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Katharopoulos and colleagues: recurrent linear attention

Contribution grade

Efficient sequence modeling · Human-designed research method or resource

Reference score125,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Reordered kernel-feature attention computation associatively, allowing linear sequence-length computation and a recurrent autoregressive implementation.

Scientific scope

Reordered kernel-feature attention computation associatively, allowing linear sequence-length computation and a recurrent autoregressive implementation. The chosen feature-map kernel is not exact softmax attention. Complexity fixes feature dimension; reported benchmark speedups do not transfer automatically.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The chosen feature-map kernel is not exact softmax attention. Complexity fixes feature dimension; reported benchmark speedups do not transfer automatically.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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