mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
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Dragan Spiridonov
6b249c034f
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>
42 lines
2.4 KiB
Markdown
42 lines
2.4 KiB
Markdown
# RuVector Robotics Crates Research
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## Crates Covered
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1. ruvector-robotics - Cognitive robotics (behavior trees, 3-tier memory, A*, swarm)
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2. agentic-robotics-core - ROS2-alternative middleware (540ns pub/sub, Zenoh)
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3. agentic-robotics-embedded - no_std embedded support (RTIC, Embassy)
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4. agentic-robotics-mcp - MCP server for AI-controlled robotics
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5. agentic-robotics-node - NAPI-RS Node.js bindings
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6. agentic-robotics-rt - Dual-runtime priority executor with HDR latency tracking
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7. agentic-robotics-benchmarks - Criterion benchmarks
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## Key Findings
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### ruvector-robotics
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- **Cognitive Core**: Perceive-think-act-learn loop with dual-process theory (Reactive/Deliberative/Emergency)
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- **Behavior Trees**: Sequence, Selector, Parallel with threshold, Decorators (Inverter/Repeat/UntilFail/Timeout)
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- **Three-tier Memory**: Working (importance-based eviction), Episodic (similarity recall), Semantic (embedding storage)
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- **Decision Engine**: Multi-criteria utility = reward - risk*aversion - energy*weight + curiosity*novelty
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- **Skill Learning**: Learning-from-demonstration via trajectory averaging
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- **Swarm Intelligence**: Formation computation, capability-based task assignment, majority consensus
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- **Planning**: A* grid search with octile heuristic, potential field planner
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- **15 MCP tools** across 6 categories (Perception, Navigation, Cognition, Swarm, Memory, Planning)
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### agentic-robotics-core
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- 540ns serialization, 30ns channel messaging, 1.8M msg/sec
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- 4-byte wire overhead (vs ROS2's 24-byte)
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- Zenoh middleware for peer-to-peer discovery
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- CDR (ROS2-compatible) + rkyv (zero-copy) + JSON serialization
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### agentic-robotics-rt
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- Dual Tokio runtime: high-priority (2 threads) + low-priority (4 threads)
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- Deadline-based routing: <1ms = high priority runtime
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- HDR histogram latency tracking (p50/p90/p99/p99.9)
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## AQE Integration Opportunities
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1. **Behavior trees for agent orchestration**: Sequence/Selector/Parallel maps directly to test strategies
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2. **Cognitive perceive-think-act-learn loop**: Adaptive QE with attention threshold adjustment
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3. **Three-tier memory**: Working (current test), Episodic (history replay), Semantic (patterns)
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4. **Multi-criteria decision engine**: Intelligent test prioritization (reward, risk, energy, curiosity)
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5. **Priority-based execution**: Critical tests on high-priority runtime, exploratory on low
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6. **MCP server pattern**: Template for exposing AQE capabilities to AI assistants
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