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+# RuVector Research Summary
+
+> Research conducted 2026-02-24 for makima improvement evaluation
+
+## Overview
+
+RuVector is a high-performance vector database with graph intelligence capabilities, self-learning DAG execution, and distributed consensus. It combines vector search, graph neural networks, and reinforcement learning into a unified platform.
+
+**Repository**: https://github.com/ruvnet/ruvector
+
+## Architecture
+
+### Core Components
+- **HNSW Indexing**: Hierarchical Navigable Small World for approximate nearest neighbor search
+- **GNN Layers**: Graph Neural Networks for query reranking and path optimization
+- **ReasoningBank**: Trajectory learning with verdict judgment
+- **SONA**: Self-Optimizing Neural Architecture for runtime adaptation
+- **Raft Consensus**: Distributed coordination and fault tolerance
+
+### Key Performance
+- 61µs p50 latency for vector queries
+- 2-32x memory reduction via adaptive compression
+- SIMD acceleration (AVX2/AVX-512/NEON)
+- 10-50x burst scaling for traffic spikes
+
+## Graph Database Capabilities
+
+### Cypher Query Support
+- Neo4j-compatible Cypher syntax: `MATCH (a)-[:SIMILAR]->(b)`
+- Hyperedge support connecting 3+ nodes simultaneously
+- Combines relationship-based search with vector similarity
+- Enables "semantic + structured search"
+
+### Graph Intelligence
+- PageRank computation
+- Spectral clustering
+- Community detection
+- Multi-head attention for neighbor importance weighting
+
+## Self-Learning DAG Execution
+
+This is the most relevant capability for makima:
+
+### Architecture
+- **Automatic query optimization** through continuous learning
+- **7 attention mechanisms** dynamically select optimal execution strategies
+- **50-80% latency reduction** over time as patterns are learned
+- **MinCut control** triggers automatic strategy switching above tension thresholds
+
+### Learning Cycle
+1. **Topological analysis** of DAG structure
+2. **Causal cone evaluation** for dependency impact
+3. **Critical path identification** and optimization
+4. **Trajectory learning** via ReasoningBank
+5. **Adaptive routing** based on learned patterns
+
+## Self-Learning Mechanisms
+
+### SONA (Self-Optimizing Neural Architecture)
+- Two-tier LoRA + EWC++ + ReasoningBank
+- <1ms adaptive learning per request
+- 55% quality improvement over baseline
+- Per-request micro-LoRA adaptation (rank 1-2)
+
+### GNN-Based Optimization
+- Multi-head attention weights neighbor importance
+- Updates representations based on graph structure
+- Reinforces frequently-accessed paths
+- "The more you search, the better results get"
+
+### Q-Learning Integration
+- Neural patterns learn optimal routing
+- HNSW memory integration for fast retrieval
+- Dynamic index topology optimization
+
+## Memory Management
+
+### Efficiency Mechanisms
+- **Adaptive tiered compression**: 2-32x memory reduction
+- **SIMD-optimized SpMV**: Sparse matrix-vector multiplication
+- **Arena allocators**: Bounds-check elimination
+- **COW branching**: Cluster-level copy-on-write (1M vectors, ~2.5MB per edit)
+
+### RVF Cognitive Container
+- Git-like branching of vector datasets
+- DNA-style lineage tracking parent/child derivation
+- Cryptographic verification of data integrity
+- Progressive indexing through layered construction
+
+## Fault Tolerance
+
+- **Raft consensus**: Leader election and log replication
+- **Multi-master replication**: Vector clock conflict resolution
+- **Geo-distributed sync**: Cross-region high availability
+- **Snapshot/backup**: Point-in-time recovery
+- **Post-quantum signatures**: ML-DSA-65 and Ed25519 on every segment
+
+## Advanced Features
+
+### 46 Attention Mechanisms
+Categories: standard (dot-product, multi-head, Flash), graph-specific (RoPE, edge-featured, dual-space), efficiency (sparse, cross-attention, neighborhood), specialized (mincut-gated transformer with 50% compute reduction).
+
+### Adaptive Routing
+- "Tiny Dancer" FastGRNN neural inference
+- 90% semantic routing accuracy
+- Hybrid keyword-first + embedding fallback strategy
+
+### Additional Capabilities
+- Local LLM execution (ruvllm with GGUF models)
+- Topological data analysis via persistent homology
+- Post-quantum cryptography
+- eBPF kernel acceleration
+- 5.5KB WASM runtime for browser deployment