Unverified50% confidenceFactExact time
HSTU uses a linear-complexity attention variant, so processing behavior sequences longer than several thousand does not result in quadratic growth in computational cost.
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50%
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Long-term
Relevance
9/30/2026
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Related Claims
UnverifiedSSMs have a linear time complexity advantage in processing long sequences, whereas the attention computation complexity of standard Transformers is quadratic with respect to sequence length.71% similarUnverifiedLinear attention, sparse attention, and State Space Models (such as Mamba) can reduce computational complexity to linear levels, opening the door to million-token context windows.68% similarVerifiedThe self-attention mechanism causes computational complexity to grow quadratically with sequence length, so expanding the context from 1,000 to 4,000 tokens increases the computation by 16 times.67% similarUnverified大语言模型的注意力机制计算复杂度与序列长度的平方成正比，处理200K token上下文的计算成本远超过处理50K token66% similarUnverifiedThe model uses a bidirectional attention encoder architecture, completing inference in a single forward pass, with constant-level latency that does not grow linearly with content length.66% similar
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