Coordination-Free Lane Partitioning for Convergent ANN Search

Production vector search systems often fan out each query across parallel lanes (threads, replicas, or shards) to meet latency service-level objectives (SLOs). In practice, these lanes rediscover the same candidates, so extra compute does not increase coverage. We present a coordination-free lane partitioner that turns duplication into complementary work at the same cost and deadline. For each query we (1) build a deterministic candidate pool sized to the total top-k budget, (2) apply a per-query pseudorandom permutation, and (3) assign each lane a disjoint slice of positions. Lanes then return different results by construction, with no runtime coordination. At equal cost with four lanes (total candidate budget 64), on SIFT1M (1M SIFT feature vectors) with Hierarchical Navigable Small World graphs (HNSW) recall@10 rises from 0.249 to 0.999 while lane overlap falls from nearly 100% to 0%. On MS MARCO (8.8M passages) with HNSW, hit@10 improves from 0.200 to 0.601 and Mean Reciprocal Rank at 10 (MRR@10) from 0.133 to 0.330. For inverted file (IVF) indexes we see smaller but consistent gains (for example, +11% on MS MARCO) by de-duplicating list routing. A microbenchmark shows planner overhead of ~37 microseconds per query (mean at the main setting) with linear growth in the number of merged candidates. These results yield a simple operational guideline: size the per-query pool to the total budget, deterministically partition positions across lanes, and turn redundant fan-out into complementary coverage without changing budget or deadline.