Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation

• URL: http://arxiv.org/abs/2606.08191v1
• Date: Sat, 06 Jun 2026 14:21:55 GMT
• Title: Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
• Abstract summary: Token aggregation is a common bottleneck in models that map token representations to sample-level predictions.<n>We propose FLaG, a plug-in aggregation module that transforms token representations with the real FFT.<n>We evaluate FLaG on antimicrobial peptide (AMP) activity prediction with ESM2, image classification with ResNet18, and text classification with RoBERTa.
• Score: 6.535173782861673
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. We propose FLaG, a plug-in aggregation module that transforms token representations with the real FFT, summarizes spectral components with learnable latent queries, applies a channel-wise gate, and reconstructs enhanced time-domain tokens for final pooling. We evaluate FLaG on antimicrobial peptide (AMP) activity prediction with ESM2, image classification with ResNet18 on CIFAR-10 and CIFAR-100, and text classification with RoBERTa on IMDB and GLUE. FLaG achieves its clearest gains on the ESM2-8M antimicrobial peptide tasks and on CIFAR-100, while remaining competitive with strong text baselines on IMDB and GLUE. Then we probe its behavior on the AMP setting with band knockouts, gate summaries, residue perturbations, latent-query readouts, and structure-proxy stratification. We find that low-frequency bands contribute the most overall, and the remaining higher-band pattern is more sample-specific. The gate acts as a broadly shared spectral reweighting stage and the cross-attention patterns are sample-specific with mild query-wise differentiation, and higher-helix peptides exhibit stronger average spectral sensitivity in both bacteria. The supplementary materials, source code and data are released at https://www.healthinformaticslab.org/supp/ and https://github.com/Kewei2023/AMPCliff/tree/FLaG.

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