Beyond Pairwise Attention: Higher-Order Modular Attention for Efficient Sequence Learning

arXiv:2603.11133 · cs.LG · Submitted 2026-03-11 · Read on arXiv

cs.LG

Submitted: 2026-03-11

Updated: 2026-09-24

Code: https://github.com/samiraslani/HOMA-Higher-Order-Modular-Attention

Terminology

• Accuracy — 25× in this paper · explained in 1 episode
• Transformer — 16× in this paper · explained in 2 episodes
• Attention Mechanism — 12× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Higher-Order Modular Attention — 7× in this paper
• Hamming Distance — 5× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Hypergraph — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Computational Biology — 2× in this paper · explained in 2 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Representational Strengths and Limitations of Transformers
• Poly-attention: a general scheme for higher-order self-attention
• Linformer: Self-Attention with Linear Complexity
• Longformer: The Long-Document Transformer
• Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

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