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Dragan Spiridonov 6b249c034f feat: RuVector integration phases 1-4 — native HNSW, neural routing, safety gates, differentiation 
Implements 23 tasks across 4 phases per docs/implementation/ruvector-integration-plan.md:

Phase 1 — Foundation (make what we have faster):
- Native HNSW backend via @ruvector/router (150x search speedup, ADR-081)
- Metadata filtering layer with composable AND/OR/NOT/FIELD expressions
- Temporal tensor compression (Int8Array quantization, 4x memory reduction)
- Deterministic golden-ratio dithering for cross-platform reproducibility
- 15 feature flags (all default false) with CLI profiles

Phase 2 — Intelligence (make agents smarter):
- Neural TinyDancer router with REINFORCE policy gradient (ADR-082)
- SONA three-loop enhancement (MicroLoRA 0.18μs/adapt via WASM)
- Cross-domain transfer learning with Thompson Sampling (ADR-084)
- Regret tracking with log-log regression growth classification

Phase 3 — Safety (make agents trustworthy):
- Sheaf-gated test validation via prime-radiant CohomologyEngine
- Coherence-gated agent actions (3-filter pipeline, ADR-083)
- Witness chain audit trail with SHA-256 hash linking
- HNSW health monitoring with spectral analysis

Phase 4 — Differentiation (make AQE unique):
- Cognitive container export/import (RVF v2)
- DAG attention scheduler for dependency-aware test ordering
- CNN visual regression with spatial pooling embeddings
- Behavior tree orchestration (Sequence/Selector/Parallel)
- Reasoning QEC with majority-vote consensus
- Browser QE dashboard scaffolding

Also: remove obsolete v2-to-v3-migration test (source already removed),
add ADR-081 through ADR-085, research docs, benchmarks, and integration
test suites for all 4 phases.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-16 17:37:43 +00:00

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RuVector Router, SONA & Utilities Research

Crates Covered

  1. ruvector-router-core - Vector DB with neural routing strategies
  2. ruvector-router-cli / ruvector-router-ffi / ruvector-router-wasm - Bindings
  3. ruvector-tiny-dancer-core - FastGRNN routing for 70-85% LLM cost reduction
  4. ruvector-tiny-dancer-node / ruvector-tiny-dancer-wasm - Bindings
  5. sona - Self-Optimizing Neural Architecture (MicroLoRA + EWC++ + ReasoningBank)
  6. prime-radiant - Sheaf Laplacian coherence gate for AI safety
  7. ruvector-collections - Multi-tenant collection management
  8. ruvector-filter - Advanced metadata filtering (rich expression language)
  9. ruvector-math - 10 advanced math modules (OT, Info Geo, TDA, Manifolds)
  10. ruvector-math-wasm - Browser-side advanced math
  11. ruvector-metrics - Prometheus-compatible monitoring

Key Findings

ruvector-tiny-dancer-core

  • FastGRNN: <1MB models, 80-90% sparsity, 7.5us inference per candidate
  • Multi-signal scoring: semantic similarity, recency, frequency, success rate
  • Circuit breaker for graceful degradation
  • Conformal prediction for uncertainty quantification
  • 10 candidates routed in 8.83us, 100 in 92.86us

sona (Self-Optimizing Neural Architecture)

  • MicroLoRA: Rank 1-2, <100us per-request adaptation with AVX2 SIMD
  • BaseLoRA: Rank 4-16, hourly background adaptation
  • EWC++: Automatic task boundary detection via gradient z-score, circular buffer memory
  • ReasoningBank: K-means++ clustering for pattern discovery, similarity search
  • Three loops: Instant (per-request), Background (hourly), Coordination (cross-agent)
  • Federated learning: Multi-agent aggregation support
  • Golden-ratio initialization for reproducibility

prime-radiant

  • Sheaf Laplacian: E(S) = sum(w_e * ||rho_u(x_u) - rho_v(x_v)||^2)
  • Compute ladder: Reflex (<1ms) -> Retrieval (~10ms) -> Heavy (~100ms) -> Human (async)
  • Blake3 hash-chained witness records
  • GPU acceleration (wgpu WGSL compute shaders)
  • RuvLLM integration: hallucination detection via coherence energy
  • SONA threshold tuning integration

ruvector-math (10 Modules)

  1. Optimal Transport: Sliced Wasserstein (1000x faster than exact), Sinkhorn, Gromov-Wasserstein
  2. Information Geometry: Fisher Info, Natural Gradient (3-5x faster than Adam), K-FAC
  3. Product Manifolds: E^n x H^n x S^n with geodesic distance, Frechet mean
  4. Spherical Geometry: S^n operations for cyclical patterns
  5. Tropical Algebra: Max-plus for piecewise linear / neural region counting
  6. Tensor Networks: TT/Tucker/CP decomposition (~99% memory compression)
  7. Spectral Methods: Chebyshev graph diffusion, spectral clustering
  8. Persistent Homology (TDA): Vietoris-Rips, persistence diagrams, topological drift
  9. Polynomial Optimization: SOS certificates for provable bounds

ruvector-filter

  • Rich expression: AND/OR/NOT, BETWEEN, IN, CONTAINS, REGEX, nested JSON access
  • Filter optimization (selectivity-based reordering)
  • Essential for "find similar failures WHERE env=prod AND severity>3"

AQE Integration Opportunities

  1. SONA: Replace JS approximations with production Rust via NAPI (already in AQE architecture)
  2. Tiny Dancer: Neural 3-tier model routing with 70-85% cost reduction
  3. Prime Radiant: Hallucination detection for AI-generated test artifacts
  4. Filtered search: Hybrid vector+metadata search for practical QE queries
  5. Wasserstein drift detection: Monitor QE metric drift over time
  6. TDA: Detect topological changes in defect clusters
  7. Prometheus metrics: Standard observability for AQE production deployments
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