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Higher-Order Modular Attention: Fusing Pairwise and Triadic Interactions for Protein Sequences

arXiv:2603.11133v1 Announce Type: new Abstract: Transformer self-attention computes pairwise token interactions, yet protein sequence to phenotype relationships often involve cooperative dependencies among three or more residues that dot product attention does not capture explicitly. We introduce Higher-Order Modular Attention, HOMA, a unified attention operator that fuses pairwise attention with an explicit triadic interaction pathway. To make triadic attention practical on long sequences, HOMA employs block-structured, windowed triadic attention. We evaluate on three TAPE benchmarks for Secondary Structure, Fluorescence, and Stability. Our attention mechanism yields consistent improvements across all tasks compared with standard self-attention and efficient variants including block-wise attention and Linformer. These results suggest that explicit triadic terms provide complementary representational capacity for protein sequence prediction at controllable additional computational cos

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Shirin Amiraslani, Xin Gao · · 1 min read · 9 views
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arXiv:2603.11133v1 Announce Type: new Abstract: Transformer self-attention computes pairwise token interactions, yet protein sequence to phenotype relationships often involve cooperative dependencies among three or more residues that dot product attention does not capture explicitly. We introduce Higher-Order Modular Attention, HOMA, a unified attention operator that fuses pairwise attention with an explicit triadic interaction pathway. To make triadic attention practical on long sequences, HOMA employs block-structured, windowed triadic attention. We evaluate on three TAPE benchmarks for Secondary Structure, Fluorescence, and Stability. Our attention mechanism yields consistent improvements across all tasks compared with standard self-attention and efficient variants including block-wise attention and Linformer. These results suggest that explicit triadic terms provide complementary representational capacity for protein sequence prediction at controllable additional computational cost.

Executive Summary

The article introduces Higher-Order Modular Attention (HOMA), a novel attention mechanism that combines pairwise and triadic interactions for protein sequence analysis. HOMA is designed to capture cooperative dependencies among three or more residues, which are not explicitly captured by traditional self-attention mechanisms. The authors evaluate HOMA on three TAPE benchmarks and demonstrate consistent improvements over standard self-attention and efficient variants. The results suggest that explicit triadic terms provide complementary representational capacity for protein sequence prediction at a controllable additional computational cost.

Key Points

  • ▸ Introduction of Higher-Order Modular Attention (HOMA) mechanism
  • ▸ HOMA combines pairwise and triadic interactions for protein sequence analysis
  • ▸ Evaluation on three TAPE benchmarks shows consistent improvements over standard self-attention and efficient variants

Merits

Improved Representational Capacity

HOMA's explicit triadic terms provide complementary representational capacity for protein sequence prediction

Controllable Computational Cost

HOMA's block-structured, windowed triadic attention makes it practical for long sequences with controllable additional computational cost

Demerits

Increased Computational Complexity

HOMA's triadic attention pathway may increase computational complexity compared to standard self-attention mechanisms

Expert Commentary

The introduction of HOMA marks a significant advancement in protein sequence analysis, as it provides a novel attention mechanism that can capture complex cooperative dependencies among residues. The evaluation on three TAPE benchmarks demonstrates the effectiveness of HOMA in improving representational capacity for protein sequence prediction. However, the increased computational complexity of HOMA's triadic attention pathway may require careful consideration of computational resources and optimization strategies. Overall, HOMA has the potential to contribute significantly to the field of protein sequence prediction and related areas of research.

Recommendations

  • ✓ Further evaluation of HOMA on diverse protein sequence prediction tasks to assess its generalizability and robustness
  • ✓ Investigation of optimization strategies to mitigate the increased computational complexity of HOMA's triadic attention pathway

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