ruvector 2026: Batched HNSW with Shared Candidate Frontier — Rust vector search, 71-97% fewer vector loads on correlated query batches (RAG, ColBERT/MaxSim, MMR), zero recall loss.

Summary (≤150 chars): Rust HNSW backend that shares candidate vectors across correlated query batches — saves 71-97% vector loads, 1.24× faster, zero recall loss.

If your Rust vector search workload is a RAG loop with multi-turn queries, an MMR / diverse-beam reranker, or a multi-vector retrieval (ColBERT / MaxSim / MuVera), you are running correlated query batches — and every mainstream HNSW library (FAISS, Milvus, Qdrant, Weaviate, Pinecone, LanceDB) treats those queries as independent, re- each candidate vector's cache lines Q times. This ruvector nightly research (2026-09-26, ADR-347) publishes a Rust HNSW search backend that shares distance computations across the batch: 71 – 97 % fewer vector loads and 1.24 × faster wall clock at Q = 16 on Apple M4 Max, with recall bit-for-bit identical to per-query search.

Keywords: rust vector search, HNSW, ANN, ruvector, batched search, RAG retrieval, ColBERT, multi-vector retrieval, approximate nearest neighbor, vector database.

• Three swappable backends behind one BatchedSearch trait:

  • IndependentSearch — baseline per-query greedyef -search.
  • BatchedKernelSearch — one candidate vector × Q queries per distance call.
  • SharedFrontierSearch — batch-wide evaluated set + cached per-query distance rows.

• Deterministic (rand_chacha ), single-crate, no unsafe.

• Real bench binary (cargo run --release --bin bsf-bench ) that publishes verifiable numbers.

• 6 tests including a differential vector-load-count assertion.

• Recall-preserving. Each query still owns its own frontier heap and result heap; recall matches the independent baseline exactly.

• Memory-bounded. Cache footprint isQ · N · 4 bytes worst case (~0.6 MiB at Q=32, N=5 000). Dense today, sparse-swappable for large N.

• Drop-in.BatchedSearch is a five-method trait; existing per-query call sites replacesearch withsearch_batch .

Apple M4 Max (arm64), Darwin 24.6.0, rustc 1.98.1, release profile. Dataset: n = 5 000, dim = 128, 32 Gaussian clusters. Correlated batch generator. k = 10, ef = 64. Numbers from cargo run --release --bin bsf-bench.

• Vector-load savings 71 – 97 % across the whole sweep.

• Wall-clock speedup 1.10 – 1.24 × vs the independent baseline.

• Zero recall degradation — every backend returns identical top-k.

• Batched L2 kernel with a 4× query-row unroll keeps the candidate vector hot in registers across queries.

• Dense Q · N distance-row cache — branch-free hot path.

• Per-query heaps preserved so top-k semantics don't change.

• Round-robin frontier scheduling so cache-warmth is exploited.

• Future work (from the research doc): NEON / AVX-512 kernel, query-centroid warm-up, RaBitQ integration, sparse cache for N > 1 M, adaptive backend router.

• Source (CrossGen-ai fork, research branch):https://github.com/CrossGen-ai/RuVector/tree/research/nightly/2026-09-26-hnsw-batch-shared-frontier/crates/ruvector-hnsw-batch-shared-frontier

• ADR:docs/adr/ADR-347-hnsw-batch-shared-frontier.md

• Research doc:docs/research/nightly/2026-09-26-hnsw-batch-shared-frontier/README.md

• Upstream project:github.com/ruvnet/RuVector

git clone https://github.com/CrossGen-ai/RuVector.git
cd RuVector
git checkout research/nightly/2026-09-26-hnsw-batch-shared-frontier
cargo test  --release -p ruvector-hnsw-batch-shared-frontier
cargo run   --release -p ruvector-hnsw-batch-shared-frontier --bin bsf-bench

Tags: rust, hnsw, vector-search, ann, rag, ruvector, colbert, multi-vector-retrieval, nearest-neighbor, approximate-nearest-neighbor.