Unverified50% confidenceFactExact time
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
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8/26/2026
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UnverifiedThe attention layer acts as an information router that aggregates context across different sequence positions, with its keys and values being short-term memory representations dynamically derived from other tokens in the prompt.76% 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74% similarUnverifiedUltra-large context windows are typically implemented relying on sparse attention mechanisms, RoPE positional encoding extrapolation, and hierarchical caching technology.72% similarUnverifiedThe Multi-Query Attention technique allows multiple attention heads to share a single set of Key and Value projection matrices, compressing KV cache memory requirements to 1/H of the original, at the cost of a slight reduction in model expressiveness.71% similarUnverifiedThe 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.70% similar
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