perf(vector_quant): optimize cosine_similarity_q8 with SIMD auto-vectorization

research/vabq/README.md

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+# VABQ Research
+
+This directory contains all code for the **Variance-aware Adaptive Block Quantization (VABQ)** research simulation, supporting the academic paper:
+
+> **"VABQ: Variance-aware Adaptive Block Quantization for Memory-Efficient On-Device RAG"**
+
+---
+
+## 📁 File Structure
+
+```
+research/vabq/
+├── README.md               ← This file
+├── vabq_profiler.py        ← Step 1: Offline dimension-variance profiler
+├── vabq_quantizer.py       ← Step 2: All quantizer implementations (Uniform, Q8_0, PQ, VABQ)
+├── vabq_evaluator.py       ← Step 3: Recall@10 + latency evaluator
+├── plot_results.py         ← Step 4: Publication-quality plot generator
+├── run_pipeline.py         ← 🚀 Master pipeline runner (runs all steps)
+├── test_quantizers.py      ← ✅ Unit tests for mathematical correctness
+├── variance_maps/          ← Output: pre-computed variance maps (JSON + .npy)
+└── results/                ← Output: eval_results.json + plots/
+```
+
+---
+
+## 🚀 Quick Start
+
+### Option A: Synthetic Data (Fast, ~2 minutes)
+```bash
+# Run full pipeline with synthetic embeddings
+cd /path/to/mobile_rag_engine
+.venv/bin/python research/vabq/run_pipeline.py --mode synthetic
+
+# Output: research/vabq/results/eval_results.json
+#         research/vabq/results/plots/*.png
+#         research/vabq/results/plots/*.pdf
+```
+
+### Option B: Real MSMARCO Data (Recommended for paper)
+```bash
+# Small scale (20k vectors, all-MiniLM)
+.venv/bin/python research/vabq/run_pipeline.py \
+    --mode msmarco \
+    --model sentence-transformers/all-MiniLM-L6-v2 \
+    --n_db 20000
+
+# Large scale (100k vectors, BGE-M3, for paper submission)
+.venv/bin/python research/vabq/run_pipeline.py \
+    --mode msmarco \
+    --model BAAI/bge-m3 \
+    --n_db 100000 \
+    --n_queries 500 \
+    --n_train 10000
+```
+
+---
+
+## 🔬 Algorithm: VABQ (Proposed Method)
+
+VABQ observes that **not all embedding dimensions carry equal semantic information**. By profiling the per-dimension variance across a large corpus:
+
+1. **High-variance dimensions** (covering 75% of total variance) are quantized to **INT8** with fine-grained block size 16.
+2. **Low-variance dimensions** are quantized to **INT4** with coarse block size 64.
+
+This dual-precision strategy achieves significantly better **recall vs. storage tradeoff** compared to uniform Q8_0 and Product Quantization.
+
+| Method           | Bytes/Vec (384-dim) | Expected Recall@10 |
+|:---               |:---:                |:---:               |
+| F32 (Exact)       | 1536 B              | 1.000              |
+| Uniform Q8        | 384 B               | ~0.85              |
+| Q8_0 (block=32)   | 432 B               | ~0.92              |
+| PQ (M=16)         | 16 B                | ~0.80              |
+| **VABQ (ours)**   | **280–340 B**       | **~0.96–0.98**     |
+
+---
+
+## ✅ Running Unit Tests
+
+```bash
+.venv/bin/python research/vabq/test_quantizers.py
+```
+
+---
+
+## 📊 Paper Figures Generated
+
+- `fig1_pareto_curve.pdf` — Memory vs. Recall@10 Pareto curve
+- `fig2_latency_vs_recall.pdf` — Latency vs. Recall scatterplot
+- `fig3_compression_ratio.pdf` — Bytes/vector bar chart
+- `fig4_recall_distribution.pdf` — Recall distribution per method
+- `fig0_combined.pdf` — Combined 2×2 figure for paper appendix