wifi-densepose/examples/ruvLLM/docs/SONA/06-COMPONENTS.md

SONA Component Integration

Overview

This document details how SONA integrates with the ruvector ecosystem and exo-ai cognitive crates to create a unified self-improving architecture.

Integration Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         SONA Integration Layer                          │
├─────────────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐    │
│  │  Learning   │  │   Router    │  │  Attention  │  │   Memory    │    │
│  │   Engine    │  │   Engine    │  │   Engine    │  │   Engine    │    │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘    │
│         │                │                │                │           │
├─────────┼────────────────┼────────────────┼────────────────┼───────────┤
│         │                │                │                │           │
│  ┌──────▼──────────────────────────────────────────────────▼──────┐    │
│  │                     ruvector Crates                            │    │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐  │    │
│  │  │  core   │ │attention│ │  gnn    │ │postgres │ │ sparse  │  │    │
│  │  │ (HNSW)  │ │(39 mech)│ │ (GNN)   │ │(persist)│ │(vectors)│  │    │
│  │  └─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘  │    │
│  └────────────────────────────────────────────────────────────────┘    │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐    │
│  │                     exo-ai Crates                              │    │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐  │    │
│  │  │exo-core │ │temporal │ │ exotic  │ │ memory  │ │attention│  │    │
│  │  │ (IIT/Φ) │ │(cycles) │ │(quantum)│ │(dreams) │ │ (39)    │  │    │
│  │  └─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘  │    │
│  └────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────┘

ruvector Crate Integration

1. ruvector-core (HNSW Index)

Purpose: High-performance approximate nearest neighbor search for pattern retrieval.

use ruvector_core::{HnswIndex, Distance, SearchParams};

/// Pattern index using HNSW for sub-millisecond retrieval
pub struct PatternIndex {
    index: HnswIndex<f32>,
    config: HnswConfig,
    metrics: IndexMetrics,
}

impl PatternIndex {
    pub fn new(dim: usize, max_patterns: usize) -> Self {
        Self {
            index: HnswIndex::new(HnswConfig {
                m: 16,                    // Connections per node
                ef_construction: 200,     // Build quality
                ef_search: 50,            // Search quality
                max_elements: max_patterns,
                dimension: dim,
            }),
            config: HnswConfig::default(),
            metrics: IndexMetrics::default(),
        }
    }

    /// Add pattern embedding to index
    pub fn add_pattern(&mut self, id: u64, embedding: &[f32]) -> Result<(), IndexError> {
        self.index.insert(id, embedding)?;
        self.metrics.total_patterns += 1;
        Ok(())
    }

    /// Find k nearest patterns
    pub fn find_similar(&self, query: &[f32], k: usize) -> Vec<(u64, f32)> {
        self.index.search(query, k, SearchParams {
            ef: self.config.ef_search,
        })
    }

    /// Batch search for multiple queries
    pub fn batch_search(&self, queries: &[Vec<f32>], k: usize) -> Vec<Vec<(u64, f32)>> {
        queries.par_iter()
            .map(|q| self.find_similar(q, k))
            .collect()
    }
}

#[derive(Default)]
pub struct IndexMetrics {
    pub total_patterns: usize,
    pub avg_search_time_us: f64,
    pub cache_hit_rate: f32,
}

Integration Points:

• ReasoningBank pattern storage
• Dream memory retrieval
• Router context lookup

2. ruvector-attention (39 Mechanisms)

Purpose: Diverse attention mechanisms for different reasoning patterns.

use ruvector_attention::{
    AttentionMechanism, MultiHeadAttention, LinearAttention,
    SparseAttention, FlashAttention, KernelizedAttention
};

/// Adaptive attention selector based on query characteristics
pub struct AdaptiveAttention {
    mechanisms: Vec<Box<dyn AttentionMechanism>>,
    router: AttentionRouter,
    performance_tracker: PerformanceTracker,
}

impl AdaptiveAttention {
    pub fn new(hidden_dim: usize, num_heads: usize) -> Self {
        Self {
            mechanisms: vec![
                Box::new(MultiHeadAttention::new(hidden_dim, num_heads)),
                Box::new(LinearAttention::new(hidden_dim)),
                Box::new(SparseAttention::new(hidden_dim, 0.1)),  // 10% sparsity
                Box::new(FlashAttention::new(hidden_dim, num_heads)),
                Box::new(KernelizedAttention::new(hidden_dim, "elu")),
            ],
            router: AttentionRouter::new(5),
            performance_tracker: PerformanceTracker::new(),
        }
    }

    /// Select optimal attention mechanism based on context
    pub fn forward(&mut self, q: &Tensor, k: &Tensor, v: &Tensor) -> Tensor {
        // Analyze query characteristics
        let features = self.analyze_query(q);

        // Route to best mechanism
        let mechanism_idx = self.router.route(&features);

        // Execute attention
        let start = Instant::now();
        let output = self.mechanisms[mechanism_idx].forward(q, k, v);
        let elapsed = start.elapsed();

        // Track performance
        self.performance_tracker.record(mechanism_idx, elapsed);

        output
    }

    fn analyze_query(&self, q: &Tensor) -> AttentionFeatures {
        AttentionFeatures {
            sequence_length: q.shape()[1],
            sparsity: q.sparsity_ratio(),
            entropy: q.attention_entropy(),
            locality: q.attention_locality(),
        }
    }
}

/// Routes queries to optimal attention mechanism
pub struct AttentionRouter {
    weights: Vec<f32>,
    history: CircularBuffer<RoutingDecision>,
}

impl AttentionRouter {
    pub fn route(&self, features: &AttentionFeatures) -> usize {
        // Decision logic based on features
        if features.sequence_length > 4096 {
            2  // SparseAttention for long sequences
        } else if features.sparsity > 0.5 {
            2  // SparseAttention for sparse patterns
        } else if features.locality > 0.8 {
            3  // FlashAttention for local patterns
        } else {
            0  // Default MultiHeadAttention
        }
    }

    pub fn update_from_feedback(&mut self, decision: usize, quality: f32) {
        self.history.push(RoutingDecision { decision, quality });
        // Online learning of routing weights
        self.weights[decision] += 0.01 * (quality - self.weights[decision]);
    }
}

Integration Points:

• Query processing pipeline
• Dream pattern recognition
• Cross-memory attention

3. ruvector-gnn (Graph Neural Networks)

Purpose: Graph-based reasoning over knowledge structures.

use ruvector_gnn::{GraphConv, GraphAttention, MessagePassing};

/// Knowledge graph reasoning with GNN
pub struct KnowledgeGraph {
    nodes: HashMap<NodeId, NodeEmbedding>,
    edges: Vec<Edge>,
    gnn: GraphNeuralNetwork,
    graph_index: GraphIndex,
}

#[derive(Clone)]
pub struct NodeEmbedding {
    pub id: NodeId,
    pub embedding: Vec<f32>,
    pub node_type: NodeType,
    pub importance: f32,
    pub last_accessed: Instant,
}

#[derive(Clone, Copy)]
pub enum NodeType {
    Concept,
    Pattern,
    Episode,
    Procedure,
    Dream,
}

impl KnowledgeGraph {
    pub fn new(embedding_dim: usize, hidden_dim: usize) -> Self {
        Self {
            nodes: HashMap::new(),
            edges: Vec::new(),
            gnn: GraphNeuralNetwork::new(embedding_dim, hidden_dim, 3),  // 3 layers
            graph_index: GraphIndex::new(),
        }
    }

    /// Add node to knowledge graph
    pub fn add_node(&mut self, id: NodeId, embedding: Vec<f32>, node_type: NodeType) {
        let node = NodeEmbedding {
            id,
            embedding: embedding.clone(),
            node_type,
            importance: 1.0,
            last_accessed: Instant::now(),
        };
        self.nodes.insert(id, node);
        self.graph_index.add(id, &embedding);
    }

    /// Create edge between nodes
    pub fn add_edge(&mut self, from: NodeId, to: NodeId, edge_type: EdgeType, weight: f32) {
        self.edges.push(Edge { from, to, edge_type, weight });
    }

    /// Propagate information through graph
    pub fn propagate(&mut self, query: &[f32], hops: usize) -> Vec<(NodeId, f32)> {
        // Find seed nodes
        let seeds = self.graph_index.search(query, 10);

        // Message passing through GNN layers
        let mut activations = HashMap::new();
        for (node_id, score) in seeds {
            activations.insert(node_id, score);
        }

        for _hop in 0..hops {
            let new_activations = self.gnn.propagate(&self.nodes, &self.edges, &activations);
            activations = new_activations;
        }

        // Return top activated nodes
        let mut results: Vec<_> = activations.into_iter().collect();
        results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        results.truncate(20);
        results
    }

    /// Learn new edge from pattern
    pub fn learn_edge(&mut self, pattern: &LearnedPattern) {
        // Extract node relationships from pattern
        let source_nodes = self.find_related_nodes(&pattern.centroid, pattern.cluster_size);

        for i in 0..source_nodes.len() {
            for j in i+1..source_nodes.len() {
                let strength = cosine_similarity(
                    &self.nodes[&source_nodes[i]].embedding,
                    &self.nodes[&source_nodes[j]].embedding
                );

                if strength > 0.7 {
                    self.add_edge(
                        source_nodes[i],
                        source_nodes[j],
                        EdgeType::Pattern,
                        strength
                    );
                }
            }
        }
    }
}

/// Graph neural network with multiple layer types
pub struct GraphNeuralNetwork {
    layers: Vec<GnnLayer>,
    aggregator: Aggregator,
}

enum GnnLayer {
    GraphConv(GraphConv),
    GraphAttention(GraphAttention),
    MessagePassing(MessagePassing),
}

Integration Points:

• Knowledge representation
• Dream creative connections
• Pattern relationship discovery

4. ruvector-postgres (Persistence)

Purpose: Durable storage for learned knowledge.

use ruvector_postgres::{PgVector, PgStore, VectorIndex};

/// Persistent pattern storage with PostgreSQL
pub struct PatternStore {
    pool: PgPool,
    vector_index: VectorIndex,
    cache: LruCache<PatternId, LearnedPattern>,
}

impl PatternStore {
    pub async fn new(database_url: &str) -> Result<Self, PgError> {
        let pool = PgPool::connect(database_url).await?;

        // Initialize schema
        sqlx::query(r#"
            CREATE TABLE IF NOT EXISTS patterns (
                id BIGSERIAL PRIMARY KEY,
                embedding vector(256),
                centroid vector(256),
                cluster_size INTEGER,
                total_weight FLOAT,
                avg_quality FLOAT,
                created_at TIMESTAMP DEFAULT NOW(),
                last_accessed TIMESTAMP DEFAULT NOW(),
                access_count INTEGER DEFAULT 0,
                pattern_type VARCHAR(50),
                metadata JSONB
            );

            CREATE INDEX IF NOT EXISTS patterns_embedding_idx
            ON patterns USING ivfflat (embedding vector_cosine_ops);

            CREATE INDEX IF NOT EXISTS patterns_centroid_idx
            ON patterns USING hnsw (centroid vector_cosine_ops);
        "#).execute(&pool).await?;

        Ok(Self {
            pool,
            vector_index: VectorIndex::new(256),
            cache: LruCache::new(NonZeroUsize::new(10000).unwrap()),
        })
    }

    /// Store pattern with vector embedding
    pub async fn store_pattern(&mut self, pattern: &LearnedPattern) -> Result<i64, PgError> {
        let embedding_vec: Vec<f32> = pattern.centroid.clone();

        let row = sqlx::query_scalar::<_, i64>(r#"
            INSERT INTO patterns (embedding, centroid, cluster_size, total_weight, avg_quality, pattern_type, metadata)
            VALUES ($1, $2, $3, $4, $5, $6, $7)
            RETURNING id
        "#)
        .bind(&embedding_vec)
        .bind(&embedding_vec)
        .bind(pattern.cluster_size as i32)
        .bind(pattern.total_weight)
        .bind(pattern.avg_quality)
        .bind("learned")
        .bind(serde_json::to_value(&pattern.metadata).unwrap())
        .fetch_one(&self.pool)
        .await?;

        // Update cache
        self.cache.put(row, pattern.clone());

        Ok(row)
    }

    /// Find similar patterns using vector similarity
    pub async fn find_similar(&self, embedding: &[f32], k: usize) -> Result<Vec<LearnedPattern>, PgError> {
        let rows = sqlx::query_as::<_, PatternRow>(r#"
            SELECT id, embedding, centroid, cluster_size, total_weight, avg_quality, metadata
            FROM patterns
            ORDER BY embedding <=> $1
            LIMIT $2
        "#)
        .bind(embedding)
        .bind(k as i64)
        .fetch_all(&self.pool)
        .await?;

        Ok(rows.into_iter().map(|r| r.into()).collect())
    }

    /// Consolidate patterns (merge similar, prune weak)
    pub async fn consolidate(&mut self) -> Result<ConsolidationResult, PgError> {
        // Find patterns to merge (similarity > 0.95)
        let merge_candidates = sqlx::query(r#"
            SELECT p1.id as id1, p2.id as id2,
                   1 - (p1.centroid <=> p2.centroid) as similarity
            FROM patterns p1
            JOIN patterns p2 ON p1.id < p2.id
            WHERE 1 - (p1.centroid <=> p2.centroid) > 0.95
            LIMIT 100
        "#).fetch_all(&self.pool).await?;

        // Prune weak patterns (low quality, low access)
        let pruned = sqlx::query(r#"
            DELETE FROM patterns
            WHERE avg_quality < 0.3
              AND access_count < 5
              AND created_at < NOW() - INTERVAL '7 days'
            RETURNING id
        "#).fetch_all(&self.pool).await?;

        Ok(ConsolidationResult {
            merged: merge_candidates.len(),
            pruned: pruned.len(),
        })
    }
}

Integration Points:

• Long-term pattern persistence
• Dream memory storage
• Knowledge graph persistence

5. ruvector-sparse (Sparse Vectors)

Purpose: Efficient sparse vector operations for pattern matching.

use ruvector_sparse::{SparseVector, SparseDot, SparseIndex};

/// Sparse pattern representation for efficient storage
pub struct SparsePatternStore {
    index: SparseIndex,
    patterns: Vec<SparsePattern>,
}

#[derive(Clone)]
pub struct SparsePattern {
    pub id: u64,
    pub indices: Vec<u32>,
    pub values: Vec<f32>,
    pub nnz: usize,  // Non-zero count
    pub metadata: PatternMetadata,
}

impl SparsePatternStore {
    pub fn new(dim: usize) -> Self {
        Self {
            index: SparseIndex::new(dim),
            patterns: Vec::new(),
        }
    }

    /// Convert dense pattern to sparse representation
    pub fn add_pattern(&mut self, dense: &[f32], threshold: f32) -> u64 {
        let (indices, values): (Vec<u32>, Vec<f32>) = dense.iter()
            .enumerate()
            .filter(|(_, &v)| v.abs() > threshold)
            .map(|(i, &v)| (i as u32, v))
            .unzip();

        let id = self.patterns.len() as u64;
        let pattern = SparsePattern {
            id,
            nnz: indices.len(),
            indices,
            values,
            metadata: PatternMetadata::default(),
        };

        self.index.insert(id, &pattern.indices, &pattern.values);
        self.patterns.push(pattern);

        id
    }

    /// Fast sparse dot product search
    pub fn search(&self, query_indices: &[u32], query_values: &[f32], k: usize) -> Vec<(u64, f32)> {
        self.index.search_sparse(query_indices, query_values, k)
    }

    /// Batch sparse search with SIMD acceleration
    #[cfg(target_arch = "x86_64")]
    pub fn batch_search_simd(&self, queries: &[SparseVector], k: usize) -> Vec<Vec<(u64, f32)>> {
        use std::arch::x86_64::*;

        queries.par_iter()
            .map(|q| {
                // SIMD-accelerated sparse dot products
                let mut scores = Vec::with_capacity(self.patterns.len());

                for pattern in &self.patterns {
                    let score = unsafe {
                        sparse_dot_simd(&q.indices, &q.values, &pattern.indices, &pattern.values)
                    };
                    scores.push((pattern.id, score));
                }

                // Top-k selection
                scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
                scores.truncate(k);
                scores
            })
            .collect()
    }
}

/// SIMD-accelerated sparse dot product
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn sparse_dot_simd(
    idx1: &[u32], val1: &[f32],
    idx2: &[u32], val2: &[f32]
) -> f32 {
    let mut i = 0;
    let mut j = 0;
    let mut sum = _mm256_setzero_ps();

    // Merge-join with SIMD accumulation
    while i + 8 <= idx1.len() && j + 8 <= idx2.len() {
        let idx1_vec = _mm256_loadu_si256(idx1[i..].as_ptr() as *const __m256i);
        let idx2_vec = _mm256_loadu_si256(idx2[j..].as_ptr() as *const __m256i);

        // Compare and accumulate matching indices
        // ... SIMD comparison logic ...

        i += 8;
        j += 8;
    }

    // Reduce SIMD accumulator
    let mut result = [0.0f32; 8];
    _mm256_storeu_ps(result.as_mut_ptr(), sum);
    result.iter().sum()
}

Integration Points:

• Pattern compression
• Fast similarity search
• Memory-efficient storage

---

exo-ai Crate Integration

1. exo-core (IIT/Φ Measurement)

Purpose: Integrated Information Theory for consciousness metrics.

use exo_core::{PhiComputer, IntegratedInformation, Constellation};

/// Φ-based quality measurement for reasoning traces
pub struct PhiEvaluator {
    phi_computer: PhiComputer,
    history: Vec<PhiMeasurement>,
    threshold: f64,
}

#[derive(Clone)]
pub struct PhiMeasurement {
    pub phi_value: f64,
    pub main_complex: Constellation,
    pub timestamp: Instant,
    pub context: String,
}

impl PhiEvaluator {
    pub fn new(threshold: f64) -> Self {
        Self {
            phi_computer: PhiComputer::new(),
            history: Vec::new(),
            threshold,
        }
    }

    /// Measure integrated information of reasoning trace
    pub fn measure_phi(&mut self, trace: &ReasoningTrace) -> PhiMeasurement {
        // Build state transition matrix from trace
        let tpm = self.build_tpm(trace);

        // Compute Φ using IIT 3.0
        let result = self.phi_computer.compute_phi(&tpm);

        let measurement = PhiMeasurement {
            phi_value: result.phi,
            main_complex: result.main_complex,
            timestamp: Instant::now(),
            context: trace.query.clone(),
        };

        self.history.push(measurement.clone());
        measurement
    }

    /// Check if reasoning meets integration threshold
    pub fn is_integrated(&self, measurement: &PhiMeasurement) -> bool {
        measurement.phi_value >= self.threshold
    }

    fn build_tpm(&self, trace: &ReasoningTrace) -> TransitionMatrix {
        let n = trace.steps.len();
        let mut tpm = TransitionMatrix::zeros(n, n);

        for i in 0..n-1 {
            let from_state = &trace.steps[i];
            let to_state = &trace.steps[i+1];

            // Compute transition probability based on embedding similarity
            let similarity = cosine_similarity(&from_state.embedding, &to_state.embedding);
            tpm[(i, i+1)] = similarity;
        }

        tpm
    }

    /// Evaluate dream quality using Φ
    pub fn evaluate_dream(&mut self, dream: &Dream) -> f64 {
        let trace = ReasoningTrace {
            query: "dream".to_string(),
            steps: dream.path.iter()
                .map(|node| ReasoningStep {
                    embedding: node.embedding.clone(),
                    ..Default::default()
                })
                .collect(),
            ..Default::default()
        };

        self.measure_phi(&trace).phi_value
    }
}

/// Reasoning trace for Φ analysis
pub struct ReasoningTrace {
    pub query: String,
    pub steps: Vec<ReasoningStep>,
    pub final_answer: Option<String>,
    pub quality_score: f32,
}

pub struct ReasoningStep {
    pub embedding: Vec<f32>,
    pub attention_pattern: Vec<f32>,
    pub activated_nodes: Vec<NodeId>,
}

Integration Points:

• Dream quality evaluation
• Reasoning coherence measurement
• Learning signal generation

2. exo-temporal (Temporal Cycles)

Purpose: Temporal pattern recognition and prediction.

use exo_temporal::{TemporalEncoder, CycleDetector, Predictor};

/// Temporal pattern learning for usage prediction
pub struct TemporalLearner {
    encoder: TemporalEncoder,
    cycle_detector: CycleDetector,
    predictor: Predictor,
    patterns: Vec<TemporalPattern>,
}

#[derive(Clone)]
pub struct TemporalPattern {
    pub id: u64,
    pub period: Duration,
    pub phase: f32,
    pub amplitude: f32,
    pub pattern_type: TemporalPatternType,
}

#[derive(Clone, Copy)]
pub enum TemporalPatternType {
    Daily,
    Weekly,
    Bursty,
    Seasonal,
    Custom,
}

impl TemporalLearner {
    pub fn new(encoding_dim: usize) -> Self {
        Self {
            encoder: TemporalEncoder::new(encoding_dim),
            cycle_detector: CycleDetector::new(),
            predictor: Predictor::new(encoding_dim, 64),
            patterns: Vec::new(),
        }
    }

    /// Record event with timestamp
    pub fn record_event(&mut self, event: &Event, timestamp: Instant) {
        let encoding = self.encoder.encode(timestamp, event);
        self.cycle_detector.add_observation(encoding, timestamp);
    }

    /// Detect temporal patterns
    pub fn detect_patterns(&mut self) -> Vec<TemporalPattern> {
        let cycles = self.cycle_detector.find_cycles();

        self.patterns = cycles.into_iter()
            .enumerate()
            .map(|(i, cycle)| TemporalPattern {
                id: i as u64,
                period: cycle.period,
                phase: cycle.phase,
                amplitude: cycle.amplitude,
                pattern_type: self.classify_cycle(&cycle),
            })
            .collect();

        self.patterns.clone()
    }

    fn classify_cycle(&self, cycle: &Cycle) -> TemporalPatternType {
        let hours = cycle.period.as_secs_f64() / 3600.0;

        if (23.0..25.0).contains(&hours) {
            TemporalPatternType::Daily
        } else if (166.0..170.0).contains(&hours) {
            TemporalPatternType::Weekly
        } else if hours < 1.0 {
            TemporalPatternType::Bursty
        } else {
            TemporalPatternType::Custom
        }
    }

    /// Predict optimal times for background learning
    pub fn predict_learning_windows(&self) -> Vec<TimeWindow> {
        let mut windows = Vec::new();

        for pattern in &self.patterns {
            if matches!(pattern.pattern_type, TemporalPatternType::Daily | TemporalPatternType::Weekly) {
                // Find low-activity periods
                let low_activity_phase = pattern.phase + std::f32::consts::PI;  // Opposite phase
                windows.push(TimeWindow {
                    start: self.phase_to_time(low_activity_phase, pattern.period),
                    duration: Duration::from_secs(3600),  // 1 hour window
                    priority: pattern.amplitude,
                });
            }
        }

        windows.sort_by(|a, b| b.priority.partial_cmp(&a.priority).unwrap());
        windows
    }

    fn phase_to_time(&self, phase: f32, period: Duration) -> Instant {
        let period_secs = period.as_secs_f32();
        let offset = (phase / (2.0 * std::f32::consts::PI)) * period_secs;
        Instant::now() + Duration::from_secs_f32(offset)
    }
}

pub struct TimeWindow {
    pub start: Instant,
    pub duration: Duration,
    pub priority: f32,
}

Integration Points:

• Learning schedule optimization
• Usage pattern prediction
• Adaptive resource allocation

3. exo-exotic (Quantum-Inspired)

Purpose: Quantum-inspired optimization for creative exploration.

use exo_exotic::{QuantumState, SuperpositionSampler, EntanglementGraph};

/// Quantum-inspired creative exploration
pub struct QuantumExplorer {
    state: QuantumState,
    sampler: SuperpositionSampler,
    entanglement: EntanglementGraph,
}

impl QuantumExplorer {
    pub fn new(dim: usize) -> Self {
        Self {
            state: QuantumState::new(dim),
            sampler: SuperpositionSampler::new(),
            entanglement: EntanglementGraph::new(),
        }
    }

    /// Create superposition of pattern states
    pub fn create_superposition(&mut self, patterns: &[LearnedPattern]) -> Superposition {
        let amplitudes: Vec<Complex64> = patterns.iter()
            .map(|p| {
                let magnitude = (p.avg_quality as f64).sqrt();
                let phase = p.total_weight as f64 * 0.1;
                Complex64::from_polar(magnitude, phase)
            })
            .collect();

        self.state.set_amplitudes(&amplitudes);

        Superposition {
            patterns: patterns.to_vec(),
            amplitudes: amplitudes.clone(),
            entanglement_strength: self.measure_entanglement(&amplitudes),
        }
    }

    /// Sample from superposition for creative exploration
    pub fn sample_creative(&self, superposition: &Superposition, n_samples: usize) -> Vec<CreativeSample> {
        self.sampler.sample(&superposition.amplitudes, n_samples)
            .into_iter()
            .enumerate()
            .map(|(i, prob)| {
                let pattern_idx = self.probability_to_index(prob, superposition.patterns.len());
                CreativeSample {
                    base_pattern: superposition.patterns[pattern_idx].clone(),
                    perturbation: self.quantum_perturbation(prob),
                    novelty_score: 1.0 - prob,  // Lower probability = more novel
                }
            })
            .collect()
    }

    fn measure_entanglement(&self, amplitudes: &[Complex64]) -> f64 {
        // Compute von Neumann entropy as entanglement measure
        let probs: Vec<f64> = amplitudes.iter()
            .map(|a| a.norm_sqr())
            .collect();

        -probs.iter()
            .filter(|&&p| p > 1e-10)
            .map(|&p| p * p.ln())
            .sum::<f64>()
    }

    fn quantum_perturbation(&self, prob: f64) -> Vec<f32> {
        // Generate quantum-inspired perturbation
        let dim = self.state.dimension();
        let mut rng = rand::thread_rng();

        (0..dim)
            .map(|_| {
                let phase = rng.gen::<f64>() * 2.0 * std::f64::consts::PI;
                let amplitude = (1.0 - prob).sqrt();
                (amplitude * phase.cos()) as f32 * 0.1
            })
            .collect()
    }

    fn probability_to_index(&self, prob: f64, n: usize) -> usize {
        ((prob * n as f64) as usize).min(n - 1)
    }
}

pub struct Superposition {
    pub patterns: Vec<LearnedPattern>,
    pub amplitudes: Vec<Complex64>,
    pub entanglement_strength: f64,
}

pub struct CreativeSample {
    pub base_pattern: LearnedPattern,
    pub perturbation: Vec<f32>,
    pub novelty_score: f64,
}

Integration Points:

• Dream creative jumps
• Novel pattern generation
• Exploration-exploitation balance

---

Unified Integration Layer

SONA Integration Manager

/// Central integration manager for all SONA components
pub struct SonaIntegration {
    // ruvector components
    pub pattern_index: PatternIndex,
    pub attention: AdaptiveAttention,
    pub knowledge_graph: KnowledgeGraph,
    pub pattern_store: PatternStore,
    pub sparse_store: SparsePatternStore,

    // exo-ai components
    pub phi_evaluator: PhiEvaluator,
    pub temporal_learner: TemporalLearner,
    pub quantum_explorer: QuantumExplorer,

    // Core SONA components
    pub lora_engine: LoraEngine,
    pub reasoning_bank: ReasoningBank,
    pub dream_engine: DreamEngine,
    pub ewc: EwcPlusPlus,

    // Coordination
    pub loop_coordinator: LoopCoordinator,
    pub metrics: IntegrationMetrics,
}

impl SonaIntegration {
    pub async fn new(config: SonaConfig) -> Result<Self, SonaError> {
        Ok(Self {
            pattern_index: PatternIndex::new(config.embedding_dim, config.max_patterns),
            attention: AdaptiveAttention::new(config.hidden_dim, config.num_heads),
            knowledge_graph: KnowledgeGraph::new(config.embedding_dim, config.hidden_dim),
            pattern_store: PatternStore::new(&config.database_url).await?,
            sparse_store: SparsePatternStore::new(config.embedding_dim),
            phi_evaluator: PhiEvaluator::new(config.phi_threshold),
            temporal_learner: TemporalLearner::new(config.temporal_dim),
            quantum_explorer: QuantumExplorer::new(config.embedding_dim),
            lora_engine: LoraEngine::new(config.lora_config),
            reasoning_bank: ReasoningBank::new(config.pattern_config),
            dream_engine: DreamEngine::new(config.dream_config),
            ewc: EwcPlusPlus::new(config.ewc_config),
            loop_coordinator: LoopCoordinator::new(),
            metrics: IntegrationMetrics::default(),
        })
    }

    /// Process query through unified pipeline
    pub async fn process(&mut self, query: &str, context: &Context) -> Result<Response, SonaError> {
        let start = Instant::now();

        // 1. Record temporal event
        self.temporal_learner.record_event(&Event::Query(query.to_string()), Instant::now());

        // 2. Embed query
        let query_embedding = self.embed_query(query);

        // 3. Find similar patterns (parallel)
        let (similar_patterns, graph_context, sparse_matches) = tokio::join!(
            self.pattern_index.find_similar(&query_embedding, 10),
            self.knowledge_graph.propagate(&query_embedding, 3),
            async { self.sparse_store.search(&[], &[], 5) }  // Sparse backup
        );

        // 4. Apply adaptive attention
        let attended = self.attention.forward(&query_embedding, &context, &similar_patterns);

        // 5. Generate response with LoRA
        let response = self.lora_engine.forward(&attended);

        // 6. Record trajectory
        let trajectory = QueryTrajectory {
            query: query.to_string(),
            steps: vec![/* reasoning steps */],
            response: response.clone(),
            quality: self.evaluate_quality(&response),
        };

        // 7. Signal learning (async)
        let signal = LearningSignal::from_trajectory(&trajectory);
        self.loop_coordinator.signal_learning(signal);

        self.metrics.queries_processed += 1;
        self.metrics.avg_latency_ms =
            (self.metrics.avg_latency_ms * 0.99) + (start.elapsed().as_millis() as f64 * 0.01);

        Ok(Response {
            text: response.text,
            confidence: response.confidence,
            patterns_used: similar_patterns.len(),
        })
    }

    /// Run background learning cycle
    pub async fn background_learn(&mut self) -> Result<LearningResult, SonaError> {
        // Check if good time for learning
        let windows = self.temporal_learner.predict_learning_windows();

        // Extract patterns from reasoning bank
        let patterns = self.reasoning_bank.extract_patterns();

        // Evaluate patterns with Φ
        for pattern in &patterns {
            let trace = pattern.to_reasoning_trace();
            let phi = self.phi_evaluator.measure_phi(&trace);

            if self.phi_evaluator.is_integrated(&phi) {
                // High-quality pattern - persist
                self.pattern_store.store_pattern(pattern).await?;
                self.knowledge_graph.learn_edge(pattern);
            }
        }

        // Update LoRA with EWC++
        let gradients = self.lora_engine.compute_gradients(&patterns);
        let safe_gradients = self.ewc.apply_constraints(&gradients);
        self.lora_engine.apply_update(&safe_gradients);

        // Consolidate storage
        self.pattern_store.consolidate().await?;

        Ok(LearningResult {
            patterns_learned: patterns.len(),
            patterns_persisted: patterns.iter().filter(|p| p.avg_quality > 0.7).count(),
        })
    }

    /// Run deep learning cycle (weekly)
    pub async fn deep_learn(&mut self) -> Result<DeepLearningResult, SonaError> {
        // Generate dreams
        let dreams = self.dream_engine.generate_dreams(50);

        // Evaluate with quantum exploration
        let quantum_samples: Vec<_> = dreams.iter()
            .filter_map(|dream| {
                let patterns = dream.to_patterns();
                if patterns.len() >= 2 {
                    let superposition = self.quantum_explorer.create_superposition(&patterns);
                    Some(self.quantum_explorer.sample_creative(&superposition, 3))
                } else {
                    None
                }
            })
            .flatten()
            .collect();

        // Evaluate dreams with Φ
        let mut integrated_dreams = Vec::new();
        for dream in &dreams {
            let phi = self.phi_evaluator.evaluate_dream(dream);
            if phi > self.phi_evaluator.threshold {
                integrated_dreams.push((dream.clone(), phi));
            }
        }

        // Integrate high-quality dreams
        for (dream, _phi) in &integrated_dreams {
            self.dream_engine.integrate_dream(dream);
        }

        // Update temporal patterns
        self.temporal_learner.detect_patterns();

        // Full EWC++ consolidation
        self.ewc.consolidate_all_tasks();

        Ok(DeepLearningResult {
            dreams_generated: dreams.len(),
            dreams_integrated: integrated_dreams.len(),
            quantum_samples: quantum_samples.len(),
        })
    }

    fn embed_query(&self, query: &str) -> Vec<f32> {
        // Query embedding implementation
        vec![0.0; 256]  // Placeholder
    }

    fn evaluate_quality(&self, response: &ResponseData) -> f32 {
        response.confidence
    }
}

#[derive(Default)]
pub struct IntegrationMetrics {
    pub queries_processed: u64,
    pub patterns_learned: u64,
    pub dreams_integrated: u64,
    pub avg_latency_ms: f64,
    pub avg_phi: f64,
}

---

Component Communication Protocol

/// Inter-component message types
pub enum SonaMessage {
    // Learning signals
    LearningSignal(LearningSignal),
    PatternDiscovered(LearnedPattern),
    DreamGenerated(Dream),

    // Coordination
    StartBackgroundLearning,
    StartDeepLearning,
    ConsolidateMemory,

    // Queries
    QueryPattern(Vec<f32>),
    QueryGraph(NodeId, usize),

    // Results
    PatternResult(Vec<LearnedPattern>),
    GraphResult(Vec<(NodeId, f32)>),
}

/// Message bus for component communication
pub struct SonaMessageBus {
    sender: broadcast::Sender<SonaMessage>,
    subscribers: HashMap<ComponentId, broadcast::Receiver<SonaMessage>>,
}

impl SonaMessageBus {
    pub fn subscribe(&mut self, component_id: ComponentId) -> broadcast::Receiver<SonaMessage> {
        self.sender.subscribe()
    }

    pub fn publish(&self, message: SonaMessage) {
        let _ = self.sender.send(message);
    }
}

---

Next Steps

1. 06-COMPONENTS.md - This document (Complete)
2. 07-IMPLEMENTATION.md - Implementation roadmap
3. 08-BENCHMARKS.md - Performance targets
4. 09-API-REFERENCE.md - Complete API documentation