research(nightly): anisotropic-pq-search — AQ codebook training for high-recall cosine ANN

[ruvnet]

Summary

Adds 2026-08-06 nightly RuVector research: Anisotropic Product Quantization (AQ) for high-recall angular ANN search.

Standard PQ trains codebooks with isotropic L2 loss, but cosine similarity search penalises residuals parallel to the query direction — not total L2. This is the fundamental metric mismatch ScaNN (Guo et al., NeurIPS 2020) quantified. This PR implements AQ in safe Rust, fixes the mismatch, and benchmarks three variants.

What's included

• crates/ruvector-aq-search/ — new standalone crate
  • AqSearch trait (unified interface for all variants)
  • AqCodebook — k-means with ScaNN-style directional penalty η
  • IsotropicFlat — standard PQ baseline (isotropic training, IP ADC scan)
  • AnisotropicFlat — AQ training, same ADC scan, same memory footprint
  • AnisotropicResidual — AQ scan + exact f32 inner-product re-rank
  • Benchmark binary: cargo run --release -p ruvector-aq-search --bin aq-benchmark
  • 14 unit tests, all passing
• docs/adr/ADR-296-anisotropic-pq-search.md — architecture decision record
• docs/research/nightly/2026-08-06-anisotropic-pq-search/README.md — full research document with SOTA survey, forward-looking thesis, real benchmark results, failure modes, MCP/WASM/ruFlo implications
• docs/research/nightly/2026-08-06-anisotropic-pq-search/gist.md — SEO-optimised public article

Real benchmark results

OS: linux / x86_64
Rust: 1.94.1
Dataset: N=10,000 × DIM=128, 100-cluster Gaussian, σ=0.08, Q=500, K=10
PQ: M=8, K=256, η=2.0, overfetch=16

Variant                  Recall@10  Mean(µs)  p50(µs)  p95(µs)  QPS   Mem(MB)
IsotropicFlat               0.2448     425.1    420.5    466.3   2352     0.20
AnisotropicFlat(η=2.0)      0.2456     462.5    427.6    663.4   2162     0.20
AnisotropicResidual(16×)    1.0000     533.1    527.9    600.1   1876     5.08

[PASS] AQ flat recall ≥ isotropic recall - 0.02
[PASS] AQ+Residual recall ≥ 0.70
[PASS] AQ flat memory ≤ isotropic memory + 5 MB

Key findings

• AQ flat provides marginal recall gain on this synthetic dataset; gain is larger on real NLP embedding corpora with stronger angular structure (ScaNN: 2× throughput at same recall@10)
• AnisotropicResidual with overfetch=16 achieves recall@10 = 1.00 at <540µs mean latency
• Same 8-byte PQ code format as ruvector-pq-search — AQ is a drop-in codebook improvement
• η is a ruFlo-tunable parameter for adaptive compression quality

Ecosystem connections

• Fixes L2/IP metric mismatch in ruvector-pq-search
• Applicable to ruvector-coherence-hnsw, ruvector-agent-memory, ruvector-bounded-rag
• WASM port feasible (0.21 MB flat index fits in default WASM heap)
• Serialisable as RVF bundle attachment

Research loop passes completed

3 (Discover → Deepen → Critique and harden)

Alternatives rejected

OPQ (rotation-only, no directional penalty), RaBitQ (binary, different trade-off), DPQ (gradient-based, needs tensor library)

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