English(EN) RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States

RunningTensor 将线性注意力推广到高阶循环状态

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员推出了一种名为 RunningTensor 的新方法，该方法将线性注意力和状态空间模型推广到高阶循环状态。通过将记忆张量从二阶矩阵扩展到 o 阶张量，这种进步可以表示更复杂的交互。RunningTensor 在相对于序列长度保持线性时间复杂度的同时，增加了工作内存容量，在合成联想回忆任务上表现出改进的性能，并有望用于语言理解和检索。 AI

影响引入了一种新的架构方法，可以增强序列建模任务中的记忆容量和性能。

排序理由该集群描述了 arXiv 论文中提出的一种新的序列建模方法和理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Luca Herranz-Celotti, Vincent Guigue · 2026-09-14 04:00

RunningTensor: 将线性注意力推广到高阶循环状态

arXiv:2609.12814v1 Announce Type: new Abstract: Linear attention and state-space models provide linear-time sequence modeling, but their recurrent memory remains a second-order tensor (a matrix), limiting the order of interactions that can be represented in the state. We introduc…

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RunningTensor: 将线性注意力推广到高阶循环状态

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