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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 Quantum Computing & Solver Crates Research

Crates Covered

  1. ruqu-core - Quantum circuit simulator (5 backends, SIMD, QEC)
  2. ruqu-algorithms - VQE, Grover, QAOA, Surface Code
  3. ruqu-exotic - Quantum-classical hybrid: reasoning QEC, swarm interference
  4. ruqu-wasm - Browser quantum simulation (25 qubits, 180KB)
  5. ruQu - Coherence gating platform (256-tile fabric, PERMIT/DEFER/DENY)
  6. ruvector-solver - 7 sublinear sparse algorithms with auto-routing
  7. ruvector-solver-node / ruvector-solver-wasm - Bindings
  8. ruvector-mincut - Dynamic minimum cut (arXiv:2512.13105, O(n^0.12))
  9. ruvector-mincut-node / ruvector-mincut-wasm - Bindings
  10. ruvector-mincut-gated-transformer - Lambda-controlled transformer inference
  11. ruvector-mincut-gated-transformer-wasm - Browser coherence-gated inference

Key Findings

ruqu-core

  • 5 backends: StateVector (exact, 32 qubits), Stabilizer (millions of qubits), Clifford+T, TensorNetwork (MPS), Hardware profiles
  • Cost-model planner auto-routes circuits to optimal backend
  • Hybrid decomposition: partition by entanglement structure for multi-backend execution
  • Subpolynomial QEC decoders: O(d^{2-epsilon} * polylog(d))

ruqu-exotic (Most Novel for AQE)

  • Reasoning QEC: Quantum error correction applied to AI reasoning chains
  • Swarm Interference: Quantum-inspired multi-agent consensus via constructive/destructive interference
  • Interference Search: Amplitude-based vector similarity (fundamentally different from cosine)
  • Reversible Memory: Quantum uncomputation for perfect undo/redo
  • Memory Decay: T1/T2 decay modeling for temporal confidence in cached results

ruvector-solver

  • 7 algorithms: Neumann, CG, Forward Push PPR, Backward Push, Hybrid Random Walk, TRUE, BMSSP Multigrid
  • SolverRouter auto-selects based on matrix sparsity profile
  • AVX2 SIMD SpMV with fused residual+norm (3x less memory traffic)
  • Arena allocator for zero per-iteration heap allocation

ruvector-mincut (Dec 2025 Breakthrough)

  • First production implementation of arXiv:2512.13105
  • O(n^0.12) amortized update time (verified)
  • 448+ tests passing
  • Advanced: Link-Cut Trees, Euler Tour Trees, graph sparsification
  • 256-core agentic chip support (6.7KB/core)
  • SNN module: temporal attractors, strange loops, causal discovery

ruvector-mincut-gated-transformer

  • Lambda (min-cut) as first-class transformer control signal
  • 4-tier system: Normal (1x) -> Reduced (2-3x) -> Safe (5-10x) -> Skip (50-200x)
  • Spike-driven scheduling: 87x energy efficiency
  • EAGLE-3 speculative decoding: 3-5x speedup
  • FlashAttention tiling: O(n) memory
  • KV cache INT4 (RotateKV): 8-16x compression
  • Every inference produces explainable witness

AQE Integration Opportunities

  1. Reasoning QEC: Validate and correct AI agent reasoning chains
  2. Swarm interference: Better multi-agent consensus mechanisms
  3. MinCut monitoring: Real-time agent network health via O(n^0.12) updates
  4. PageRank prioritization: Rank test cases/agents by graph importance
  5. Lambda-gated execution: 4-tier compute allocation based on system health
  6. Memory decay modeling: Confidence decay for cached test results over time
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