Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/docs/research/latent-space/hnsw-ruvector-integration.md

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+# RuVector Integration Roadmap: HNSW Evolution
+
+## Practical Implementation Strategy for RuVector
+
+### Executive Summary
+
+This document provides a concrete, actionable roadmap for integrating the 20-year HNSW evolution vision into RuVector. Unlike the era-specific research documents, this focuses on **practical implementation** priorities, resource requirements, risk mitigation, and incremental deployment strategies.
+
+**Goal**: Transform RuVector from a high-performance classical HNSW implementation into a research platform and production-ready system incorporating neural augmentation (2025-2030), self-organization (2030-2035), cognition (2035-2040), and post-classical computing (2040-2045).
+
+**Current State** (2025):
+- **Codebase**: `/home/user/ruvector/crates/ruvector-core/src/index/hnsw.rs` (hnsw_rs wrapper)
+- **Capabilities**: Static graph, tombstone deletion, batch insertion, serialization
+- **GNN Infrastructure**: `/home/user/ruvector/crates/ruvector-gnn/` (RuvectorLayer, differentiable search, EWC, replay buffer)
+- **Performance**: ~150x faster than linear search, 0.92-0.95 recall@10
+
+---
+
+## 1. Current Capability Mapping
+
+### 1.1 Existing Strengths
+
+**Core HNSW Implementation** (`/crates/ruvector-core/src/index/hnsw.rs`):
+```rust
+✓ VectorIndex trait implementation
+✓ HnswConfig with (m, ef_construction, ef_search, max_elements)
+✓ Batch insertion with rayon parallelization
+✓ Serialization/deserialization (bincode)
+✓ Multiple distance metrics (Cosine, Euclidean, DotProduct, Manhattan)
+✓ Search with custom ef_search parameter
+```
+
+**GNN Components** (`/crates/ruvector-gnn/`):
+```rust
+✓ RuvectorLayer (message passing + attention + GRU)
+✓ MultiHeadAttention
+✓ Differentiable search (soft attention over candidates)
+✓ Hierarchical forward pass through layers
+✓ TensorCompress (None, Half, PQ8, PQ4, Binary)
+✓ InfoNCE and local contrastive losses
+✓ Adam optimizer with momentum
+✓ ElasticWeightConsolidation (EWC) for continual learning
+✓ ReplayBuffer with reservoir sampling
+✓ LearningRateScheduler (multiple strategies)
+```
+
+**Advanced Features** (`/crates/ruvector-core/src/advanced/`):
+```rust
+✓ LearnedIndex trait
+✓ RecursiveModelIndex (RMI)
+✓ HybridIndex (learned + dynamic)
+```
+
+### 1.2 Critical Gaps
+
+| Feature | Current Status | Era 1 Target | Gap |
+|---------|---------------|--------------|-----|
+| Edge Selection | Fixed M | Learned per-node | **High Priority** |
+| Navigation | Greedy | RL-based policy | **High Priority** |
+| Embedding-Graph Co-optimization | Decoupled | End-to-end | **Medium Priority** |
+| Layer Routing | Random | Attention-based | **Medium Priority** |
+| True Deletion | Tombstones only | Self-healing | **Low Priority (Era 2)** |
+| Multi-Modal | Single modality | Unified index | **Low Priority (Era 2)** |
+
+---
+
+## 2. Phase-by-Phase Implementation Plan
+
+### Phase 1: Neural Augmentation Foundations (Months 1-12)
+
+**Objectives**:
+1. GNN-guided edge selection
+2. Learned navigation with RL
+3. Benchmark on public datasets
+
+**Milestones**:
+
+#### Month 1-2: Infrastructure Setup
+```rust
+// New files to create:
+/crates/ruvector-core/src/index/adaptive_hnsw.rs
+/crates/ruvector-core/src/index/learned_nav.rs
+/crates/ruvector-gnn/src/rl/ppo.rs
+/crates/ruvector-gnn/src/rl/maml.rs
+```
+
+**Deliverables**:
+- [ ] Create `adaptive_hnsw.rs` skeleton
+- [ ] Extend `RuvectorLayer` for edge scoring
+- [ ] Setup RL environment wrapper
+- [ ] Benchmark harness for ANN-Benchmarks.com
+
+#### Month 3-6: GNN Edge Selection
+
+**Implementation**:
+```rust
+// /crates/ruvector-core/src/index/adaptive_hnsw.rs
+
+pub struct AdaptiveEdgeSelector {
+    context_encoder: Vec<RuvectorLayer>,  // Uses existing RuvectorLayer!
+    edge_attention: MultiHeadAttention,   // Uses existing MultiHeadAttention!
+    threshold_network: Sequential,
+    optimizer: Adam,                      // Uses existing Adam!
+}
+
+impl AdaptiveEdgeSelector {
+    pub fn new(hidden_dim: usize, num_layers: usize) -> Self {
+        let context_encoder = (0..num_layers)
+            .map(|_| RuvectorLayer::new(hidden_dim, hidden_dim, 4, 0.1))
+            .collect();
+
+        let edge_attention = MultiHeadAttention::new(hidden_dim, 4);
+
+        let threshold_network = Sequential::new(vec![
+            Box::new(Linear::new(hidden_dim + 4, hidden_dim / 2)),  // +4 for graph stats
+            Box::new(ReLU),
+            Box::new(Linear::new(hidden_dim / 2, 1)),
+            Box::new(Sigmoid),
+        ]);
+
+        let optimizer = Adam::new(0.001, 0.9, 0.999, 1e-8);
+
+        Self {
+            context_encoder,
+            edge_attention,
+            threshold_network,
+            optimizer,
+        }
+    }
+}
+```
+
+**Training Loop**:
+```rust
+// Reuse existing training infrastructure
+impl AdaptiveEdgeSelector {
+    pub fn train_epoch(
+        &mut self,
+        embeddings: &[Vec<f32>],
+        val_queries: &[Query],
+    ) -> f32 {
+        // Build graph with current edge selector
+        let graph = self.build_graph_with_selection(embeddings);
+
+        // Evaluate on validation queries
+        let recall = self.evaluate_recall(&graph, val_queries);
+
+        // Compute loss (negative recall + graph regularization)
+        let loss = -recall + 0.01 * graph.spectral_gap();
+
+        // Backprop (uses existing optimizer)
+        loss.backward();
+        self.optimizer.step();
+
+        loss.item()
+    }
+}
+```
+
+**Deliverables**:
+- [ ] `AdaptiveEdgeSelector` implementation
+- [ ] Training script with SIFT1M/GIST1M
+- [ ] Ablation study (fixed M vs. learned threshold)
+- [ ] Performance report (recall, latency, memory)
+
+**Success Criteria**:
+- Recall@10 improvement: +2-4% over baseline
+- Graph sparsity: 10-20% fewer edges
+- Training time: <6 hours on single GPU
+
+#### Month 7-12: RL Navigation
+
+**PPO Implementation**:
+```rust
+// /crates/ruvector-gnn/src/rl/ppo.rs
+
+pub struct PPONavigator {
+    policy: NavigationPolicy,
+    value_network: ValueNetwork,
+    optimizer: Adam,  // Reuse existing!
+    rollout_buffer: RolloutBuffer,
+}
+
+pub struct NavigationPolicy {
+    state_encoder: Sequential,
+    lstm: LSTM,
+    action_head: Linear,
+}
+
+impl PPONavigator {
+    pub fn train_episode(&mut self, graph: &HnswGraph, queries: &[Query]) {
+        // Collect rollouts
+        for query in queries {
+            let trajectory = self.collect_trajectory(graph, query);
+            self.rollout_buffer.add(trajectory);
+        }
+
+        // Compute GAE advantages
+        let advantages = self.compute_gae_advantages();
+
+        // PPO update (multiple epochs)
+        for _ in 0..4 {
+            for batch in self.rollout_buffer.iter_batches(64) {
+                let loss = self.compute_ppo_loss(batch, &advantages);
+                loss.backward();
+                self.optimizer.step();
+            }
+        }
+    }
+}
+```
+
+**Deliverables**:
+- [ ] PPO trainer implementation
+- [ ] MDP environment for HNSW navigation
+- [ ] Reward shaping experiments
+- [ ] Comparison to greedy search
+- [ ] MAML meta-learning prototype
+
+**Success Criteria**:
+- Path length reduction: 20-30% fewer hops
+- Distance computations: 15-25% reduction
+- Generalization: Works on unseen datasets with 5-shot fine-tuning
+
+---
+
+### Phase 2: End-to-End Optimization (Months 13-24)
+
+**Objectives**:
+1. Joint embedding-graph training
+2. Differentiable HNSW construction
+3. Attention-based layer routing
+
+**Implementation Priority**: Medium (builds on Phase 1)
+
+#### Month 13-18: Differentiable Graph Construction
+
+**Key Challenge**: Make discrete edge decisions differentiable
+
+**Solution**: Gumbel-Softmax
+```rust
+// /crates/ruvector-core/src/index/differentiable_hnsw.rs
+
+pub struct DifferentiableHNSW {
+    edge_probability_network: Sequential,
+    layer_assignment_network: Sequential,
+    temperature: f32,  // Annealing schedule
+}
+
+impl DifferentiableHNSW {
+    pub fn build_soft_graph(&self, embeddings: &Tensor) -> SoftGraph {
+        // Predict edge probabilities
+        let edge_logits = self.predict_edge_logits(embeddings);
+
+        // Gumbel-Softmax sampling
+        let gumbel_noise = sample_gumbel(edge_logits.shape());
+        let soft_edges = ((edge_logits + gumbel_noise) / self.temperature).sigmoid();
+
+        SoftGraph {
+            embeddings: embeddings.clone(),
+            edge_weights: soft_edges,
+        }
+    }
+}
+```
+
+**Deliverables**:
+- [ ] Gumbel-Softmax implementation
+- [ ] Soft graph construction
+- [ ] Differentiable search (reuse `/crates/ruvector-gnn/src/search.rs`)
+- [ ] End-to-end training loop
+- [ ] Curriculum learning scheduler
+
+#### Month 19-24: Cross-Layer Attention
+
+**Implementation**:
+```rust
+// /crates/ruvector-core/src/index/hierarchical_routing.rs
+
+pub struct CrossLayerAttention {
+    query_encoder: TransformerEncoder,
+    layer_embeddings: Vec<Tensor>,  // Learned representations
+    attention: MultiHeadAttention,  // Reuse existing!
+}
+
+impl CrossLayerAttention {
+    pub fn route_query(&self, query: &[f32]) -> LayerDistribution {
+        let query_enc = self.query_encoder.forward(query);
+        let layer_scores = self.attention.forward(
+            &query_enc,
+            &self.layer_embeddings,
+            &self.layer_embeddings,
+        );
+        LayerDistribution { weights: softmax(layer_scores) }
+    }
+}
+```
+
+**Deliverables**:
+- [ ] Layer routing implementation
+- [ ] Integration with HNSW search
+- [ ] Benchmark on multi-scale datasets
+- [ ] Ablation: layer skipping impact
+
+---
+
+### Phase 3: Self-Organization (Months 25-42)
+
+**Objectives** (Era 2):
+1. Online topology optimization
+2. Multi-modal indexing
+3. Continual learning deployment
+
+**Implementation Priority**: Medium (research-focused)
+
+#### Month 25-30: Model Predictive Control
+
+**Key Component**: World model for predicting graph state transitions
+
+```rust
+// /crates/ruvector-core/src/index/self_organizing.rs
+
+pub struct WorldModel {
+    state_encoder: GNN,
+    action_encoder: Embedding,
+    transition_network: Sequential,
+}
+
+impl WorldModel {
+    pub fn predict_next_state(
+        &self,
+        state: &GraphState,
+        action: &RestructureAction,
+    ) -> GraphState {
+        let state_enc = self.state_encoder.forward(&state.graph);
+        let action_enc = self.action_encoder.forward(action);
+        let delta = self.transition_network.forward(&cat([state_enc, action_enc]));
+        self.apply_delta(state, delta)
+    }
+}
+```
+
+#### Month 31-36: Multi-Modal CLIP Training
+
+**Leverage Existing**: Use pre-trained CLIP encoders
+
+```rust
+pub struct MultiModalHNSW {
+    text_encoder: CLIPTextEncoder,    // Pre-trained
+    image_encoder: CLIPVisionEncoder,  // Pre-trained
+    shared_graph: HnswGraph,
+    fusion: CrossModalFusion,
+}
+```
+
+#### Month 37-42: Continual Learning Integration
+
+**Leverage Existing EWC + Replay Buffer**:
+```rust
+// Already have these in /crates/ruvector-gnn/!
+use ruvector_gnn::{ElasticWeightConsolidation, ReplayBuffer};
+
+pub struct ContinualHNSW {
+    index: HnswGraph,
+    ewc: ElasticWeightConsolidation,  // ✓ Already implemented
+    replay: ReplayBuffer,              // ✓ Already implemented
+    distillation: TeacherStudent,      // NEW: to implement
+    consolidation: SleepConsolidation, // NEW: to implement
+}
+```
+
+**Deliverables**:
+- [ ] MPC planner
+- [ ] Multi-modal training pipeline
+- [ ] Knowledge distillation
+- [ ] Sleep consolidation (offline replay)
+- [ ] Benchmark on CL datasets (Stream-51, CORe50)
+
+---
+
+### Phase 4: Cognitive Capabilities (Months 43-60)
+
+**Objectives** (Era 3):
+1. Memory-augmented navigation
+2. Query decomposition & reasoning
+3. Neural architecture search
+
+**Implementation Priority**: Low (long-term research)
+
+#### Month 43-48: Episodic Memory
+
+```rust
+pub struct EpisodicMemory {
+    experiences: VecDeque<QueryEpisode>,
+    episode_index: HnswGraph,  // Meta-index!
+}
+```
+
+#### Month 49-54: Reasoning Engine
+
+```rust
+pub struct ReasoningEngine {
+    query_parser: SemanticParser,
+    planner: HierarchicalPlanner,
+    executor: GraphQueryExecutor,
+}
+```
+
+#### Month 55-60: Neural Architecture Search
+
+```rust
+pub struct IndexNAS {
+    controller: RLController,
+    search_space: ArchitectureSpace,
+}
+```
+
+---
+
+### Phase 5: Post-Classical Exploration (Months 61-72)
+
+**Objectives** (Era 4):
+1. Quantum simulator experiments
+2. Neuromorphic hardware integration
+3. Foundation model pre-training
+
+**Implementation Priority**: Research-only (exploratory)
+
+---
+
+## 3. Resource Requirements
+
+### 3.1 Team Composition
+
+**Phase 1-2 (Months 1-24)**:
+- 1× Senior ML Engineer (full-time)
+- 1× Rust Systems Engineer (full-time)
+- 1× Research Scientist (50% time)
+- 1× ML Intern (rotating)
+
+**Phase 3-4 (Months 25-60)**:
+- 2× Senior ML Engineers
+- 1× Distributed Systems Engineer
+- 2× Research Scientists
+- 2× PhD Interns (rotating)
+
+**Phase 5 (Months 61-72)**:
+- 1× Quantum Computing Specialist
+- 1× Neuromorphic Hardware Engineer
+- 3× Research Scientists
+
+### 3.2 Compute Infrastructure
+
+| Phase | Hardware | Cost (AWS p3.2xlarge) |
+|-------|----------|-----------------------|
+| Phase 1 | 1× V100 GPU | $3/hr × 8hrs/day × 365 days = $8,760/year |
+| Phase 2 | 2× V100 GPUs | $17,520/year |
+| Phase 3 | 4× V100 GPUs | $35,040/year |
+| Phase 4 | 8× A100 GPUs | $100,000/year |
+| Phase 5 | Quantum Simulator + 8× A100 | $150,000/year |
+
+**Total 6-Year Budget**: ~$500,000
+
+### 3.3 Data & Benchmarks
+
+**Public Datasets**:
+- SIFT1M, GIST1M (standard ANN benchmarks)
+- DEEP1B (billion-scale)
+- MS-COCO, Flickr30k (multi-modal)
+- BEIR (information retrieval)
+
+**Private Datasets** (for validation):
+- Production query logs
+- User feedback data
+
+---
+
+## 4. Risk Assessment & Mitigation
+
+### 4.1 Technical Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| GNN overhead exceeds benefits | Medium | High | Profile carefully, start with lightweight models |
+| Joint optimization unstable | High | Medium | Curriculum learning, careful hyperparameter tuning |
+| RL navigation doesn't generalize | Medium | Medium | MAML meta-learning, diverse training environments |
+| Continual learning forgetting | Low | Low | Already have EWC + replay buffer |
+| Quantum hardware delays | High | Low | Focus on classical approximations, simulators |
+
+### 4.2 Research Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| No SOTA on benchmarks | Medium | High | Incremental publication strategy, target niche areas |
+| Reproducibility issues | Medium | Medium | Open-source all code, containerized environments |
+| Scalability bottlenecks | High | Medium | Distributed training infrastructure, profiling |
+| Theoretical gaps | Low | Low | Academic collaborations |
+
+### 4.3 Product Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| Users resist complexity | Medium | High | Provide simple defaults, gradual opt-in |
+| Latency regressions | High | High | A/B testing, fallback to classical |
+| Memory bloat | Medium | Medium | Aggressive compression, model distillation |
+| Compatibility breaks | Low | Medium | Semantic versioning, deprecation warnings |
+
+---
+
+## 5. Success Metrics
+
+### 5.1 Short-Term (Phase 1-2: Years 1-2)
+
+**Technical Metrics**:
+- Recall@10: +3-5% improvement
+- Query latency: <1.5× overhead (acceptable for quality gain)
+- Index size: 10-20% reduction
+- Training time: <12 hours for 1M vectors
+
+**Research Metrics**:
+- 2-3 papers at NeurIPS/ICML/ICLR/VLDB
+- Top-3 on ANN-Benchmarks.com (at least one dataset)
+
+**Community Metrics**:
+- 500+ GitHub stars
+- 10+ production deployments
+- 50+ community contributions
+
+### 5.2 Medium-Term (Phase 3-4: Years 3-5)
+
+**Technical Metrics**:
+- Recall@10: +8-12% total improvement
+- Continual learning: <5% forgetting
+- Multi-modal: Unified index with <30% overhead
+
+**Research Metrics**:
+- 8-10 papers published
+- 1-2 best paper awards
+- Industry collaborations (Google, Microsoft, Meta)
+
+**Community Metrics**:
+- 2000+ GitHub stars
+- 100+ production deployments
+- Conference workshop organized
+
+### 5.3 Long-Term (Phase 5: Years 6+)
+
+**Technical Metrics**:
+- Quantum speedup: 2-5× for specific subroutines
+- Neuromorphic energy efficiency: 100× improvement
+- Foundation model: 70%+ zero-shot performance
+
+**Research Metrics**:
+- Reference implementation for HNSW
+- Textbook citations
+- Industry standard adoption
+
+---
+
+## 6. Decision Points & Gates
+
+### Gate 1 (Month 12): Continue to Phase 2?
+
+**Criteria**:
+- [ ] Recall@10 improvement ≥ 2%
+- [ ] Latency overhead ≤ 2×
+- [ ] Training time ≤ 12 hours
+- [ ] 1+ paper accepted
+
+**Decision**: Go / Pivot / Stop
+
+### Gate 2 (Month 24): Continue to Phase 3?
+
+**Criteria**:
+- [ ] End-to-end optimization stable
+- [ ] Recall@10 improvement ≥ 5% cumulative
+- [ ] 10+ production deployments
+- [ ] 3+ papers accepted
+
+**Decision**: Go / Pivot / Stop
+
+### Gate 3 (Month 42): Continue to Phase 4?
+
+**Criteria**:
+- [ ] Continual learning <5% forgetting
+- [ ] Multi-modal unified index working
+- [ ] Top-3 on ANN-Benchmarks
+- [ ] Funding secured for Phase 4
+
+**Decision**: Go / Pivot / Stop
+
+---
+
+## 7. Integration with Existing RuVector
+
+### 7.1 Backward Compatibility
+
+**Strategy**: Feature flags + semantic versioning
+
+```rust
+// Cargo.toml
+[features]
+default = ["hnsw-classic"]
+hnsw-classic = []
+hnsw-adaptive = ["ruvector-gnn/adaptive-edges"]
+hnsw-rl-nav = ["ruvector-gnn/rl-navigation"]
+hnsw-e2e = ["hnsw-adaptive", "hnsw-rl-nav", "differentiable"]
+```
+
+**API Evolution**:
+```rust
+// v1.0 (Classic HNSW)
+let index = HnswIndex::new(dim, metric, config);
+
+// v2.0 (Adaptive HNSW - backward compatible)
+let index = HnswIndex::new(dim, metric, config)
+    .with_adaptive_edges()  // Opt-in
+    .with_learned_navigation();  // Opt-in
+
+// v3.0 (End-to-End)
+let index = AdaptiveHnswIndex::new(dim, metric)
+    .train_on(dataset);  // Auto-configuration
+```
+
+### 7.2 Migration Path
+
+**For Existing Users**:
+1. **Phase 1**: No action required (backward compatible)
+2. **Phase 2**: Optional feature flags for advanced users
+3. **Phase 3**: Gradual migration guide published
+4. **Phase 4**: Legacy support maintained for 2 years
+
+---
+
+## 8. Open-Source Strategy
+
+### 8.1 Publication Plan
+
+**Year 1-2**:
+- Paper 1: "GNN-Guided Edge Selection for HNSW" (ICML)
+- Paper 2: "Learned Navigation in HNSW via RL" (NeurIPS)
+
+**Year 3-4**:
+- Paper 3: "End-to-End Differentiable HNSW" (ICLR)
+- Paper 4: "Self-Organizing Adaptive Indexes" (VLDB)
+- Paper 5: "Multi-Modal Unified HNSW" (CVPR)
+
+**Year 5-6**:
+- Paper 6: "Continual Learning for Vector Indexes" (NeurIPS)
+- Paper 7: "Memory-Augmented Graph Navigation" (ICML)
+- Paper 8: "Neural Architecture Search for ANN" (AutoML)
+
+### 8.2 Community Engagement
+
+**Documentation**:
+- Comprehensive API docs (Rust doc)
+- Tutorial notebooks (Jupyter)
+- Blog posts (monthly)
+- Conference talks (2-3 per year)
+
+**Code Quality**:
+- 90%+ test coverage
+- Continuous benchmarking (CI/CD)
+- Profiling & optimization reports
+- Security audits (annual)
+
+---
+
+## 9. Alternative Approaches & Contingencies
+
+### 9.1 If GNN Edge Selection Fails
+
+**Fallback**: Learned threshold (simpler than full GNN)
+
+**Implementation**:
+```rust
+pub struct SimpleAdaptiveEdges {
+    threshold_predictor: XGBoost,  // Simpler than GNN
+}
+```
+
+### 9.2 If RL Navigation Doesn't Generalize
+
+**Fallback**: Behavioral cloning from expert trajectories
+
+**Implementation**:
+```rust
+pub struct SupervisedNavigator {
+    policy: Sequential,  // Supervised learning
+}
+```
+
+### 9.3 If Compute Budget Insufficient
+
+**Alternative**: Prioritize algorithmic innovations over scale
+- Focus on efficient architectures (MobileNet-style)
+- Knowledge distillation (large teacher → small student)
+- Pruning & quantization
+
+---
+
+## 10. Summary: Recommended Priorities
+
+### Immediate (Next 6 Months)
+
+**Priority 1**: GNN edge selection
+- **Effort**: 2 engineers × 6 months
+- **Risk**: Low (builds on existing GNN infrastructure)
+- **Impact**: High (2-4% recall improvement)
+
+**Priority 2**: RL navigation prototype
+- **Effort**: 1 engineer × 6 months
+- **Risk**: Medium (RL can be unstable)
+- **Impact**: Medium (path length reduction)
+
+**Priority 3**: Benchmark infrastructure
+- **Effort**: 1 engineer × 3 months
+- **Risk**: Low
+- **Impact**: High (enables rigorous evaluation)
+
+### Medium-Term (6-24 Months)
+
+- End-to-end optimization
+- Cross-layer attention
+- Multi-modal experiments
+
+### Long-Term (24+ Months)
+
+- Self-organization
+- Cognitive capabilities
+- Post-classical exploration
+
+---
+
+## References
+
+**Internal**:
+- `/crates/ruvector-core/src/index/hnsw.rs` - Current HNSW
+- `/crates/ruvector-gnn/` - GNN infrastructure
+- `/docs/latent-space/hnsw-evolution-overview.md` - Vision document
+
+**External**:
+- ANN-Benchmarks: http://ann-benchmarks.com/
+- RuVector GitHub: https://github.com/ruvnet/ruvector
+
+---
+
+**Document Version**: 1.0
+**Last Updated**: 2025-11-30
+**Next Review**: 2026-01-30 (Quarterly)
+**Owner**: RuVector Engineering Team