Approximate nearest neighbor search (ANNS) underpins large-scale vector retrieval in search, recommendation, and retrieval-augmented generation. Graph-based indexes have demonstrated state-of-the-art search performance for ANNS. They connect each corpus vector to a small set of nearby or navigationally useful vertices and answer queries by traversing the resulting graph. Because these edges are selected using construction-time distances, the graph index is tied to the embedding model. Re-encoding a corpus with a new model may change distances and neighborhoods of the vectors. Reconstructing the graph for the new embedding vectors incurs substantial construction cost and delays deployment. When the embedding model changes, we observe a phenomenon in the old graph index that we call residual reachability. Specifically, although derived from different models, the vectors describe the same underlying objects and often retain part of their similarity structure. These shared relations are reflected in the connectivity of the old graph index, leaving many exact new-model neighbors reachable within a few hops in the old graph index. Motivated by this observation, we develop an index-migration approach that utilize the residual reachability in the old graph index to faster construct the new graph index for the new embedding vectors. Our method, Drift-Guided Migration (DGM), provides two migration paths. DGM-Local performs parallel shallow expansion over the inherited graph index and screens second-hop candidates with packed position sign codes before exact evaluation. DGM-Search uses hop-bounded beam traversal to explore beyond shallow expansion. Across eight text and image migrations, our DGM methods can achieve up to 17.43 times speedup on constructing the new graph index than the fastest degree-matched reconstruction method while keeping competitive recalls.

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