Winnex Definitive Benchmark v1.0 Complete empirical evaluation of the Winnex AI Stack vs FAISS baselines across 3 datasets, 16 methods, and 12 metrics. Summary This benchmark establishes exactly what the Winnex AI Stack delivers by measuring all methods against FAISS baselines on validated datasets (SIFT-1M, News Category, Synthetic uniform sphere). Methods Tested (16 variants) Winnex: MadhavaCore [64,128], [32,64], MadHybrid np=5/10/15, HMC Hierarchical, H4+M10 Gate FAISS: HNSW ef=32/64/128/256, IVF nprobe=1/10/20/50, PQ m=16, FlatIP (exact) Key Results SIFT-50K MadhavaCore [64,128]: NDCG=1.000, Latency=1.42ms, Build=0.09s, Zero bound violations Synthetic 100K MadhavaCore [64,128]: NDCG=0.998, Build 23x faster than HNSW (0.23s vs 15s) Near-tie H4+M10 Gate: flp_bad=0 across all gap levels, Spearman rho=0.9996 Verified Advantages Mathematical bound guarantee per excluded document (unique to Madhava) Build speed: 5-65x faster than HNSW Zero bound violations across 254M+ query-vector pairs Deterministic and CPU-only inference H4+M10 eliminates false positives in near-tie scenarios Limitations Latency 3-10x higher than HNSW (Python vs C++/SIMD) NDCG degrades on uniform data at N > 100K MadHybrid adds complexity without consistent gains over simple MadhavaCore Kaggle: https://www.kaggle.com/code/kleniopadilha/winnex-definitive-benchmark-v1-0 License: BSL 1.1 | pay@winnex.ai