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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 Robotics Crates Research

Crates Covered

  1. ruvector-robotics - Cognitive robotics (behavior trees, 3-tier memory, A*, swarm)
  2. agentic-robotics-core - ROS2-alternative middleware (540ns pub/sub, Zenoh)
  3. agentic-robotics-embedded - no_std embedded support (RTIC, Embassy)
  4. agentic-robotics-mcp - MCP server for AI-controlled robotics
  5. agentic-robotics-node - NAPI-RS Node.js bindings
  6. agentic-robotics-rt - Dual-runtime priority executor with HDR latency tracking
  7. agentic-robotics-benchmarks - Criterion benchmarks

Key Findings

ruvector-robotics

  • Cognitive Core: Perceive-think-act-learn loop with dual-process theory (Reactive/Deliberative/Emergency)
  • Behavior Trees: Sequence, Selector, Parallel with threshold, Decorators (Inverter/Repeat/UntilFail/Timeout)
  • Three-tier Memory: Working (importance-based eviction), Episodic (similarity recall), Semantic (embedding storage)
  • Decision Engine: Multi-criteria utility = reward - riskaversion - energyweight + curiosity*novelty
  • Skill Learning: Learning-from-demonstration via trajectory averaging
  • Swarm Intelligence: Formation computation, capability-based task assignment, majority consensus
  • Planning: A* grid search with octile heuristic, potential field planner
  • 15 MCP tools across 6 categories (Perception, Navigation, Cognition, Swarm, Memory, Planning)

agentic-robotics-core

  • 540ns serialization, 30ns channel messaging, 1.8M msg/sec
  • 4-byte wire overhead (vs ROS2's 24-byte)
  • Zenoh middleware for peer-to-peer discovery
  • CDR (ROS2-compatible) + rkyv (zero-copy) + JSON serialization

agentic-robotics-rt

  • Dual Tokio runtime: high-priority (2 threads) + low-priority (4 threads)
  • Deadline-based routing: <1ms = high priority runtime
  • HDR histogram latency tracking (p50/p90/p99/p99.9)

AQE Integration Opportunities

  1. Behavior trees for agent orchestration: Sequence/Selector/Parallel maps directly to test strategies
  2. Cognitive perceive-think-act-learn loop: Adaptive QE with attention threshold adjustment
  3. Three-tier memory: Working (current test), Episodic (history replay), Semantic (patterns)
  4. Multi-criteria decision engine: Intelligent test prioritization (reward, risk, energy, curiosity)
  5. Priority-based execution: Critical tests on high-priority runtime, exploratory on low
  6. MCP server pattern: Template for exposing AQE capabilities to AI assistants
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