Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/vibecast-7sense/docs/adr/ADR-005-self-learning-hooks.md

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+# ADR-005: Self-Learning and Hooks Integration
+
+## Status
+
+Proposed
+
+## Date
+
+2026-01-15
+
+## Context
+
+7sense processes bioacoustic data through Perch 2.0 embeddings (1536-D vectors) stored in RuVector with HNSW indexing. To maximize the value of this acoustic geometry, we need a self-learning system that:
+
+1. Continuously improves retrieval quality based on user feedback
+2. Discovers and consolidates successful clustering configurations
+3. Learns species-specific embedding characteristics over time
+4. Prevents catastrophic forgetting when adapting to new domains (marine vs avian vs terrestrial)
+
+RuVector includes a built-in GNN layer designed for index self-improvement, and the claude-flow framework provides a comprehensive hooks system with 27 hooks and 12 background workers that can orchestrate continuous learning pipelines.
+
+## Decision
+
+We will implement a four-stage learning loop architecture integrated with claude-flow hooks, utilizing SONA (Self-Optimizing Neural Architecture) patterns and EWC++ (Elastic Weight Consolidation) for continual learning without forgetting.
+
+### Learning Loop Architecture
+
+```
++-------------------+     +------------------+     +-------------------+     +---------------------+
+|     RETRIEVE      | --> |      JUDGE       | --> |      DISTILL      | --> |    CONSOLIDATE      |
+| (HNSW + Pattern)  |     | (Verdict System) |     | (LoRA Fine-tune)  |     | (EWC++ Integration) |
++-------------------+     +------------------+     +-------------------+     +---------------------+
+         ^                                                                            |
+         |                                                                            |
+         +----------------------------------------------------------------------------+
+                                    Continuous Feedback Loop
+```
+
+#### Stage 1: RETRIEVE
+
+Fetch relevant patterns from the ReasoningBank using HNSW-indexed vector search:
+
+```bash
+# Search for similar bioacoustic analysis patterns
+npx @claude-flow/cli@latest memory search \
+  --query "whale song clustering high-frequency harmonics" \
+  --namespace patterns \
+  --limit 5 \
+  --threshold 0.7
+
+# Retrieve species-specific embedding characteristics
+npx @claude-flow/cli@latest hooks intelligence pattern-search \
+  --query "humpback whale vocalization" \
+  --namespace species \
+  --top-k 3
+```
+
+Performance characteristics:
+- HNSW retrieval: 150x-12,500x faster than brute force
+- Pattern matching: 761 decisions/sec
+- Sub-millisecond adaptation via SONA
+
+#### Stage 2: JUDGE
+
+Evaluate retrieved patterns with a verdict system that scores relevance and success:
+
+```typescript
+interface BioacousticVerdict {
+  pattern_id: string;
+  task_type: 'clustering' | 'motif_discovery' | 'species_identification' | 'anomaly_detection';
+  verdict: 'success' | 'partial' | 'failure';
+  confidence: number;  // 0.0 - 1.0
+  metrics: {
+    silhouette_score?: number;      // For clustering
+    retrieval_precision?: number;   // For search quality
+    user_correction_rate?: number;  // For feedback integration
+    snr_threshold_effectiveness?: number;
+  };
+  feedback_source: 'automatic' | 'user_correction' | 'expert_annotation';
+}
+```
+
+Verdict aggregation rules:
+- Success (confidence > 0.85): Promote pattern to long-term memory
+- Partial (0.5 < confidence < 0.85): Mark for refinement
+- Failure (confidence < 0.5): Demote or archive with failure context
+
+#### Stage 3: DISTILL
+
+Extract key learnings via LoRA (Low-Rank Adaptation) fine-tuning:
+
+```bash
+# Train neural patterns on successful bioacoustic analysis
+npx @claude-flow/cli@latest hooks intelligence trajectory-start \
+  --task "clustering whale songs by call type" \
+  --agent "bioacoustic-analyzer"
+
+# Record analysis steps
+npx @claude-flow/cli@latest hooks intelligence trajectory-step \
+  --trajectory-id "$TRAJ_ID" \
+  --action "applied hierarchical clustering with ward linkage" \
+  --result "silhouette score 0.78" \
+  --quality 0.85
+
+# Complete trajectory with success
+npx @claude-flow/cli@latest hooks intelligence trajectory-end \
+  --trajectory-id "$TRAJ_ID" \
+  --success true \
+  --feedback "user confirmed 23/25 clusters as valid call types"
+```
+
+LoRA benefits for bioacoustics:
+- 99% parameter reduction (critical for edge deployment on field sensors)
+- 10-100x faster training than full fine-tuning
+- Minimal memory footprint for continuous learning
+
+#### Stage 4: CONSOLIDATE
+
+Prevent catastrophic forgetting via EWC++ when learning new domains:
+
+```bash
+# Force SONA learning cycle with EWC++ consolidation
+npx @claude-flow/cli@latest hooks intelligence learn \
+  --consolidate true \
+  --trajectory-ids "$WHALE_TRAJ,$BIRD_TRAJ,$INSECT_TRAJ"
+```
+
+EWC++ strategy for bioacoustics:
+- Compute Fisher information matrix for critical embedding dimensions
+- Penalize changes to weights important for existing species recognition
+- Allow plasticity for new acoustic domains (marine -> avian -> terrestrial)
+
+### Claude-Flow Hooks Integration
+
+#### Pre-Task Hook: Route Bioacoustic Analysis Tasks
+
+The `pre-task` hook routes incoming analysis requests to optimal processing paths:
+
+```bash
+# Before starting any bioacoustic analysis
+npx @claude-flow/cli@latest hooks pre-task \
+  --task-id "analysis-$(date +%s)" \
+  --description "cluster humpback whale songs from Pacific Northwest dataset"
+```
+
+Routing decisions based on task characteristics:
+
+| Task Type | Recommended Agent | Model Tier | Rationale |
+|-----------|-------------------|------------|-----------|
+| Simple retrieval | retrieval-agent | Haiku | Fast kNN lookup |
+| Clustering | clustering-specialist | Sonnet | Algorithm selection |
+| Motif discovery | sequence-analyzer | Sonnet | Temporal pattern analysis |
+| Cross-species analysis | bioacoustic-expert | Opus | Complex reasoning |
+| Anomaly detection | anomaly-detector | Haiku | Real-time processing |
+| Embedding refinement | ml-specialist | Opus | Architecture decisions |
+
+Pre-task also retrieves relevant patterns:
+
+```bash
+# Get routing recommendation with pattern retrieval
+npx @claude-flow/cli@latest hooks route \
+  --task "identify dialect variations in orca pod communications" \
+  --context "Pacific Northwest, 2024 field recordings"
+```
+
+Output includes:
+- Recommended agent type and model tier
+- Top-3 similar successful patterns from memory
+- Suggested HNSW parameters based on past success
+- Estimated confidence and processing time
+
+#### Post-Task Hook: Store Successful Patterns
+
+After successful analysis, store the pattern for future retrieval:
+
+```bash
+# Record task completion
+npx @claude-flow/cli@latest hooks post-task \
+  --task-id "$TASK_ID" \
+  --success true \
+  --agent "clustering-specialist" \
+  --quality 0.92
+
+# Store the successful pattern
+npx @claude-flow/cli@latest memory store \
+  --namespace patterns \
+  --key "whale-clustering-hierarchical-ward-2026-01" \
+  --value '{
+    "task_type": "clustering",
+    "species_group": "cetacean",
+    "algorithm": "hierarchical",
+    "linkage": "ward",
+    "distance_metric": "cosine",
+    "min_cluster_size": 5,
+    "silhouette_score": 0.78,
+    "num_clusters_discovered": 23,
+    "snr_threshold": 15,
+    "embedding_preprocessing": "l2_normalize",
+    "hnsw_params": {"ef_construction": 200, "M": 32}
+  }'
+
+# Train neural patterns on the success
+npx @claude-flow/cli@latest hooks post-edit \
+  --file "analysis-results.json" \
+  --success true \
+  --train-neural true
+```
+
+#### Pre-Edit Hook: Context for Embedding Refinement
+
+Before modifying embedding configurations or HNSW parameters:
+
+```bash
+# Get context before editing embedding pipeline
+npx @claude-flow/cli@latest hooks pre-edit \
+  --file "src/embeddings/perch_config.rs" \
+  --operation "refactor"
+```
+
+Returns:
+- Related patterns that worked for similar configurations
+- Agent recommendations for the edit type
+- Risk assessment for the change
+- Suggested validation tests
+
+#### Post-Edit Hook: Train Neural Patterns
+
+After successful configuration changes:
+
+```bash
+# Record successful embedding refinement
+npx @claude-flow/cli@latest hooks post-edit \
+  --file "src/embeddings/perch_config.rs" \
+  --success true \
+  --agent "ml-specialist"
+
+# Store the refinement as a pattern
+npx @claude-flow/cli@latest hooks intelligence pattern-store \
+  --pattern "HNSW ef_search=150 optimal for whale song retrieval" \
+  --type "configuration" \
+  --confidence 0.88 \
+  --metadata '{"species": "cetacean", "corpus_size": 500000}'
+```
+
+### Memory Namespaces for Bioacoustics
+
+#### Namespace: `patterns`
+
+Stores successful clustering and analysis configurations:
+
+```bash
+# Store clustering pattern
+npx @claude-flow/cli@latest memory store \
+  --namespace patterns \
+  --key "birdsong-dbscan-dawn-chorus" \
+  --value '{
+    "algorithm": "DBSCAN",
+    "eps": 0.15,
+    "min_samples": 3,
+    "preprocessing": ["l2_normalize", "pca_128"],
+    "context": "dawn_chorus",
+    "success_rate": 0.91,
+    "species_groups": ["passerine", "corvid"],
+    "temporal_window": "04:00-07:00"
+  }'
+
+# Search for relevant patterns
+npx @claude-flow/cli@latest memory search \
+  --namespace patterns \
+  --query "clustering algorithm for dense dawn chorus recordings"
+```
+
+Pattern schema:
+```typescript
+interface ClusteringPattern {
+  algorithm: 'DBSCAN' | 'HDBSCAN' | 'hierarchical' | 'kmeans' | 'spectral';
+  parameters: Record<string, number | string>;
+  preprocessing: string[];
+  context: string;
+  success_rate: number;
+  species_groups: string[];
+  environmental_conditions?: {
+    habitat?: string;
+    time_of_day?: string;
+    season?: string;
+    weather?: string;
+  };
+  hnsw_tuning?: {
+    ef_construction: number;
+    ef_search: number;
+    M: number;
+  };
+}
+```
+
+#### Namespace: `motifs`
+
+Stores discovered sequence patterns and syntactic structures:
+
+```bash
+# Store discovered motif
+npx @claude-flow/cli@latest memory store \
+  --namespace motifs \
+  --key "humpback-song-unit-sequence-A" \
+  --value '{
+    "species": "Megaptera novaeangliae",
+    "pattern_type": "song_unit_sequence",
+    "sequence": ["A1", "A2", "B1", "A1", "C1"],
+    "transition_probabilities": {
+      "A1->A2": 0.85,
+      "A2->B1": 0.72,
+      "B1->A1": 0.68,
+      "A1->C1": 0.45
+    },
+    "typical_duration_ms": 45000,
+    "occurrence_rate": 0.34,
+    "recording_ids": ["rec_2024_001", "rec_2024_002"],
+    "discovered_by": "sequence-analyzer",
+    "confidence": 0.89
+  }'
+
+# Search for similar motifs
+npx @claude-flow/cli@latest memory search \
+  --namespace motifs \
+  --query "humpback whale song phrase transitions"
+```
+
+Motif schema:
+```typescript
+interface SequenceMotif {
+  species: string;
+  pattern_type: 'song_unit_sequence' | 'call_response' | 'alarm_cascade' | 'contact_pattern';
+  sequence: string[];
+  transition_probabilities: Record<string, number>;
+  typical_duration_ms: number;
+  occurrence_rate: number;
+  temporal_context?: {
+    time_of_day?: string;
+    season?: string;
+    behavioral_context?: string;
+  };
+  recording_ids: string[];
+  discovered_by: string;
+  confidence: number;
+  validation_status: 'automatic' | 'expert_verified' | 'disputed';
+}
+```
+
+#### Namespace: `species`
+
+Stores species-specific embedding characteristics:
+
+```bash
+# Store species embedding profile
+npx @claude-flow/cli@latest memory store \
+  --namespace species \
+  --key "orca-pacific-northwest-resident" \
+  --value '{
+    "species": "Orcinus orca",
+    "population": "Southern Resident",
+    "location": "Pacific Northwest",
+    "embedding_characteristics": {
+      "centroid_cluster_distance": 0.12,
+      "intra_pod_variance": 0.08,
+      "inter_pod_variance": 0.23,
+      "frequency_range_hz": [500, 12000],
+      "dominant_frequencies_hz": [2000, 5000, 8000]
+    },
+    "retrieval_optimization": {
+      "optimal_k": 15,
+      "distance_threshold": 0.25,
+      "ef_search": 200
+    },
+    "known_call_types": 34,
+    "dialect_markers": ["S01", "S02", "S03"],
+    "last_updated": "2026-01-15"
+  }'
+
+# Search for species characteristics
+npx @claude-flow/cli@latest memory search \
+  --namespace species \
+  --query "cetacean vocalization embedding characteristics Pacific"
+```
+
+Species schema:
+```typescript
+interface SpeciesEmbeddingProfile {
+  species: string;
+  population?: string;
+  location?: string;
+  embedding_characteristics: {
+    centroid_cluster_distance: number;
+    intra_population_variance: number;
+    inter_population_variance: number;
+    frequency_range_hz: [number, number];
+    dominant_frequencies_hz: number[];
+    embedding_norm_range?: [number, number];
+  };
+  retrieval_optimization: {
+    optimal_k: number;
+    distance_threshold: number;
+    ef_search: number;
+    ef_construction?: number;
+  };
+  known_call_types: number;
+  dialect_markers?: string[];
+  acoustic_niche?: {
+    typical_snr_db: number;
+    overlap_species: string[];
+    distinguishing_features: string[];
+  };
+  last_updated: string;
+}
+```
+
+### Background Workers Utilization
+
+#### Worker: `optimize` - HNSW Parameter Tuning
+
+Continuously optimizes HNSW parameters based on retrieval quality:
+
+```bash
+# Dispatch HNSW optimization worker
+npx @claude-flow/cli@latest hooks worker dispatch \
+  --trigger optimize \
+  --context "bioacoustic-hnsw" \
+  --priority high
+
+# Check optimization status
+npx @claude-flow/cli@latest hooks worker status
+```
+
+Optimization targets:
+- `ef_construction`: Balance between index build time and recall
+- `ef_search`: Balance between query latency and accuracy
+- `M`: Balance between memory usage and graph connectivity
+
+Automated tuning workflow:
+1. Sample recent queries and their success rates
+2. Run parameter sweep on subset
+3. Evaluate recall@k and latency
+4. Apply best parameters if improvement > 5%
+5. Store successful configuration in `patterns` namespace
+
+```typescript
+interface HNSWOptimizationResult {
+  previous_params: { ef_construction: number; ef_search: number; M: number };
+  new_params: { ef_construction: number; ef_search: number; M: number };
+  improvement: {
+    recall_at_10: number;  // Percentage improvement
+    latency_p99_ms: number;
+    memory_mb: number;
+  };
+  evaluation_corpus_size: number;
+  applied: boolean;
+  timestamp: string;
+}
+```
+
+#### Worker: `consolidate` - Memory Consolidation
+
+Consolidates learned patterns and prevents memory fragmentation:
+
+```bash
+# Dispatch consolidation worker (low priority, runs during idle)
+npx @claude-flow/cli@latest hooks worker dispatch \
+  --trigger consolidate \
+  --priority low \
+  --background true
+```
+
+Consolidation operations:
+1. Merge similar patterns within each namespace
+2. Archive low-confidence or stale patterns
+3. Update pattern embeddings for improved retrieval
+4. Compute and cache centroid patterns for fast routing
+5. Run EWC++ to protect critical learned weights
+
+```bash
+# Force SONA learning cycle with consolidation
+npx @claude-flow/cli@latest hooks intelligence learn \
+  --consolidate true
+```
+
+Consolidation schedule:
+- Hourly: Merge patterns with >0.95 similarity
+- Daily: Archive patterns not accessed in 30 days
+- Weekly: Full EWC++ consolidation pass
+
+#### Worker: `audit` - Data Quality Checks
+
+Validates embedding quality and detects drift:
+
+```bash
+# Dispatch audit worker
+npx @claude-flow/cli@latest hooks worker dispatch \
+  --trigger audit \
+  --context "embedding-quality" \
+  --priority critical
+```
+
+Audit checks:
+1. **Embedding health**: Detect NaN, infinity, or collapsed embeddings
+2. **Distribution drift**: Compare embedding statistics over time
+3. **Retrieval quality**: Sample-based precision/recall checks
+4. **Label consistency**: Cross-reference with expert annotations
+5. **Temporal coherence**: Verify sequence relationships
+
+```typescript
+interface AuditResult {
+  check_type: 'embedding_health' | 'distribution_drift' | 'retrieval_quality' | 'label_consistency';
+  status: 'pass' | 'warning' | 'fail';
+  metrics: {
+    nan_rate?: number;
+    norm_variance?: number;
+    drift_score?: number;
+    precision_at_10?: number;
+    consistency_rate?: number;
+  };
+  affected_recordings?: string[];
+  recommended_action?: string;
+  timestamp: string;
+}
+```
+
+Automated responses:
+- Warning: Log and notify, continue processing
+- Fail: Pause ingestion, alert operators, revert to last known good state
+
+### Transfer Learning from Related Projects
+
+#### Project Transfer Protocol
+
+Leverage patterns from related bioacoustic projects:
+
+```bash
+# Transfer patterns from a related whale research project
+npx @claude-flow/cli@latest hooks transfer \
+  --source-path "/projects/cetacean-acoustics" \
+  --min-confidence 0.8 \
+  --filter "species:cetacean"
+
+# Transfer from IPFS-distributed pattern registry
+npx @claude-flow/cli@latest hooks transfer store \
+  --pattern-id "marine-mammal-clustering-v2"
+```
+
+Transfer eligibility criteria:
+1. Source project confidence > 0.8
+2. Domain overlap > 50% (based on species groups)
+3. No conflicting patterns in target
+4. Embedding model compatibility (same Perch version)
+
+Transfer adaptation process:
+1. Retrieve candidate patterns from source
+2. Validate against target domain characteristics
+3. Apply domain adaptation if needed (fine-tune on local data)
+4. Integrate with reduced initial confidence (0.7x)
+5. Gradually increase confidence based on local success
+
+```bash
+# Check transfer candidates
+npx @claude-flow/cli@latest transfer store-search \
+  --query "bioacoustic clustering" \
+  --category "marine" \
+  --min-rating 4.0 \
+  --verified true
+```
+
+### Feedback Loops: User Corrections to Embedding Refinement
+
+#### Correction Capture
+
+```typescript
+interface UserCorrection {
+  correction_id: string;
+  timestamp: string;
+  user_id: string;
+  expertise_level: 'novice' | 'intermediate' | 'expert' | 'domain_expert';
+  correction_type: 'cluster_assignment' | 'species_label' | 'call_type' | 'sequence_boundary';
+  original_prediction: {
+    value: string;
+    confidence: number;
+    source: 'automatic' | 'pattern_match';
+  };
+  corrected_value: string;
+  affected_segments: string[];
+  context?: string;
+}
+```
+
+#### Feedback Integration Pipeline
+
+```bash
+# Step 1: Log user correction
+npx @claude-flow/cli@latest memory store \
+  --namespace corrections \
+  --key "correction-$(date +%s)-$USER" \
+  --value '{
+    "correction_type": "species_label",
+    "original": {"value": "Megaptera novaeangliae", "confidence": 0.72},
+    "corrected": "Balaenoptera musculus",
+    "segment_ids": ["seg_001", "seg_002"],
+    "user_expertise": "domain_expert"
+  }'
+
+# Step 2: Trigger learning from correction
+npx @claude-flow/cli@latest hooks intelligence trajectory-start \
+  --task "learn from species misclassification correction"
+
+npx @claude-flow/cli@latest hooks intelligence trajectory-step \
+  --trajectory-id "$TRAJ_ID" \
+  --action "analyzed embedding distance between humpback and blue whale" \
+  --result "found confounding frequency overlap in low-SNR segments" \
+  --quality 0.7
+
+npx @claude-flow/cli@latest hooks intelligence trajectory-end \
+  --trajectory-id "$TRAJ_ID" \
+  --success true \
+  --feedback "updated SNR threshold from 10 to 15 dB for cetacean classification"
+
+# Step 3: Update species namespace
+npx @claude-flow/cli@latest memory store \
+  --namespace species \
+  --key "blue-whale-humpback-distinction" \
+  --value '{
+    "confusion_pair": ["Megaptera novaeangliae", "Balaenoptera musculus"],
+    "distinguishing_features": ["frequency_range", "call_duration"],
+    "recommended_snr_threshold": 15,
+    "embedding_distance_threshold": 0.18
+  }'
+```
+
+#### Feedback Weight by Expertise
+
+| Expertise Level | Weight | Trigger Threshold | Immediate Action |
+|-----------------|--------|-------------------|------------------|
+| Domain Expert | 1.0 | 1 correction | Update pattern |
+| Expert | 0.8 | 2 corrections | Update pattern |
+| Intermediate | 0.5 | 5 corrections | Flag for review |
+| Novice | 0.2 | 10 corrections | Queue for expert |
+
+#### Continuous Refinement Loop
+
+```
+User Correction
+      |
+      v
++------------------+
+| Correction Store |  (namespace: corrections)
++------------------+
+      |
+      v
++------------------+
+| Pattern Analysis |  (identify affected patterns)
++------------------+
+      |
+      v
++------------------+
+| Verdict Update   |  (reduce confidence of failed patterns)
++------------------+
+      |
+      v
++------------------+
+| SONA Learning    |  (trajectory-based fine-tuning)
++------------------+
+      |
+      v
++------------------+
+| EWC++ Consolidate|  (protect other learned patterns)
++------------------+
+      |
+      v
++------------------+
+| Pattern Update   |  (store refined pattern)
++------------------+
+      |
+      v
+Improved Retrieval
+```
+
+### Implementation Checklist
+
+#### Phase 1: Core Infrastructure (Week 1-2)
+
+- [ ] Set up memory namespaces (`patterns`, `motifs`, `species`, `corrections`)
+- [ ] Implement pre-task hook for bioacoustic task routing
+- [ ] Implement post-task hook for pattern storage
+- [ ] Configure HNSW parameters for 1536-D Perch embeddings
+- [ ] Set up audit worker for embedding health checks
+
+#### Phase 2: Learning Integration (Week 3-4)
+
+- [ ] Implement trajectory tracking for analysis workflows
+- [ ] Configure LoRA fine-tuning for embedding refinement
+- [ ] Set up EWC++ consolidation schedule
+- [ ] Implement feedback capture from user interface
+- [ ] Configure optimize worker for HNSW tuning
+
+#### Phase 3: Advanced Features (Week 5-6)
+
+- [ ] Implement motif discovery and storage
+- [ ] Set up species-specific embedding profiles
+- [ ] Configure transfer learning from related projects
+- [ ] Implement expertise-weighted feedback integration
+- [ ] Set up consolidate worker for memory optimization
+
+#### Phase 4: Monitoring and Refinement (Ongoing)
+
+- [ ] Dashboard for learning metrics
+- [ ] Alerting for quality degradation
+- [ ] A/B testing for pattern effectiveness
+- [ ] Regular audit of learned patterns
+
+## Consequences
+
+### Positive
+
+1. **Continuous Improvement**: System gets better with every analysis task
+2. **Domain Adaptation**: EWC++ allows learning new species without forgetting existing knowledge
+3. **Expert Knowledge Capture**: User corrections are systematically integrated
+4. **Efficient Processing**: Pattern reuse reduces computation for common tasks
+5. **Transparent Learning**: Trajectory tracking provides explainability
+6. **Cross-Project Synergy**: Transfer learning leverages community knowledge
+
+### Negative
+
+1. **Complexity**: Multiple interacting systems require careful orchestration
+2. **Storage Growth**: Pattern storage will grow over time (mitigated by consolidation)
+3. **Cold Start**: Initial deployments lack learned patterns (mitigated by transfer)
+4. **Feedback Dependency**: Quality depends on user correction quality
+
+### Neutral
+
+1. **Operational Overhead**: Background workers require monitoring
+2. **Parameter Tuning**: Initial HNSW parameters need manual optimization
+3. **Expertise Requirements**: Domain experts needed for high-quality feedback
+
+## References
+
+1. RuVector GNN Architecture: https://github.com/ruvnet/ruvector
+2. SONA Pattern Documentation: claude-flow v3 hooks system
+3. EWC++ Paper: "Overcoming catastrophic forgetting in neural networks"
+4. Perch 2.0 Embeddings: https://arxiv.org/abs/2508.04665
+5. HNSW Algorithm: "Efficient and robust approximate nearest neighbor search"
+6. LoRA Fine-tuning: "LoRA: Low-Rank Adaptation of Large Language Models"