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arXiv · 2512.07782

GatedFWA: Linear Flash Windowed Attention with Gated Associative Memory

Jiaxu Liu·Yuhe Bai·Xiangyu Yin·Christos-Savvas Bouganis
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Abstract

Modern autoregressive models rely on attention, yet the Softmax full attention in Transformers scales quadratically with sequence length. Sliding Window Attention (SWA) achieves linear-time encoding/decoding by constraining the attention pattern, but under an \textit{Associative Memory} interpretation, its difference-style update renders the training objective effectively \emph{unbounded}. In contrast, Softmax attention normalizes updates, leading to \emph{memory shrinkage and gradient vanishing}. We propose GatedFWA: a Memory-\underline{Gated} (\underline{F}lash) \underline{W}indowed \underline{A}ttention mechanism that preserves SWAs efficiency while stabilizing memory updates and making gradient flow controllable. In essence, GatedFWA accumulate a per-token/head gate into a decay bias added to the attention logits, acting as a learnable contraction in the memory recurrence. We implement a fused one-pass gate preprocessing and a FlashAttention-compatible kernel that injects the gate under a sliding mask, ensuring I/O efficiency and numerical stability. On language modelling benchmarks, GatedFWA delivers competitive throughput with negligible overhead and better use of global context, and it integrates cleanly with token compression/selection methods such as NSA and generalizes to various autoregressive domains.

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Jiaxu Liu, Yuhe Bai, Xiangyu Yin, Christos-Savvas Bouganis. 2026-01-07. GatedFWA: Linear Flash Windowed Attention with Gated Associative Memory. https://arxiv.org/abs/2512.07782

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