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* docs(bet1): pre-register reuse-under-drift gate on real GNN trajectory Productionize BET 1 (ADR-200 WIN under synthetic drift) by wiring re-weight + periodic-rebuild into the ruvector-diskann loop behind a feature flag, validated on a REAL contrastive-link-prediction embedding trajectory on ogbn-arxiv (ADR-200 next-step #4). Gate frozen before any contender run (prove-not-hype): WIN = ReweightOnly within 2% recall@10 of AlwaysRebuild + Periodic{k} within 1% at <=50% cumulative rebuild cost; KILL = no transfer from synthetic to real drift. Minimum-drift precondition (>=15% top-10 churn) guards against a vacuous pass. Self-contained off main; independent of PR #535. Outcome -> ADR-202. Linked: ruvnet/RuVector#534 * feat(diskann): M0 — reuse-under-drift policy module behind feature flag DriftingIndex wraps a VamanaGraph and owns only the rebuild decision (RebuildPolicy: AlwaysRebuild / ReweightOnly / Periodic{k}); the consumer owns the drifting vectors and passes snapshots to on_metric_update + search. Native reuse hook: greedy_search takes vectors externally, so adapt-to-drift recomputes only distances. Feature-gated (reuse-under-drift, default off) — default build byte-identical. 5 unit tests green (cadence + search). Refs ruvnet/RuVector#534 * feat(bet1): M1-M3 real-trajectory validation harness examples/diskann_real_trajectory.rs: generates a REAL learned-GNN metric trajectory via contrastive link-prediction (InfoNCE over ogbn-arxiv citations, ruvector-gnn Optimizer + info_nce_loss, embeddings on the unit sphere so cosine==dot and L2 ranking agrees), then drives the diskann reuse policy (DriftingIndex) through all four contenders step-by-step. Result (n=20k, gradual trajectory to 67% churn): - WIN. Reuse holds within 2% recall@10 of full rebuild up to 40% top-10 churn (>= ADR-200's synthetic ~36% regime) -- transfer confirmed on real learned drift. Stale control collapses 92%->33% (teeth). - Periodic recovers the high-churn tail: P k=4 = 98.7% (gap -0.01%) at 24% of rebuild cost, evals 1.00x B. ADR-200 hybrid reproduced on real drift. - Honest caveat: pure reuse past the ceiling decays (-4.73% over the whole overdriven trajectory, 1.05x evals); the shippable periodic policy does not. Refs ruvnet/RuVector#534 * style(bet1): rustfmt the reuse module + trajectory harness * docs(adr): ADR-202 — reuse-under-drift WIN on a real learned-GNN trajectory Outcome ADR for BET 1 productionization (closes ADR-200 next-step #4). Fixed-topology reuse + periodic rebuild, validated on a real contrastive- link-prediction trajectory over ogbn-arxiv (not synthetic A(t)). WIN at n=20k AND n=50k: pure reuse holds within 2% recall@10 of full rebuild up to a 40% top-10 churn ceiling (identical at both scales, >= ADR-200's synthetic ~36%); Periodic{k:4} recovers the high-churn tail to within 0.01% (20k) / above rebuild (50k) at 20-24% of rebuild cost, equal per-query work. Stale control collapses (teeth). Honest caveat: pure reuse past the ceiling decays -- the shippable policy is periodic, not never. Refs ruvnet/RuVector#534 * docs(bet1): record WIN outcome pointer to ADR-202 in pre-registration * docs(bet1): pre-register sampled-recall trigger gate + force_rebuild plumbing Pre-register (frozen before any run) the ADR-200 next-step #2 bet: does a sampled-recall rebuild trigger beat fixed Periodic{k} under VARIABLE-RATE drift, and beat the Frobenius monitor ADR-200 found wanting? Honest test = the (rebuilds, recall) Pareto frontier; WIN = trigger >=25% fewer rebuilds at matched recall with probe cost counted; KILL = no frontier dominance. Plumbing (allowed pre-freeze): DriftingIndex::force_rebuild + harness. Refs ruvnet/RuVector#534 * fix(bet1): trigger harness — Adam + enforced churn precondition (first run was VOID) The first variable-rate run was VOID (0% churn): plain SGD at lr 0.002-0.03 on unit-normalized embeddings doesn't move them. Switched to Adam (real motion in bursts), n=20k for edge density, and ENFORCED the >=15% churn precondition (abort before rendering a verdict) so a no-drift trajectory can't masquerade as a result. Gate criteria unchanged. Result (n=20k, bursty trajectory, per-step Δchurn ~45 burst / ~2 calm, 89% end churn): WIN. Recall{floor=0.95} = 97.2% @ 7 rebuilds beats Periodic{k=2} (96.8% @ 12) on BOTH axes; probe cost ~1s vs ~73s rebuild time saved (trap passed); beats best Frobenius (97.3% @ 9) on rebuilds. Refs ruvnet/RuVector#534 * feat(bet1): productionize RecallTrigger (WIN) + ADR-202 addendum The sampled-recall trigger WON (ADR-200 next-step #2): under bursty drift it uses ~42% fewer rebuilds than fixed Periodic{k} at matched recall, beats the Frobenius monitor ADR-200 found wanting, and passes the probe-cost trap (~1s probe vs ~73s rebuild saved). Productionized as RecallTrigger in ruvector_diskann::reuse (DriftingIndex in ReweightOnly mode + a probe-driven force_rebuild); its knob 'floor' IS the recall SLA, unlike k/tau. 8 reuse tests (incl. holds-under-no-drift + fires-then-recovers). ADR-202 addendum records the result; pre-registration carries the WIN outcome pointer. Refs ruvnet/RuVector#534 * docs(bet1): pre-register objective-dependence check + nodeclass trajectory Frozen-before-run generality check of ADR-202's 40% holding ceiling: does it generalize beyond contrastive link-prediction to a DIFFERENT learned objective? Adds a node-classification trajectory (real arxiv 40-class labels, CE on a linear head, embeddings as params) selectable via an 'objective=nodeclass' arg to the existing harness — same contenders + 2% gate, only the objective changes. CONFIRM = holding ceiling >=30% churn + periodic recovers; CAVEAT = <20% or materially different (reportable). Refs ruvnet/RuVector#534 * docs(bet1): objective-dependence CONFIRMED + class-collapse degeneracy caveat Node-classification trajectory (2nd objective) holds reuse within 2% of rebuild up to a 54% churn ceiling (>= link-pred's 40%) -> the ADR-202 holding-ceiling result GENERALIZES across two learned objectives; the objective-dependence caveat is resolved. Honest finding (reported, not buried): past ~60% churn node-class CE collapses embeddings into ~40 class blobs where recall@10 is ill-posed (intra-blob near-ties) and the FULL-REBUILD baseline itself destabilizes (B swings 55-96%). The trajectory-wide 'reuse > rebuild +4.3%' is a benchmark-degeneracy artifact (ADR-200's t=0.25 dip amplified), NOT a genuine superiority claim. Operational conclusion unaffected (reuse+periodic never worse). ADR-202 addendum + next-step #5 (collapse-aware metric). Refs ruvnet/RuVector#534 |
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