Hierarchy-aware attention is a deep learning mechanism that models multi-level relationships in data by organizing and aggregating features across different structural or semantic scales. While standard attention mechanisms evaluate connections uniformly across a flat sequence of tokens, hierarchy-aware attention accounts for nested structures by progressively grouping fine-grained details into higher-level representations across successive network layers. This structured aggregation allows neural networks to capture both localized components and broader conceptual contexts, improving representation learning and alignment across complex data modalities like text and images.
