On-disk graph-based indexes are widely used in approximate nearest neighbor (ANN) search systems for large-scale, high-dimensional vectors. However, traditional coupled storage methods, which store vectors within the index, are inefficient for index updates. Coupled storage incurs excessive redundant vector reads and writes when updating the graph topology, leading to significant invalid I/O. To address this issue, we propose a decoupled storage architecture. While a decoupled architecture reduces query performance. To overcome this limitation, we design two tailored strategies: (i) a three-stage query mechanism that leverages multiple PQ compressed vectors to filter invalid I/O and computations, and (ii) an incremental page-level topological reordering strategy that incrementally inserts new nodes into pages containing their most similar neighbors to mitigate read amplification. Together, these techniques substantially reduce both I/O and computational overhead during ANN search. Experimental results show that the decoupled architecture improves update speed by 10.05x for insertions and 6.89x for deletions, while the three-stage query and incremental reordering enhance query efficiency by 2.66x compared to the traditional coupled architecture.

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