MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution
Paper recorded by Signals 4 on 2026-09-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29
Category: cs.AI · 人工智能 · first seen 2026-09-29
Abstract
Gated Linear Attention (GLA) Transformers advance linear recurrent models through data-dependent gating, but face a core limitation: the fixed-capacity memory matrices across all heads operate at a single temporal resolution, where each token is processed individually, forcing them to simultaneously encode local syntactic patterns and long-range semantic structure, creating a representational bott
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Cite this page: MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution: the #20 most recent of 440 cs.AI papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/ms-gla-multi-scale-gated-linear-attention-for-addressing-representational-bottle.html
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