I built a similarity search algorithm that beats FAISS HNSW on both speed     

and recall. No graph construction, no learned indexes — phase-correlation routing with multi-probe search. O(1) per query, scales to any dimension.

Tested on the same data, same machine (RTX 4060), same queries, verified against brute-force ground truth:

VOIS Similarity Search — Patent Pending 100K vectors, 128-dim, top-10 retrieval, RTX 4060

                          QPS        Recall
  VOIS v3 (3 probes):   112,879      95.2%
  VOIS v3 (4 probes):   100,426      99.5%
  VOIS v3 (5 probes):    94,812     100.0%
  FAISS HNSW:            41,668      92.1%

  2.7x faster than HNSW with better recall

  Multi-dimension scaling (seperate method, same hardware)

  100K vectors, top-10, RTX 4060

  DIM    VOIS QPS    VOIS Recall   HNSW QPS   HNSW Recall
  128     145,163       100.0%       70,956       89.3%
  256     79,349        99.99%      31,875       80.9%
  512     29,750       100.0%       12,901       78.6%
  768     40,274        97.9%        8,991       74.3%

  500K vectors:

  DIM    VOIS QPS    VOIS Recall   HNSW QPS   HNSW Recall
  128    32,696       100.0%       31,139       67.3%
  256    14,384       100.0%       17,109       50.9%
  512    5,864       100.0%        8,700       41.8%

  HNSW recall collapses at higher dimensions. VOIS is immune.
  100% recall achieved at 2 probes for 512-dim and 768-dim.