73% similarUnverifiedHybrid architectures (linear attention + standard attention) are more efficient than pure full attention on long-context tasks, but linear attention may be slightly inferior to standard attention on certain reasoning tasks that require precise global dependencies72% similarUnverifiedFor a model with L layers, H attention heads, and per-head dimension d, caching n tokens requires VRAM of 2 × L × H × d × n × (precision bytes).71% similarUnverifiedK3 uses a mixture of linear attention layers and standard attention layers to seek a balance between accuracy and efficiency.71% similar
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