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# 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