🤖 AI Summary
This work proposes an efficient hierarchical sequence modeling architecture based on a binary tree structure to address the high computational complexity of standard self-attention and its difficulty in capturing hierarchical dependencies in long sequences. The method introduces a binary tree reduction mechanism with hierarchical inductive bias, replacing conventional self-attention with recursively applied Gated Linear Units (GLUs). This design achieves O(log n) parallel depth while maintaining O(n) space complexity. Experimental results demonstrate that the proposed model significantly outperforms standard Transformers on long-sequence tasks, exhibiting faster convergence and higher accuracy—particularly excelling in tasks where hierarchical dependency structures are critical.
📝 Abstract
Work introduces a hierarchical binary tree-based reduction that replaces standard self-attention. The core idea is to use a recursive Gated Linear Unit merge operation, achieving O(n) total merge operations O(log n) parallel depth O(n d^2) total work and O(n) space complexity. In these experiments, the model significantly outperforms standard Transformers in both convergence speed and accuracy on long-range structural dependencies, specifically where hierarchical inductive bias is critical.