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Adaptive Fourier neural operators for efficient token mixing

Contribution grade

Scientific machine learning · Human-designed research method or resource

Reference score115,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Adapts Fourier neural operators into a vision token mixer using block-diagonal channel mixing, shared Fourier-domain MLPs and soft-thresholded frequency features.

Scientific scope

Adapts Fourier neural operators into a vision token mixer using block-diagonal channel mixing, shared Fourier-domain MLPs and soft-thresholded frequency features. Guibas and Mardani share first authorship, second leads project. Quasi-linear sequence-length cost assumes fixed channel/block dimensions and is not exact attention-output equivalence, arbitrary-resolution accuracy or a blanket end-to-end speed guarantee.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Guibas and Mardani share first authorship, second leads project. Quasi-linear sequence-length cost assumes fixed channel/block dimensions and is not exact attention-output equivalence, arbitrary-resolution accuracy or a blanket end-to-end speed guarantee.

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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