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标题为英语SMat-Attention: Structured Long-Context Sequence Modeling

arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing…

arXiv — cs.AI作者: Emile Anand, Abdullah Ateyeh, Archer Wang, Marin Solja\v{c}i\'c2026年9月30日 (9月30日)
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  • arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing…
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SMat-Attention: Structured Long-Context Sequence Modeling

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