Michael O'Boyle a1a04a3570 bench: comprehensive quantization method comparison (8 methods, 3 datasets) ... First benchmark comparing all ruvector-core quantization methods against TurboQuant on standard vector search datasets. 8 configurations, 3 datasets (GloVe d=200, SIFT d=128, PKM d=384), 3 trials per config with variance. Key findings: - Int4 beats TurboQuant MSE on recall at 8x compression (91.2% vs 89.6% R@1) - QJL correction hurts recall for vector search (9-41% loss) - PQ with 8 subspaces fails at d=200 (18.2% R@1) - TurboQuant MSE 3-bit fills unserved 10.7x compression tier (82.0% R@1) - QuantizedVector::distance() never called during HNSW search Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>		2026-04-19 21:37:14 -04:00
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results	bench: comprehensive quantization method comparison (8 methods, 3 datasets)	2026-04-19 21:37:14 -04:00
ANALYSIS.md	bench: comprehensive quantization method comparison (8 methods, 3 datasets)	2026-04-19 21:37:14 -04:00
benchmark_quantized_search.py	bench: comprehensive quantization method comparison (8 methods, 3 datasets)	2026-04-19 21:37:14 -04:00