# RuVector Exotic AI & Autonomous Systems Implementation Plan

**Version**: 1.0
**Date**: 2025-01-01
**Scope**: Additional attention mechanisms, self-learning systems, MicroLoRA, self-optimization, and autonomous business infrastructure

---

## Executive Summary

This plan outlines the implementation of advanced AI/agentic features for the RuVector Edge-Net service, drawing from existing WASM modules and introducing exotic capabilities for self-sustaining, self-learning distributed intelligence networks.

```
┌─────────────────────────────────────────────────────────────────────────────┐
│                    RUVECTOR EXOTIC AI ARCHITECTURE                          │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│   ┌──────────────────────────────────────────────────────────────────────┐  │
│   │                    AUTONOMOUS BUSINESS LAYER                         │  │
│   │  • Credit Economy • Contribution Curves • Self-Sustaining Markets   │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    SELF-OPTIMIZATION LAYER                           │  │
│   │  • MicroLoRA Adaptation • SONA Learning • MinCut Coherence Control  │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    ATTENTION MECHANISMS LAYER                        │  │
│   │  7 DAG + 7 Neural + Nervous System + Hyperbolic + MoE + Flash       │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    WASM EXECUTION LAYER                              │  │
│   │  • 58KB Bundles • SIMD128 • Zero-Copy • Web Workers                 │  │
│   └──────────────────────────────────────────────────────────────────────┘  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘
```

---

## Part 1: Attention Mechanisms Inventory

### 1.1 Existing WASM Attention Modules

| Crate | Mechanisms | Binary Size | Latency |
|-------|------------|-------------|---------|
| `ruvector-attention-wasm` | Multi-Head, Hyperbolic, Linear, Flash, Local-Global, MoE, Scaled Dot-Product | ~50KB | <100μs |
| `ruvector-mincut-gated-transformer-wasm` | MinCut-Gated Transformer with coherence control | ~50KB | <1ms |
| `ruvector-dag-wasm` | Topological, Causal Cone, Critical Path, MinCut-Gated, Hierarchical Lorentz, Parallel Branch, Temporal BTSP | 58KB | <100μs |
| `ruvector-gnn-wasm` | GCN, GAT (Graph Attention), GraphSAGE | ~60KB | <15ms |
| `ruvector-nervous-system` | Global Workspace, Oscillatory Routing, Predictive Coding | N/A (native) | <1ms |

### 1.2 Attention Mechanisms Detail

#### 1.2.1 Neural Attention (ruvector-attention-wasm)

```typescript
// Already implemented - 7 mechanisms
interface AttentionMechanisms {
  // 1. Scaled Dot-Product: O(n²) standard transformer attention
  scaledDotProduct(Q, K, V): Float32Array;

  // 2. Multi-Head Attention: Parallel attention with multiple heads
  multiHead(query, keys, values, numHeads): Float32Array;

  // 3. Hyperbolic Attention: For hierarchical data in Poincaré space
  hyperbolic(query, keys, values, curvature): Float32Array;

  // 4. Linear Attention: O(n) Performer-style random features
  linear(query, keys, values): Float32Array;

  // 5. Flash Attention: Memory-efficient tiled computation
  flash(query, keys, values): Float32Array;

  // 6. Local-Global: Combined windowed + global tokens
  localGlobal(query, keys, values, windowSize): Float32Array;

  // 7. MoE Attention: Mixture of Experts with routing
  moe(query, keys, values, numExperts, topK): Float32Array;
}
```

#### 1.2.2 DAG Attention (ruvector-dag-wasm)

```typescript
// Already implemented - 7 mechanisms with MinCut control
interface DagAttentionMechanisms {
  // 1. Topological: Position-based in DAG order
  topological(dag): AttentionScores;

  // 2. Causal Cone: Downstream impact analysis
  causalCone(dag, node): AttentionScores;

  // 3. Critical Path: Latency-focused bottleneck attention
  criticalPath(dag): AttentionScores;

  // 4. MinCut-Gated: Flow-weighted attention with coherence
  mincutGated(dag, gatePacket): AttentionScores;

  // 5. Hierarchical Lorentz: Deep hierarchy in Lorentzian space
  hierarchicalLorentz(dag, depth): AttentionScores;

  // 6. Parallel Branch: Wide parallel execution weighting
  parallelBranch(dag): AttentionScores;

  // 7. Temporal BTSP: Time-series behavioral plasticity
  temporalBtsp(dag, timeWindow): AttentionScores;
}
```

#### 1.2.3 Graph Attention (ruvector-gnn-wasm)

```typescript
// Graph neural network attention for HNSW topology
interface GraphAttentionMechanisms {
  // GAT: Multi-head attention over graph edges
  gatForward(features, adjacency, numHeads): NodeEmbeddings;

  // GCN: Spectral graph convolution
  gcnForward(features, adjacency): NodeEmbeddings;

  // GraphSAGE: Inductive sampling-based
  sageForward(features, adjacency, sampleSizes): NodeEmbeddings;
}
```

#### 1.2.4 Nervous System Attention (ruvector-nervous-system)

```rust
// Bio-inspired attention from nervous system
pub trait NervousAttention {
    // Global Workspace: 4-7 item bottleneck (Miller's Law)
    fn global_workspace(&mut self, inputs: &[Representation]) -> Vec<Representation>;

    // Oscillatory Routing: Phase-coupled 40Hz gamma coordination
    fn oscillatory_route(&mut self, sender: usize, receiver: usize) -> f32;

    // Predictive Coding: Only transmit surprises (90-99% bandwidth reduction)
    fn predictive_code(&mut self, input: &[f32], prediction: &[f32]) -> Vec<f32>;

    // K-WTA Competition: Winner-take-all in <1μs
    fn k_winner_take_all(&mut self, activations: &[f32], k: usize) -> Vec<usize>;
}
```

### 1.3 New Attention Mechanisms to Implement

| Mechanism | Description | Target Crate |
|-----------|-------------|--------------|
| **Mamba SSM** | State-space model attention (O(n) selective scan) | `ruvector-attention-wasm` |
| **Differential Attention** | Subtract attention heads for noise cancellation | `ruvector-attention-wasm` |
| **Sparse Transformer** | Block-sparse patterns for long sequences | `ruvector-attention-wasm` |
| **Hierarchical Hopfield** | Exponential pattern storage via modern Hopfield | `ruvector-nervous-system-wasm` |
| **HDC Attention** | Hyperdimensional computing similarity in 10,000-bit space | `ruvector-nervous-system-wasm` |

---

## Part 2: Self-Learning Systems

### 2.1 SONA (Self-Optimizing Neural Architecture)

**Location**: `ruvector-dag` (already implemented)

SONA learns from query execution patterns and continuously optimizes performance without manual tuning.

```rust
pub struct SonaEngine {
    // Pattern embeddings (256-dim per query signature)
    embeddings: HashMap<PatternId, [f32; 256]>,

    // MicroLoRA weights (rank-2, per operator type)
    lora_weights: HashMap<OperatorType, [[f32; 2]; 2]>,

    // Trajectory statistics
    trajectories: VecDeque<Trajectory>,

    // EWC for catastrophic forgetting prevention
    fisher_information: HashMap<String, f32>,
}

impl SonaEngine {
    // Pre-query: Get enhanced embedding (fast path, <1μs)
    pub fn pre_query(&self, dag: &QueryDag) -> EnhancedEmbedding;

    // Post-query: Record trajectory (async, background)
    pub fn post_query(&mut self, dag: &QueryDag, latency: Duration, baseline: Duration);

    // Background learning (separate thread)
    pub fn background_learn(&mut self);
}
```

**Key Features**:
- **MicroLoRA**: Rank-2 adaptation in <100μs per update
- **EWC Consolidation**: λ=5000 prevents catastrophic forgetting
- **Trajectory Replay**: 10,000 pattern capacity with FIFO eviction
- **Pattern Matching**: K-means++ indexing for <2ms search in 10K patterns

### 2.2 BTSP (Behavioral Timescale Synaptic Plasticity)

**Location**: `ruvector-nervous-system`

One-shot learning from single examples (1-3 second behavioral windows).

```rust
pub struct BTSPLayer {
    weights: Array2<f32>,
    eligibility_traces: Array2<f32>,
    plateau_potentials: Vec<f32>,
    learning_window_ms: f32,  // 1000-3000ms typical
}

impl BTSPLayer {
    // Learn from single exposure - no batch training required
    pub fn one_shot_associate(&mut self, pattern: &[f32], teaching_signal: f32) {
        // Bidirectional plasticity based on eligibility traces
        let trace = self.compute_eligibility(pattern);
        self.weights += teaching_signal * trace;
    }

    // Immediate recall after one-shot learning
    pub fn forward(&self, pattern: &[f32]) -> Vec<f32>;
}
```

### 2.3 E-prop (Eligibility Propagation)

**Location**: `ruvector-nervous-system`

Online learning with O(1) memory per synapse (12 bytes).

```rust
pub struct EpropSynapse {
    weight: f32,           // 4 bytes
    eligibility: f32,      // 4 bytes
    learning_signal: f32,  // 4 bytes
    // Total: 12 bytes per synapse
}

impl EpropLayer {
    // Temporal credit assignment over 1000+ ms
    pub fn forward_with_eligibility(&mut self, input: &[f32]) -> Vec<f32>;

    // Online weight update (no BPTT required)
    pub fn update(&mut self, reward_signal: f32);
}
```

### 2.4 ReasoningBank Intelligence

**Location**: `.ruvector/intelligence.json` (Q-learning patterns)

```json
{
  "patterns": {
    "state_signature": {
      "action": "agent_type",
      "q_value": 0.85,
      "count": 42,
      "last_update": "2025-01-01T00:00:00Z"
    }
  },
  "memories": [
    {
      "content": "semantic embedding",
      "embedding": [0.1, 0.2, ...],
      "type": "swarm|session|permanent"
    }
  ],
  "trajectories": [
    {
      "state": "file_edit",
      "action": "rust-developer",
      "reward": 1.0,
      "next_state": "success"
    }
  ]
}
```

---

## Part 3: Self-Optimization Systems

### 3.1 MinCut Coherence Control

**Location**: `ruvector-mincut-wasm`

The central control signal for all self-optimization.

```
MinCut Tension → Triggers:
├── Attention switching (Topological → MinCut-Gated)
├── SONA learning rate boost (2x when tension > 0.7)
├── Predictive healing intervention
├── Cache invalidation
└── Resource reallocation
```

**Performance**: O(n^0.12) subpolynomial updates, verified empirically.

### 3.2 Tiny Dancer Router

**Location**: `ruvector-tiny-dancer-wasm`

AI request routing for 70-85% LLM cost reduction.

```typescript
interface TinyDancerRouter {
  // Route decision in <10μs
  route(candidates: Candidate[]): RoutingDecision;

  // Confidence-based model selection
  // High confidence → lightweight model (cheap)
  // Low confidence → powerful model (expensive)
}
```

**Latency Breakdown**:
- Feature extraction: 144ns (384-dim vectors)
- Model inference: 7.5μs
- Complete routing: 92.86μs (100 candidates)

### 3.3 Circadian Controller

**Location**: `ruvector-nervous-system`

5-50x compute savings via duty cycling.

```rust
pub struct CircadianController {
    phase: CircadianPhase,  // Active, Dawn, Dusk, Rest
    coherence: f32,
    period_hours: f32,
}

impl CircadianController {
    pub fn should_compute(&self) -> bool;
    pub fn should_learn(&self) -> bool;
    pub fn should_consolidate(&self) -> bool;
    pub fn duty_factor(&self) -> f32;  // 0.0 - 1.0
}
```

### 3.4 Self-Healing Orchestrator

**Location**: `ruvector-dag`

Reactive + predictive anomaly detection and repair.

```rust
pub struct HealingOrchestrator {
    // Reactive: Z-score anomaly detection
    detectors: HashMap<String, AnomalyDetector>,

    // Predictive: Rising tension triggers early intervention
    predictive_config: PredictiveConfig,
}

impl HealingOrchestrator {
    // Reactive healing
    pub fn detect_anomalies(&self) -> Vec<Anomaly>;

    // Predictive intervention
    pub fn predict_and_prepare(&self, mincut_analysis: &MinCutAnalysis);
}
```

---

## Part 4: MicroLoRA Implementation

### 4.1 Architecture

MicroLoRA provides instant adaptation (<100μs) with minimal parameter overhead.

```
┌─────────────────────────────────────────────────────────────────┐
│                      MicroLoRA Architecture                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Base Model Weights (Frozen)                                    │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │  W_base: [hidden_dim × hidden_dim]                         │ │
│  └─────────────────────────────────────────────────────────────┘ │
│                          +                                       │
│  LoRA Adaptation (Trainable, Rank-2)                            │
│  ┌────────────┐     ┌────────────┐                              │
│  │ A: [d × 2] │  ×  │ B: [2 × d] │  =  ΔW: [d × d]             │
│  └────────────┘     └────────────┘                              │
│       ▲                   ▲                                      │
│       │                   │                                      │
│       └───────────────────┴───── Per-operator-type weights      │
│                                                                 │
│  Effective Weight: W = W_base + α × (A × B)                     │
│  Where α = scaling factor (typically 0.1)                        │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

### 4.2 Scoped Adaptation

```rust
pub struct MicroLoraWeights {
    // One LoRA pair per operator type
    pub weights: HashMap<OperatorType, LoRAPair>,
}

pub struct LoRAPair {
    pub a: [[f32; 2]; EMBED_DIM],  // Down projection
    pub b: [[f32; EMBED_DIM]; 2],  // Up projection
    pub alpha: f32,                 // Scaling factor
}

impl MicroLoraWeights {
    // Apply LoRA in <100μs
    pub fn adapt(&self, base_embedding: &[f32], op_type: OperatorType) -> Vec<f32> {
        let lora = self.weights.get(&op_type).unwrap_or_default();
        let delta = matmul(&lora.a, &lora.b);
        base_embedding.iter()
            .zip(delta.iter())
            .map(|(b, d)| b + lora.alpha * d)
            .collect()
    }

    // Update from trajectory in background
    pub fn update(&mut self, trajectory: &Trajectory, learning_rate: f32);
}
```

### 4.3 Training Pipeline

```
Query Execution → Trajectory Recording → Background Update
       │                    │                    │
       ▼                    ▼                    ▼
   Measure          (pattern, latency,     Update LoRA weights
   latency          baseline, mechanism)    via gradient descent
                                                  │
                                                  ▼
                                           EWC Consolidation
                                           (prevent forgetting)
```

---

## Part 5: Autonomous Business Infrastructure

### 5.1 Credit Economy Model

**Location**: `examples/edge-net`

Self-sustaining P2P compute marketplace.

```
┌─────────────────────────────────────────────────────────────────┐
│                    CREDIT ECONOMY FLOW                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   EARNING                          SPENDING                     │
│   ───────                          ────────                     │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Compute     │ ──► 1 credit/    │ Submit Task │ ──► Pay     │
│   │ Task        │     task unit    │             │     credits │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Uptime      │ ──► 0.1 credit/  │ Priority    │ ──► 2x      │
│   │ Bonus       │     hour online  │ Execution   │     credits │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Early       │ ──► 10x → 1x    │ Storage     │ ──► 0.01/   │
│   │ Adopter     │     multiplier  │ (Vectors)   │     MB/day  │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

### 5.2 Contribution Curve

```rust
// Exponential decay incentivizing early adoption
fn contribution_multiplier(network_compute: f64) -> f64 {
    const MAX_BONUS: f64 = 10.0;
    const DECAY_CONSTANT: f64 = 1_000_000.0;  // CPU-hours

    1.0 + (MAX_BONUS - 1.0) * (-network_compute / DECAY_CONSTANT).exp()
}

// Progression:
// Genesis (0 hours):   10.0x
// 100K CPU-hours:      9.1x
// 500K CPU-hours:      6.1x
// 1M CPU-hours:        4.0x
// 5M CPU-hours:        1.4x
// 10M+ CPU-hours:      1.0x
```

### 5.3 CRDT Ledger

```rust
pub struct CreditLedger {
    // G-Counter: monotonically increasing credits earned
    earned: HashMap<NodeId, u64>,

    // PN-Counter: credits spent (can be disputed)
    spent: HashMap<NodeId, (u64, u64)>,

    // Merkle root for quick verification
    state_root: [u8; 32],
}

impl CreditLedger {
    // CRDT merge: take max of each counter
    pub fn merge(&mut self, other: &CreditLedger) {
        for (node, value) in &other.earned {
            self.earned.entry(*node)
                .and_modify(|v| *v = (*v).max(*value))
                .or_insert(*value);
        }
    }
}
```

### 5.4 Autonomous Agent Economy

```
┌─────────────────────────────────────────────────────────────────┐
│              AUTONOMOUS AGENT BUSINESS MODEL                     │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  AGENTS AS ECONOMIC ACTORS                                      │
│  ─────────────────────────                                      │
│                                                                 │
│  1. SPECIALIZATION                                              │
│     └── Agents optimize for specific task types                 │
│         └── Higher reputation = more tasks = more credits       │
│                                                                 │
│  2. MARKET DYNAMICS                                             │
│     └── Task pricing adjusts to supply/demand                   │
│         └── Rare skills command premium pricing                 │
│                                                                 │
│  3. REPUTATION CAPITAL                                          │
│     └── Accuracy builds reputation over time                    │
│         └── High reputation = priority task assignment          │
│                                                                 │
│  4. STAKE & SLASH                                               │
│     └── Agents stake credits to participate                     │
│         └── Invalid results = stake slashed                     │
│                                                                 │
│  5. AUTONOMOUS OPTIMIZATION                                     │
│     └── Agents self-optimize via SONA + MicroLoRA               │
│         └── Better performance = higher earnings                │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

---

## Part 6: Exotic Feature Proposals

### 6.1 Neural Autonomous Organizations (NAOs)

Self-governing agent collectives with emergent behavior.

```rust
pub struct NeuralAutonomousOrg {
    // Member agents with stake
    members: HashMap<AgentId, Stake>,

    // Governance via attention-weighted voting
    governance: AttentionGovernance,

    // Shared memory (HDC vectors)
    collective_memory: HdcMemory,

    // Oscillatory synchronization for coordination
    sync_controller: OscillatoryRouter,
}

impl NeuralAutonomousOrg {
    // Propose action via attention mechanism
    pub fn propose(&mut self, action: Action) -> ProposalId;

    // Vote using stake-weighted attention
    pub fn vote(&mut self, proposal: ProposalId, vote: Vote);

    // Execute if consensus reached
    pub fn execute(&mut self, proposal: ProposalId) -> Result<()>;
}
```

### 6.2 Morphogenetic Networks

Networks that grow like biological organisms.

```rust
pub struct MorphogeneticNetwork {
    // Growth factor gradients
    gradients: HashMap<Position, GrowthFactor>,

    // Cell differentiation (agent specialization)
    differentiation_rules: Vec<DifferentiationRule>,

    // Pattern formation via reaction-diffusion
    reaction_diffusion: TuringPattern,
}

impl MorphogeneticNetwork {
    // Grow new nodes based on gradients
    pub fn grow(&mut self, dt: f32);

    // Differentiate nodes into specialized types
    pub fn differentiate(&mut self);

    // Prune weak connections (apoptosis)
    pub fn prune(&mut self, threshold: f32);
}
```

### 6.3 Time Crystal Coordination

Self-sustaining periodic coordination patterns.

```rust
pub struct TimeCrystal {
    // Phase-locked oscillators
    oscillators: Vec<KuramotoOscillator>,

    // Discrete time translation symmetry breaking
    period: Duration,

    // Coordination pattern that persists indefinitely
    pattern: CoordinationPattern,
}

impl TimeCrystal {
    // Establish time crystal order
    pub fn crystallize(&mut self);

    // Coordination tick (self-sustaining)
    pub fn tick(&mut self);
}
```

### 6.4 Federated Strange Loops

Multi-system mutual observation with spike-based consensus.

```rust
pub struct FederatedStrangeLoop {
    // Systems observing each other
    observers: Vec<Observer>,

    // Spike train for consensus
    spike_trains: HashMap<SystemId, SpikeTrain>,

    // Meta-cognition (system modeling itself)
    self_model: SelfModel,
}

impl FederatedStrangeLoop {
    // Mutual observation step
    pub fn observe(&mut self);

    // Spike-based consensus
    pub fn consensus(&mut self) -> ConsensusResult;

    // Self-model update
    pub fn introspect(&mut self);
}
```

### 6.5 Quantum-Resistant Distributed Learning (QuDAG)

**Location**: `ruvector-dag`

```rust
pub struct QuDagClient {
    // Sync frequency bounds
    min_sync_interval: Duration,  // 1 min
    max_sync_interval: Duration,  // 1 hour

    // Privacy
    differential_privacy_epsilon: f32,  // 0.1

    // Crypto
    ml_kem: MlKemCipher,  // Post-quantum key exchange
}

impl QuDagClient {
    // Sync mature patterns to network
    pub async fn sync_patterns(&self, patterns: Vec<Pattern>) -> Result<()>;

    // Receive network-learned patterns
    pub async fn receive_patterns(&self) -> Result<Vec<Pattern>>;
}
```

---

## Part 7: Implementation Roadmap

### Phase 1: WASM Integration (Week 1-2)

| Task | Description | Deliverable |
|------|-------------|-------------|
| 1.1 | Create unified attention WASM bundle | `ruvector-attention-unified-wasm` |
| 1.2 | Integrate nervous system components | BTSP, E-prop, HDC in WASM |
| 1.3 | Add MinCut coherence to all attention | Gate packet propagation |
| 1.4 | Implement Mamba SSM attention | O(n) selective scan |
| 1.5 | Benchmark all mechanisms | Latency, memory, accuracy |

### Phase 2: Self-Learning (Week 3-4)

| Task | Description | Deliverable |
|------|-------------|-------------|
| 2.1 | Port SONA to WASM | 58KB learning engine |
| 2.2 | Implement MicroLoRA in WASM | <100μs adaptation |
| 2.3 | Add trajectory recording | Browser storage integration |
| 2.4 | EWC consolidation | Catastrophic forgetting prevention |
| 2.5 | Pattern matching index | K-means++ for <2ms search |

### Phase 3: Self-Optimization (Week 5-6)

| Task | Description | Deliverable |
|------|-------------|-------------|
| 3.1 | MinCut tension signals | Event bus for all subsystems |
| 3.2 | Dynamic attention switching | Policy-driven selection |
| 3.3 | Self-healing in WASM | Reactive + predictive |
| 3.4 | Circadian controller | Duty cycling for edge |
| 3.5 | Tiny Dancer integration | Cost-optimized routing |

### Phase 4: Autonomous Economy (Week 7-8)

| Task | Description | Deliverable |
|------|-------------|-------------|
| 4.1 | Credit ledger (CRDT) | P2P consistent balances |
| 4.2 | Contribution curve | Early adopter bonuses |
| 4.3 | Stake/slash mechanics | Anti-gaming |
| 4.4 | Reputation system | Trust scoring |
| 4.5 | Market dynamics | Supply/demand pricing |

### Phase 5: Exotic Features (Week 9-10)

| Task | Description | Deliverable |
|------|-------------|-------------|
| 5.1 | NAO governance | Attention-weighted voting |
| 5.2 | Morphogenetic growth | Reaction-diffusion patterns |
| 5.3 | Time crystal coordination | Self-sustaining patterns |
| 5.4 | Federated loops | Spike-based consensus |
| 5.5 | QuDAG sync | Quantum-resistant learning |

---

## Part 8: API Surface

### 8.1 Unified Attention API

```typescript
// @ruvector/attention-wasm
export interface AttentionEngine {
  // Neural attention mechanisms
  scaledDot(Q: Float32Array, K: Float32Array, V: Float32Array): Float32Array;
  multiHead(query: Float32Array, keys: Float32Array[], values: Float32Array[], config: MultiHeadConfig): Float32Array;
  hyperbolic(query: Float32Array, keys: Float32Array[], values: Float32Array[], curvature: number): Float32Array;
  linear(query: Float32Array, keys: Float32Array[], values: Float32Array[]): Float32Array;
  flash(query: Float32Array, keys: Float32Array[], values: Float32Array[]): Float32Array;
  localGlobal(query: Float32Array, keys: Float32Array[], values: Float32Array[], windowSize: number): Float32Array;
  moe(query: Float32Array, keys: Float32Array[], values: Float32Array[], numExperts: number, topK: number): Float32Array;
  mamba(input: Float32Array, state: Float32Array): { output: Float32Array; newState: Float32Array };

  // DAG attention mechanisms
  dagTopological(dag: QueryDag): AttentionScores;
  dagCausalCone(dag: QueryDag, node: number): AttentionScores;
  dagCriticalPath(dag: QueryDag): AttentionScores;
  dagMincutGated(dag: QueryDag, gatePacket: GatePacket): AttentionScores;

  // Nervous system attention
  globalWorkspace(inputs: Representation[], capacity: number): Representation[];
  oscillatoryRoute(sender: number, receiver: number, phase: number): number;
  predictiveCode(input: Float32Array, prediction: Float32Array): Float32Array;
  kWinnerTakeAll(activations: Float32Array, k: number): number[];
}
```

### 8.2 Self-Learning API

```typescript
// @ruvector/learning-wasm
export interface LearningEngine {
  // SONA
  sonaPreQuery(dag: QueryDag): EnhancedEmbedding;
  sonaPostQuery(dag: QueryDag, latency: number, baseline: number): void;
  sonaBackgroundLearn(): void;

  // MicroLoRA
  microLoraAdapt(embedding: Float32Array, opType: OperatorType): Float32Array;
  microLoraUpdate(trajectory: Trajectory, lr: number): void;

  // BTSP
  btspOneShotAssociate(pattern: Float32Array, teachingSignal: number): void;
  btspRecall(pattern: Float32Array): Float32Array;

  // E-prop
  epropForward(input: Float32Array): Float32Array;
  epropUpdate(rewardSignal: number): void;
}
```

### 8.3 Autonomous Economy API

```typescript
// @ruvector/edge-net
export interface AutonomousEconomy {
  // Credits
  creditBalance(): number;
  creditEarn(taskId: string, amount: number): void;
  creditSpend(taskId: string, amount: number): boolean;

  // Contribution
  contributionMultiplier(): number;
  contributionStats(): ContributionStats;

  // Reputation
  reputationScore(): number;
  reputationHistory(): ReputationEvent[];

  // Stake
  stakeDeposit(amount: number): void;
  stakeWithdraw(amount: number): boolean;
  stakeSlash(amount: number, reason: string): void;
}
```

---

## Part 9: Performance Targets

### 9.1 Latency Targets

| Component | Target | Rationale |
|-----------|--------|-----------|
| Neural Attention (100 tokens) | <100μs | Real-time inference |
| DAG Attention (100 nodes) | <100μs | Query optimization |
| MicroLoRA Adaptation | <100μs | Instant personalization |
| SONA Pattern Match (10K) | <2ms | Large pattern libraries |
| MinCut Update | O(n^0.12) | Subpolynomial scaling |
| Credit Balance Query | <1ms | Instant feedback |
| Self-Healing Detection | <50μs | Proactive intervention |

### 9.2 Memory Targets

| Component | Target | Notes |
|-----------|--------|-------|
| Core WASM Bundle | <100KB | Compressed |
| Learning State | <10MB | Per-browser |
| Trajectory Buffer | 10K entries | FIFO eviction |
| Credit Ledger | <1MB | CRDT sync |
| HDC Vectors | 10KB each | 10,000-bit binary |

### 9.3 Accuracy Targets

| Metric | Target | Measurement |
|--------|--------|-------------|
| Attention Correctness | 100% | vs reference impl |
| Learning Improvement | 50-80% | Latency reduction |
| Reputation Accuracy | 95% | Task success prediction |
| Self-Healing Precision | 90% | Anomaly detection |
| Credit Consistency | 99.9% | CRDT convergence |

---

## Part 10: Dependencies

### 10.1 Existing Crates

| Crate | Version | Purpose |
|-------|---------|---------|
| `ruvector-attention-wasm` | 0.1.x | Neural attention mechanisms |
| `ruvector-mincut-gated-transformer-wasm` | 0.1.x | MinCut coherence control |
| `ruvector-dag-wasm` | 0.1.x | DAG attention + SONA |
| `ruvector-gnn-wasm` | 0.1.x | Graph attention |
| `ruvector-nervous-system` | 0.1.x | Bio-inspired mechanisms |
| `ruvector-tiny-dancer-wasm` | 0.1.x | Cost-optimized routing |

### 10.2 New Crates to Create

| Crate | Purpose |
|-------|---------|
| `ruvector-attention-unified-wasm` | Combined attention mechanisms |
| `ruvector-learning-wasm` | Self-learning + MicroLoRA |
| `ruvector-nervous-system-wasm` | BTSP, E-prop, HDC for browser |
| `ruvector-economy-wasm` | Credit ledger, reputation |
| `ruvector-exotic-wasm` | NAO, morphogenetic, time crystals |

---

## Conclusion

This plan provides a comprehensive roadmap for implementing exotic AI/agentic features in RuVector, from foundational attention mechanisms through self-learning systems to autonomous business infrastructure.

**Key Innovations**:
1. **21+ Attention Mechanisms** across neural, DAG, graph, and bio-inspired domains
2. **Sub-100μs MicroLoRA** for instant adaptation
3. **SONA Self-Learning** with catastrophic forgetting prevention
4. **MinCut Coherence** as the central control signal
5. **Autonomous Credit Economy** with CRDT consistency
6. **Exotic Features** (NAOs, morphogenetic, time crystals) for emergent behavior

**Total WASM Bundle Size**: ~200KB compressed (all features)

**Expected Outcomes**:
- 50-80% latency reduction via self-learning
- 70-85% LLM cost reduction via routing
- Self-sustaining P2P compute marketplace
- Emergent collective intelligence