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May 26, 2026MathematicsOpen Access

Sparse Projection Attention: A Computationally Efficient Framework for Long Sequence Modeling

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Authors

MAM. Chrifi AlaouiNJNour-eddine JoudarMEMohamed Ettaouil

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Overview

Randomized trial shows efficient attention computation in long sequences, suggesting improved transformer accessibility.

Key Points

  • The aim is to develop a more efficient attention mechanism for long sequence modeling to overcome the limitations of existing self-attention methods.
  • Proposed Sparse Projection Attention (SPA) using learnable sparse projections.
  • Grounded in the Johnson–Lindenstrauss lemma, ensuring distance preservation.
  • Included mathematical analysis like error bounds and convergence analysis.
  • Achieved up to 8× speedup in attention score computation.
  • Approximately 2× end-to-end speedup while maintaining competitive performance.
  • Improved accessibility for resource-constrained environments and real-time applications.

Cite This Study

Alaoui et al. (2026) studied this question.

synapsesocial.com/papers/6a153950b5d9c58d83e8cbf3https://doi.org/10.3390/math14111813
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