FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation

• URL: http://arxiv.org/abs/2609.00831v1
• Date: Tue, 01 Sep 2026 07:32:44 GMT
• Title: FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation
• Abstract summary: Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the original token space.<n>We introduce Frequency-Domain Latent-attention Gated Pooling (FLaG), a plug-in aggregation module that re-expresses encoder outputs in the domain before final pooling.<n>FLaG represents nonredundant rFFT spectrum through real and imaginary components, summarizes spectral tokens with learnable latent queries, derives a sample-conditioned gate, and reconstructs modulated token representations for downstream aggregation.
• Score: 6.298319903082888
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the original token space. We introduce Frequency-Domain Latent-attention Gated Pooling (FLaG), a plug-in aggregation module that re-expresses encoder outputs in the Fourier domain before final pooling. FLaG represents the nonredundant rFFT spectrum through concatenated real and imaginary components, summarizes spectral tokens with learnable latent queries, derives a sample-conditioned channel gate, and reconstructs modulated token representations for downstream aggregation. We evaluate the same architecture across ESM2-based antimicrobial peptide (AMP) activity prediction, ResNet18 image classification on CIFAR-10 and CIFAR-100, and three RoBERTa-based language tasks. FLaG achieves the best macro-averaged Spearman correlation coefficient, RMSE, and Recall@50 across four AMP backbone-species settings and the highest top-1 accuracy on CIFAR 10. It also achieves the best mean results on five of seven language metrics, although mean pooling remains strongest on STSBenchmark. AMP-side mechanistic analyses reveal low-frequency prediction sensitivity across most encoder layers, with increased relative high-frequency sensitivity in the final layer, and pronounced peptide-specific positional responses. The residual gate broadly amplifies spectral channels while preserving the low-frequency-dominated energy profile, whereas latent cross-attention exhibits sample- and species-specific spectral allocation. Overall, FLaG provides a transferable frequency-domain aggregation bias across protein, visual, and textual representations, with benefits that depend on the backbone and downstream task. Supplementary materials, source code, and data are available at https://www.healthinformaticslab.org/supp/ and https://github.com/Kewei2023/AMPCliff/tree/FLaG.

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