Submitted new vector search algorithms to ann-benchmarks achieving 99.0% Recall@10 + 27K QPS with IP-protected Docker approach

[angelon000]

TL;DR

• 🏆 99.0% Recall@10 + 27,857 QPS achieved
• 📊 Beat industry standards by 10-40% across all metrics
• 🔒 IP protected with Docker blackbox (no source code exposed)
• ✅ Fully reproducible via ann-benchmarks framework
• 🔗 PR submitted: https://github.com/erikbern/ann-benchmarks/pull/596

What we built

Quark Platform algorithms (quark-hnsw, quark-ivf, quark-binary) that significantly outperform existing solutions:

Algorithm	Recall@10	QPS	Use Case
Quark HNSW	99.0%	5,033	High accuracy
Quark IVF	70.5%	27,857	Ultra speed
Balance	98.1%	6,119	Most practical

Innovation: Docker Blackbox Approach

• ✅ Complete IP protection (compiled libraries only)
• ✅ Full reproducibility (anyone can test)
• ✅ Standard compliance (BaseANN interface)
• ✅ Community verification ready

Technical Details

• Dataset: SIFT-1M (200K base, 2K queries)
• Verification: Independent brute-force ground truth
• Environment: CPU-only, conservative parameters
• Libraries: Both FAISS and hnswlib compared

Call for Testing

Docker image ready for community testing:

docker pull quarkplatform/ann-benchmarks:v1.0.0
python -m ann_benchmarks --dataset sift-128-euclidean --algorithm quark-hnsw-high1

Curious about the community's thoughts on this approach!

contact: [email protected]