Explainability Mechanisms in High-Dimensional Neural Representations

Hierarchical Sparse Transformer Architectures for Computationally Efficient Long-Sequence Modeling This repository accompanies the research paper Hierarchical Sparse Transformer Architectures for Computationally Efficient Long-Sequence Modeling by Ashwanth Krishna H, Feroan Mothy JJ, and Alwin Johith A. The work introduces a Hierarchical Sparse Transformer (HST) architecture designed to address the scalability limitations of conventional transformer models when processing long sequences. Standard self-attention mechanisms exhibit quadratic computational complexity with respect to sequence length, making them inefficient for large-scale applications such as scientific document analysis, genomic sequence modeling, long video understanding, and large code repositories. The proposed HST architecture combines three complementary components: Local sparse attention windows for efficient neighborhood interaction. Global contextual tokens for long-range information exchange. Hierarchical token pooling for multi-scale representation learning. Through structured sparse attention and hierarchical aggregation, the model reduces computational complexity from O(n²) to approximately O(n log n), significantly improving memory efficiency and scalability while preserving strong contextual modeling capabilities. This repository includes: Research paper and supplementary materials. PyTorch implementation of the Hierarchical Sparse Transformer. Sparse attention modules and hierarchical pooling components. Experimental configurations and benchmark evaluation details. Complexity analysis and reproducibility resources. The proposed architecture demonstrates competitive performance on long-context sequence modeling tasks while providing substantial reductions in computational cost, making it suitable for next-generation large-scale transformer applications.