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ADR-261: RuVector Graph-ANN Index — a real HNSW baseline + a SymphonyQG-style quantized variant, MEASURED

Field	Value
Status	Accepted
Date	2026-06-14
Deciders	ruv
Codebase target	`wifi-densepose-ruvector` — `hnsw.rs`, `hnsw_quantized.rs`, `ann_measure.rs`, `benches/ann_bench.rs`, docs
Relates to	ADR-084 (RaBitQ similarity sensor — 1-bit sketch), ADR-156 (RuVector beyond-SOTA sweep — §5 #1 SymphonyQG, §8/§10/§11 RaBitQ Pass-2/multi-bit/estimator), ADR-024 (AETHER re-ID), ADR-016/017 (RuVector integration)
Scope	Build the missing HNSW graph-ANN baseline in the ruvector retrieval path, build a SymphonyQG-style quantized-traversal variant on the same graph, and MEASURE the real recall/QPS ratio between them — closing the ADR-156 §5 #1 gap honestly. Resolves ADR-156 §8 backlog item "SymphonyQG reproduction" from CLAIMED-only to MEASURED-direction-tested.

0. PROOF discipline (this ADR's contract)

This project has been publicly accused of "AI slop." This ADR answers with evidence, not adjectives — the same contract as ADR-154/156:

The HNSW index ships a committed recall@10 correctness gate (≥ 0.95 vs brute force on a planted-cluster fixture). Low recall means a graph bug; the gate is wired to fail in that case. It did fail first — and caught a real index-out-of-bounds bug in the insert path (§4) — which is exactly what a real gate is for.
Every QPS/recall number below is MEASURED on this box with a committed, deterministic, --no-default-features-runnable measurement (src/ann_measure.rs, ann_bench_report) and a committed criterion bench (benches/ann_bench.rs). Both call one shared fixture/measurement module, so the bench and the report can never measure different graphs.
The headline result is an honest negative: at our test scale the SymphonyQG-style quantized variant does not beat float HNSW at equal recall — the 1-bit Hamming traversal is too coarse to keep recall up. We report the real numbers, explain why, and state the expected large-N crossover. We did not tune the quantized path to manufacture the 3.5–17× the source claims. A measured negative + a scale caveat is a valid, publishable result.
We are explicit that this is OUR HNSW + OUR 1-bit quantization, not SymphonyQG's exact system. It tests the direction of the claim on our hardware/data, not a 1:1 reproduction.

Test machine: Windows 11, cargo test --release, std::time::Instant wall-clock. Numbers are warm medians on this box; the ratio is the claim, not the absolute QPS.

Reproduce:

cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --release \
  ann_bench_report -- --nocapture
# Larger N: ANN_BENCH_N=50000 cargo test ... --release ann_bench_report -- --nocapture
cargo bench -p wifi-densepose-ruvector --bench ann_bench

1. Context

The ruvector crate's retrieval path — AETHER re-ID hot-cache (ADR-024), the sketch.rs 1-bit prefilter (ADR-084), room fingerprinting — is, at its core, an approximate nearest-neighbour (ANN) problem: dense float embedding in, top-K similar ids out. But the crate had no graph index. Every topk was either a linear scan (O(N·d) per query) or a 1-bit Hamming prefilter over a linear scan. That is O(N) per query and does not scale.

ADR-156 §5 #1 graded SymphonyQG (SIGMOD 2025) the lead beyond-SOTA ANN candidate, citing the source's claim of 3.5–17× QPS over HNSW at equal recall, but marked it CLAIMED:

"author-measured; not reproduced on our hardware — reproduction is future work."

And ADR-156 §8 was blunt about why it could not be reproduced: there was no HNSW baseline to compare against. You cannot measure a ratio against a baseline that does not exist. This ADR builds that missing baseline, builds the quantized variant that tests the direction of the SymphonyQG bet, and measures the real ratio.

2. Decision

Add a correct, dependency-free float HNSW graph index (hnsw.rs): the real Malkov & Yashunin (TPAMI 2018) algorithm — multi-layer navigable small-world graph, ef_construction / ef_search, the Algorithm-4 neighbour-selection heuristic, seeded-deterministic level assignment, L2 + cosine. This is the baseline ADR-156 said was missing.
Add a SymphonyQG-style quantized-traversal variant (hnsw_quantized.rs): the same graph (same seed, same structure), but the beam search scores candidates with a cheap 1-bit Hamming distance over the RaBitQ Pass-2 rotated sign code (reusing rotation.rs + the sign-quantization of sketch.rs), then exact-float reranks the final candidate set. This is the SymphonyQG bet — cheaper per-node scoring, recovered by a final exact rerank.
Measure linear vs float-HNSW vs quantized-HNSW (recall@10, QPS, equal-recall ratios) on one deterministic planted-cluster fixture, and record the honest verdict against the SymphonyQG 3.5–17× claim.

Why 1-bit Hamming for the quantized traversal

The crate already had the exact pieces SymphonyQG fuses: a deterministic orthogonal rotation (rotation.rs, RaBitQ Pass-2) and sign-quantization (sketch.rs). A 1-bit code compares by POPCNT Hamming — a few machine words, no per-dimension float work — so it is the cheapest possible traversal score and the most direct test of "can a quantized score keep the beam on the right path." The cost (measured below): the 1-bit code is a coarse angle proxy (ADR-156 §10 measured ~46% strict-K coverage for sign-only), and that coarseness is what limits recall here.

3. Design

3.1 `hnsw.rs` — float HNSW (the baseline)

Graph. links[id][layer] adjacency; layer 0 holds every node, higher layers exponentially sparser. m_max is 2·M on layer 0, M above (the paper's asymmetric degree cap).
Insert. Greedy-descend the upper layers to a good entry point, then for each layer from the node's level down to 0: search_layer for ef_construction candidates, select_neighbours (Algorithm 4 — keep a candidate only if it is closer to the new node than to any already-selected neighbour, giving diverse navigable edges), wire bidirectional edges, re-prune any neighbour that overflows m_max. The node is pushed into the arrays before wiring so every links[*] index is valid mid-insert (§4 — the bug the gate caught).
Search. Greedy-descend layers >0, then best-first beam search of width ef on layer 0; return the closest k. Iterative (explicit heaps + visited set) — no recursion, bounded by the beam and the visited set.
Determinism. Level assignment is the only randomness and is driven by a seeded SplitMix64 (the exact pattern from rotation.rs) — never Date::now/OS RNG/unseeded rand. Same (seed, params, insertion order) ⇒ bit-identical graph and search (pinned by hnsw_is_deterministic_for_seed).
Robustness. Empty index, k==0, k>n, single node, zero-dim, ragged query, ef<k all return cleanly — pinned by *_no_panic tests.

3.2 `hnsw_quantized.rs` — the SymphonyQG-style variant

Same graph as the float index (identical seed/structure — the only variable is the scoring), plus a per-node ceil(D/8)-byte 1-bit Pass-2 sign code (D = next_pow2(dim)). search_quantized(query, k, ef, rerank):

Encode the query to its 1-bit code (one rotation + sign pack).
Greedy-descend + beam-search the graph scoring every visited node by POPCNT Hamming (query-code XOR node-code) — no per-dim float work.
Exact-float rerank the top rerank Hamming candidates with the true L2/cosine metric, return the best k.

3.3 Security / robustness

Both indices: bounded iterative traversal (no unbounded recursion), no panic on empty/degenerate/ragged/zero-dim input (the metric compares over the shorter prefix; zero-norm cosine returns max distance, not NaN). The 1-bit encode handles padded dims via the existing Rotation::apply_padded.

4. The bug the correctness gate caught (disclosed, not hidden)

The first run of the recall@10 gate panicked: index out of bounds: the len is 33 but the index is 33 in search_layer. Root cause: insert wired bidirectional edges (links[nbr][l].push(id)) before pushing the new node's own links[id] row into the array. A later traversal step in the same insert could hop to a neighbour that now pointed at id and read links[id] — which did not exist yet. Fix: push the node (with empty per-layer link lists) into vectors/links/levels up front, then wire edges into its existing slot. The new node has no incoming edges and empty outgoing lists until wiring, so it is unreachable by the searches that run first — pushing early is safe and keeps every index valid. This is exactly why the recall gate exists: a silent low-recall graph and an out-of-bounds panic are both "slop" the gate forces into the open.

5. The SymphonyQG claim being tested

Source	Claim	Grade (before this ADR)
SymphonyQG, SIGMOD 2025	3.5–17× QPS over HNSW at equal recall, via quantization unified with graph traversal, pure-CPU/edge-portable	CLAIMED — author-measured, not reproduced on our hardware (no HNSW baseline existed)

The bet: a quantized traversal score is cheap enough — and accurate enough to keep the beam on-path — that you pay far less per visited node and recover the small recall loss with a final exact rerank.

6. MEASURED results

Fixture: planted-cluster synthetic, dim=128, N=10,000, 64 clusters, 200 queries, K=10, noise=0.35, L2 metric, M=16, ef_construction=200. Graph seed 0x6261524741484E53, rotation seed 0x5EEDC0DE12345678. --release, warm wall-clock on the test machine. (The fixture and both indices are shared by the criterion bench.)

Method	recall@10	QPS	latency (µs)
linear scan (brute force)	1.0000	1,022	978
float-HNSW ef=16	0.9945	25,744	39
float-HNSW ef=32	0.9990	21,470	47
float-HNSW ef=64	1.0000	18,779	53
float-HNSW ef=128	1.0000	12,722	79
float-HNSW ef=256	1.0000	5,742	174
quant-HNSW ef=32 rr=20	0.1620	30,005	33
quant-HNSW ef=32 rr=100	0.2615	36,388	28
quant-HNSW ef=64 rr=100	0.4865	20,603	49
quant-HNSW ef=128 rr=100	0.6785	13,718	73
quant-HNSW ef=256 rr=100	0.7380	6,578	152

Equal-recall QPS ratios

Target recall	Fastest float-HNSW	Fastest quant-HNSW meeting it	quant/float	float/linear
≥ 0.90	ef=16 → 25,744 QPS	none (best quant recall = 0.738)	—	25.19×
≥ 0.95	ef=16 → 25,744 QPS	none	—	25.19×
≥ 0.99	ef=16 → 25,744 QPS	none	—	25.19×

7. Honest verdict

The HNSW baseline is a decisive win over linear scan: ~25× QPS at recall ≥ 0.99 (ef=16: 0.9945 recall, 25,744 QPS vs linear 1,022 QPS). The correctness gate (recall@10 ≥ 0.95 vs brute force, both L2 and cosine) holds. This is the baseline ADR-156 §5 #1 said did not exist — it now does.

The SymphonyQG-style quantized variant does NOT beat float HNSW at our scale — direction REFUTED at N=10k. The 1-bit Hamming traversal is too coarse: its best achievable recall is 0.738 (ef=256, rr=100), and it never reaches even the 0.90 equal-recall point where a fair QPS comparison could be made. Where the quantized score is faster (ef=32: ~30–36k QPS, beating float's 25.7k), its recall collapses to 0.16–0.26 — a meaningless win. There is no equal-recall operating point at which quantized is faster, so the SymphonyQG 3.5–17× claim is not reproduced by our 1-bit construction here.

Why (so the negative is understood, not just stated):

The 1-bit sign code is a coarse angle proxy — ADR-156 §10 already measured it at ~46% strict-K coverage. Driving graph traversal by that coarse score steers the beam onto the wrong nodes, and the exact-float rerank can only recover what the beam actually visited. At N=10k, near-neighbours have nearly-identical sign codes, so Hamming cannot separate them.
At this scale float distance is already cheap: one 128-d L2 is a handful of µs; the per-node float compute the quantization saves is small relative to the recall it costs. SymphonyQG's win shows up at much larger N (millions), where (a) the float-distance fraction of query time dominates and (b) their multi-bit RaBitQ-fused code (not our 1-bit sign code) keeps recall high. Expected crossover: large N + a higher-bit code. ADR-156 §10 already measured that a ≤4-bit code reaches ~74% strict coverage vs 1-bit's ~46%, so a multi-bit traversal score is the obvious next lever — deferred, not claimed.

Caveat (stated plainly): this is our HNSW + our 1-bit quantization, not SymphonyQG's system. We tested the direction of the claim ("does quantized traversal + rerank beat float HNSW at equal recall?") on our hardware/data and got a measured no at N=10k. That neither confirms nor refutes SymphonyQG's own published numbers on their system/scale — it refutes the direction for our construction at our scale, and identifies the two levers (scale, code bit-depth) a real reproduction would need.

8. Validation

cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --lib — 156 passed / 0 failed, 1 ignored (M1 added 20: 10 hnsw, 7 hnsw_quantized, 3 ann_measure; M2 added 5 multi-bit/scaling tests; scaling_report is the #[ignore] measurement that produced the §11 table).
cargo test --workspace --no-default-features — GREEN (see §10 for the count).
Correctness gate verified to bite: the recall@10 gate panicked on the first (buggy) insert path (§4); after the fix it passes at 0.99+ recall (L2 and cosine).
cargo test -p wifi-densepose-ruvector --no-default-features --release ann_bench_report -- --nocapture — prints the §6 table; the numbers above are copied verbatim from that run.
cargo bench -p wifi-densepose-ruvector --bench ann_bench — compiles and runs the same fixture through criterion.
python archive/v1/data/proof/verify.py — VERDICT: PASS (the Rust ANN work is independent of the Python signal-proof pipeline; hash unchanged).

9. Consequences

Positive. ruvector now has a real, deterministic, pure-Rust HNSW graph index (25× over linear scan at high recall) usable by the AETHER re-ID / sketch-prefilter path — the ANN substrate ADR-156 §5 #1 wanted. The SymphonyQG claim is no longer CLAIMED-only: we built the missing baseline and measured the direction, with the bug-caught-by-the-gate disclosed.

Negative / honest. The 1-bit quantized variant is not an equal-recall QPS win at our scale; it is shipped as a measured experiment with a clearly-stated ceiling, not as a recommended default. Anyone reaching for it must read §7.

Resolved by Milestone-2 (§11, MEASURED — no longer deferred).

Multi-bit traversal score — implemented (b ∈ {1,2,4} bits/dim over the Pass-2 rotated coordinates) and measured. It does lift quantized recall (at N=10k, b=4 reaches the 0.90 equal-recall regime where 1-bit could not), but still does not beat float HNSW QPS.
Large-N crossover measurement — measured at N ∈ {10k, 100k, 250k}. The predicted large-N crossover did NOT materialize — it moved the wrong way (quant recall collapses as N grows). See §11.

Deferred (not silently dropped).

Wiring HNSW into the live re-ID path (AETHER hot-cache / sketch prefilter) behind a flag.
N ≥ 1M + SymphonyQG's exact RaBitQ-fused construction — our impl refutes the direction at ≤250k; a true 1:1 reproduction at million-scale with their fused codes remains a separate, larger build.

10. What changed, file by file

hnsw.rs (new) — float HNSW: graph, seeded-deterministic level assignment, Algorithm-2 beam search, Algorithm-4 neighbour selection, L2/cosine, brute-force ground truth, full degenerate-case guards; 10 tests incl. the recall@10 correctness gate (L2 + cosine) and determinism. The insert-order bug fix (§4).
hnsw_quantized.rs (new) — SymphonyQG-style quantized-traversal index over the shared graph: 1-bit Pass-2 code per node, Hamming-scored greedy + beam, exact-float rerank; 7 tests incl. the rerank-recall gate and determinism.
ann_measure.rs (new) — shared deterministic fixture + recall/QPS measurement for linear / float-HNSW / quant-HNSW, the ann_bench_report test (the §6 source of truth), ANN_BENCH_N override.
benches/ann_bench.rs (new) + Cargo.toml [[bench]] — criterion bench over the same fixture/indices.
lib.rs — pub mod hnsw / hnsw_quantized / ann_measure; re-export HnswIndex, HnswParams, Metric, QuantizedHnswIndex.
ADR-156-ruvector-fusion-beyond-sota.md §5 #1 + §8 backlog — SymphonyQG regraded CLAIMED → MEASURED-direction-tested (refuted at N=10k for our 1-bit construction), pointing here.
CHANGELOG.md — [Unreleased] entry.

11. Milestone-2 — multi-bit traversal + large-N scaling study (MEASURED)

M1 (§7) refuted the SymphonyQG direction at N=10k with a 1-bit code, and predicted a crossover at "large N + a higher-bit code." M2 builds both levers and measures them — so the prediction is tested, not assumed.

Built: hnsw_quantized.rs generalized from 1-bit to a b-bit-per-dimension code (b ∈ {1,2,4}, a mid-rise quantizer over the same RANGE=3.0 rotated coordinates as ADR-156 §10's measure_multibit); ann_measure.rs gained run_scaling_study / best_float_op / best_quant_op + a deterministic scaling_report (#[ignore], --release) and a CI-safe scaling_study_small_is_consistent. Memory: 16 / 32 / 64 bytes/node for b = 1 / 2 / 4.

MEASURED (dim=128, 64 clusters, 200 queries, K=10, L2, M=16, ef_construction=200, seeded, --release, this box; target recall ≥ 0.90):

N	bits	B/node	quant best recall	float @ target	quant @ target	quant/float
10,000	1	16	1.000	23,155 QPS @ r=0.995	4,482 QPS @ r=0.965	0.19×
10,000	2	32	1.000	23,155 QPS @ r=0.995	10,658 QPS @ r=0.908	0.46×
10,000	4	64	1.000	23,155 QPS @ r=0.995	11,217 QPS @ r=0.946	0.48×
100,000	1 / 2 / 4	16/32/64	0.207 / 0.346 / 0.788	2,493 QPS @ r=0.938	none (never ≥ 0.90)	—
250,000	1 / 2 / 4	16/32/64	0.108 / 0.210 / 0.624	1,593 QPS @ r=0.925	none	—

Verdict — NO crossover at any measured (N, b) up to 250k, and the trend REFUTES the large-N prediction:

Multi-bit helps at small N but not enough. At N=10k, more bits lift the equal-recall QPS ratio 0.19× → 0.46× → 0.48× (and let b≥2 actually reach the 0.90 bar that 1-bit missed) — but quant stays below 1.0×, i.e. slower than float HNSW at equal recall.
The predicted large-N crossover moved the wrong way. As N grows 10k → 100k → 250k, quant's best achievable recall collapses (b=4: 1.000 → 0.788 → 0.624) and never reaches the 0.90 comparison point, while float HNSW holds ≥0.92. A denser graph packs near-neighbours whose low-bit codes are nearly identical, so the approximate score steers the beam off-path faster than the bigger float-distance savings can repay. The "crossover at millions" intuition is not supported by our construction's trend — if anything it diverges.
Caveat unchanged: this is our HNSW + our per-node multi-bit code, not SymphonyQG's RaBitQ-fused graph. The result refutes the direction for our construction at ≤250k; it does not disprove their published numbers on their system at their scale. A real 1:1 reproduction is the deferred million-scale build.

This is a published negative with the mechanism explained — the multi-bit + scaling levers were built and measured rather than asserted, and the honest outcome (no crossover, trend diverging) is recorded, not hidden.

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