HNSW is a data structure and algorithm designed for ANN search that leverages skip lists and small world graphs. While a comprehensive overview of HNSW is available in the original paper, we summarize relevant components for this study. The HNSW algorithm constructs a hierarchical graph structure by sequentially inserting elements into the existing graph. It assigns each element a maximum layer within the hierarchy according to an exponentially decaying random variable. Then, the algorithm inserts the element into this maximum layer and all layers below, resulting in a sparse top layer and a bottom layer with all elements. Element insertion employs a greedy search to locate efConstruction approximate nearest neighbors per layer and bidirectionally connects the element to M of these neighbors in each layer (2*M in the bottom layer). To choose the M connected neighbors from the efConstruction candidates, the algorithm first connects the nearest candidate to the inserted element. It then proceeds to the next nearest candidate, which is connected if it is nearer to the inserted element than it is to any currently connected neighbors, otherwise, it is skipped. This process continues until the algorithm connects M neighbors to the inserted element or evaluates all efConstruction candidates (Figure ). According to the original paper, this strategy encouraged diverse connections among highly clustered data. Modifying the parameters efConstruction and M influenced the HNSW construction speed, memory requirements, and the recall rate of our RAD traversal method.