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FNet: fixed Fourier mixing in language encoders

Contribution grade

Sequence-model architectures · Human-designed research method or resource

Reference score105,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Replaced encoder self-attention with real-valued outputs of Fourier transforms over sequence and hidden dimensions, retaining feed-forward blocks and studying speed–accuracy tradeoffs.

Scientific scope

Replaced encoder self-attention with real-valued outputs of Fourier transforms over sequence and hidden dimensions, retaining feed-forward blocks and studying speed–accuracy tradeoffs. Fourier mixing is not an approximation that preserves each attention matrix or output. FFT and dense-DFT implementations have different complexity and device tradeoffs. Reported speedups use particular encoder tasks and hardware and do not establish drop-in causal decoding equivalence.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Fourier mixing is not an approximation that preserves each attention matrix or output. FFT and dense-DFT implementations have different complexity and device tradeoffs. Reported speedups use particular encoder tasks and hardware and do not establish drop-in causal decoding equivalence.

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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