V3DB: Audit-on-Demand Zero-Knowledge Proofs for Verifiable Vector Search over Committed Snapshots

Dense retrieval services underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically see only a top- k list with no auditable execution evidence. We present V3DB, a verifiable, versioned vector-search service that checks on demand whether an untrusted provider's approximate nearest-neighbor (ANN) result was produced by executing published IVF-PQ semantics on a committed snapshot. V3DB commits to each corpus snapshot and standardizes IVF-PQ into a fixed-shape, five-step query semantics. Given a public commitment and query embedding, the service returns top- k payloads and, when challenged, produces a succinct zero-knowledge proof that the output follows these semantics on the committed snapshot, without revealing corpus embeddings or private index contents to the verifier during audit. To make proving practical, V3DB avoids costly in-circuit sorting and random access with multiset equality/inclusion checks plus lightweight boundary conditions. Our Plonky2 prototype proves up to 22× faster and uses up to 40% less peak memory than the circuit-only baseline, with millisecond verification.