This research addresses the quadratic complexity challenge in transformer models, enabling them to process longer sequences efficiently.

• Applies sparse graph processing techniques to attention mechanisms
• Reduces memory and computational requirements for long context lengths
• Maintains model performance while extending possible sequence length
• Offers practical solutions for handling larger inputs in transformers

This innovation directly impacts AI systems' ability to process longer documents, conversations, and data sequences without prohibitive computational costs—expanding the practical applications of transformer models across industries.

Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques