A recent article published on arXiv provides a comprehensive review of simulation-based problems involving complex hierarchical input spaces, with a particular focus on challenges in data representation and modeling. The authors highlight difficulties in handling mixed-variable inputs within these hierarchical structures, which complicate optimization processes. To address these challenges, the article proposes a unified framework designed to simplify existing surrogate-based methods. This framework aims to enhance the efficiency and effectiveness of architecture design by improving optimization over mixed-variable inputs. The contribution is positioned as significant within the field, offering a streamlined approach that consolidates prior methodologies. By doing so, it advances the state of the art in surrogate-based architecture design, potentially facilitating better design outcomes in complex hierarchical settings. This work aligns with ongoing efforts to refine modeling techniques for hierarchical spaces in simulation contexts.