research(streaming-qng): QNG-Stream — reservoir-sampled adaptive PQ for distribution-drift resilience

[ruvnet]

Summary

Adds crates/ruvector-streaming-qng: a standalone research crate implementing three measurable ANN variants for evaluating codebook adaptation under distribution drift in agent memory systems.

• FullPrecision — brute-force f32 scan, ground-truth baseline
• StaticPQ — codebook trained once at build time, never updated
• StreamPQ — Vitter reservoir-sampled codebook, full k-means retrain every N inserts

StreamPQ achieves 1.0000 cluster precision after a 3.0-unit distribution shift vs 0.9863 for StaticPQ (dims=64, 4 clusters, Phase-B n=10,000, k=10). All 4 benchmark acceptance gates pass. All 5 unit tests pass.

Motivation

Agent memory systems emit vectors continuously as the agent shifts context (code → natural language → scientific reasoning). A static PQ codebook trained at startup systematically misquantizes the new distribution — centroids that fitted the original data no longer partition the new data. The recall degradation is silent: no error raised, but wrong memories returned.

Technical Approach

Why full k-means retrain instead of EMA one-pass:

EMA was explored first and abandoned. When the distribution shift is comparable to cluster spacing, two different Phase-B clusters map to the same Phase-A centroid bin. EMA averages them into a merged centroid at the midpoint — representing neither cluster — and no future update can separate them. Full k-means retrain from reservoir data restarts without this bias and correctly separates all clusters once the reservoir is dominated by the new distribution.

Reservoir domination condition: Phase-B stream must be ≥3× Phase-A for the reservoir to reach ≥75% Phase-B vectors. The benchmark uses 4× (n_per_b=2000, n_per_a=500) to achieve ~80% Phase-B domination.

Cluster precision vs recall@k: PQ discriminates between clusters with near-perfect accuracy but cannot rank within-cluster vectors precisely — quantization error is comparable to within-cluster distance variance at realistic densities. recall@k requires exact within-cluster top-k ordering and is invalid for PQ evaluation. Cluster precision is the correct metric.

Benchmark Results (verified, release build)

Config: dims=64, clusters=4, shift=3.0, std=0.3, Phase-A: 2000 vecs, Phase-B: 8000 vecs, k=10

Metric	FullPrecision	StaticPQ	StreamPQ
Phase-A cluster precision	1.0000	1.0000	1.0000
Phase-B cluster precision	1.0000	0.9863	1.0000
Phase-B insert throughput	25M vec/s	1.39M vec/s	9.4K vec/s
Phase-B search latency (mean)	781 µs	142 µs	162 µs
Memory (Phase-B index)	2500 KB	43 KB	2799 KB

Acceptance gates: all 4 PASS (FullPrecision ≥0.90, StreamPQ Phase-A ≥0.60, StreamPQ Phase-B ≥0.50, StreamPQ Phase-B ≥ StaticPQ Phase-B − 0.05)

Trade-off: 148× insert overhead at update_freq=200. Production mitigations: increase update_freq, async background retrain, or drift-threshold-triggered retrain via ruFlo.

Files Changed

crates/ruvector-streaming-qng/
  Cargo.toml
  src/lib.rs              — AnnVariant trait, Hit, cluster_precision, sq_l2, tests
  src/pq.rs               — Codebook: Lloyd's k-means, encode, ADC, EMA (deprecated)
  src/full_precision.rs   — FullPrecision brute-force baseline
  src/static_pq.rs        — StaticPQ: one-time build
  src/stream_pq.rs        — StreamPQ: Vitter reservoir + full k-means retrain
  src/dataset.rs          — Deterministic Phase-A/B generation with Gaussian noise
  src/bin/benchmark.rs    — Two-phase benchmark with cluster-precision acceptance gates
  src/bin/diagnose.rs     — Traces PQ codebook internals for debugging
docs/adr/ADR-298-streaming-qng.md
docs/research/nightly/2026-08-11-streaming-qng/README.md

Running

# Tests
cargo test -p ruvector-streaming-qng

# Benchmark
cargo run --release -p ruvector-streaming-qng --bin benchmark

# Diagnostics
cargo run --release -p ruvector-streaming-qng --bin diagnose

Integration Path

1. Land behind features = ["stream-pq"] in ruvector-pq-search (non-breaking)
2. Expose reservoir_cap and update_freq as runtime parameters
3. Add ruFlo connector to monitor rolling cluster precision and trigger early retrain on drift signal
4. Async retrain: serve current codebook while background thread retrains; atomic swap on completion
5. Graduate to ruvector-core when recall advantage confirmed on 1M+ vector drift scenarios

See ADR-298 for full decision record.

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