TraceRelay 架构提高了循环神经网络在序列任务上的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员推出了一种新颖的注意力对齐循环神经网络架构 TraceRelay。该设计将持久表示分布在一系列低维轨迹上，使局部注意力能够从较低层表示中形成增量。该架构使用固定的加性相位循环来累积这些延迟增量，而步进式前缀和则有助于并行预填充和有界缓冲区续接。在 Equal Repeats 和 Dyck closing-type prediction 等任务上的实验表明，具有循环相位继承的模型与独立训练的变体相比，准确性显著提高，尤其是在更长的序列长度下。 AI

影响引入了一种新颖的循环架构，提高了序列建模任务的性能，可能影响未来的模型设计。

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arXiv cs.LG TIER_1 English(EN) · Sungwoo Goo, Hwi-yeol Yun, Sangkeun Jung · 2026-10-09 04:00

TraceRelay：注意力对齐的滚动轨迹递归

arXiv:2610.11743v1 Announce Type: new Abstract: We present TraceRelay, an attention-aligned recurrent architecture that distributes persistent representations over a rolling sequence of low-dimensional traces. Local right looking attention forms increments from lower-layer repres…

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TraceRelay：注意力对齐的滚动轨迹递归

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