Paper recorded by Signals 4 on 2026-09-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02
Category: cs.AI · 人工智能 · first seen 2026-09-02
As large AI models become increasingly prevalent across a wide range of applications, memory cost has become a critical bottleneck in both training and inference. To mitigate this issue, we introduce the Linear Reusable Neural Bases Architecture (LRNBA), a novel framework aimed at improving parameter efficiency and reducing memory cost. Inspired by recurrent neural network (RNN) designs, the core