arXiv:2606.08191v1 Announce Type: new 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 o
The continuous drive for more efficient AI model architectures and specialized applications in areas like synthetic biology makes novel token aggregation methods crucial.
Improved token aggregation can significantly enhance the performance and efficiency of AI models across diverse domains, particularly for complex biological and image data, reducing training costs and improving accuracy.
This new FLaG module offers a more sophisticated method for processing token representations, potentially leading to more powerful and generalizable AI applications in critical scientific fields.
- · AI model developers
- · Synthetic biology companies
- · Drug discovery research
- · Bioinformatics
- · Traditional token pooling methods
- · AI models constrained by aggregation bottlenecks
More accurate and efficient AI models for diverse data types, especially biological sequences.
Accelerated discovery of new antimicrobial peptides and other biological compounds, impacting pharmaceuticals and agriculture.
Enhanced AI capabilities contribute to a broader platform effect for synthetic biology, enabling new product development and market expansion.
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Read at arXiv cs.LG