The research paper presents PageANN, a novel framework engineered to overcome the severe latency and scalability limitations facing existing disk-based Approximate Nearest Neighbor Search (ANNS) methods used in vector databases. Current systems suffer from inefficient search paths and a crucial misalignment between logical graph node size and the physical I/O granularity of Solid-State Drives (SSDs). PageANN introduces a core innovation: a page-node graph structure that directly maps logical graph nodes to physical SSD pages, significantly shortening I/O traversal paths and maximizing data utility during retrieval. This is supported by a co-designed disk data layout that embeds compressed neighbor vectors within each page and a dynamic memory management strategy utilizing lightweight indexing for fast query routing. According to experimental results, PageANN consistently outperforms state-of-the-art techniques, achieving substantial gains in throughput and latency across diverse datasets and memory constraints while maintaining comparable recall accuracy. Source: https://arxiv.org/pdf/2509.25487