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gMLP: cross-token spatial gating without self-attention

Contribution grade

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

Reference score80,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined channel projections with multiplicative gates driven by learned spatial projections, testing attention-free blocks on image classification and masked language modeling.

Scientific scope

Combined channel projections with multiplicative gates driven by learned spatial projections, testing attention-free blocks on image classification and masked language modeling. The spatial projection is tied to sequence length and has quadratic sequence cost in the original formulation. Attention-free does not mean token-independent or linear-time. Matching a Transformer on selected tasks may require greater model capacity.

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 spatial projection is tied to sequence length and has quadratic sequence cost in the original formulation. Attention-free does not mean token-independent or linear-time. Matching a Transformer on selected tasks may require greater model capacity.

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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