Gated Linear Attention: linear attention with gates
Published on September 20, 2026, the paper presents a transformer formulation with linear attention and data-dependent gates, also expressible as an RNN with matrix-valued hidden states.
Source: Gated Linear Attention combina atenção linear com gates dependentes dos dados (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on September 20, 2026, the paper presents Gated Linear Attention, a transformer formulation that combines linear attention with data-dependent gates and can also be expressed as an RNN with matrix-valued hidden states. It also describes a hardware-efficient training algorithm.
A company can assess this approach as an alternative to softmax attention for inference with sequence-linear scaling and for hardware-efficient training. A test using representative workloads can compare quality, latency, memory use, and cost before deciding whether to adopt it.
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Wendelmaques can diagnose the workload and constraints, define a comparative evaluation, and implement an inference and monitoring prototype on the company’s infrastructure, with scope and operation set out in a proposal.
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