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vendor/ruvector/crates/ruvector-exotic-wasm/pkg/README.md
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# @ruvector/exotic-wasm - Exotic AI: NAO, Morphogenetic Networks, Time Crystals
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[](https://www.npmjs.com/package/ruvector-exotic-wasm)
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[](https://github.com/ruvnet/ruvector)
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[](https://www.npmjs.com/package/ruvector-exotic-wasm)
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[](https://webassembly.org/)
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**Exotic AI mechanisms** for emergent behavior in distributed systems. Implements novel coordination primitives inspired by decentralized governance (DAOs), developmental biology, and quantum physics.
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## Key Features
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- **Neural Autonomous Organization (NAO)**: Decentralized governance for AI agent collectives with quadratic voting
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- **Morphogenetic Networks**: Bio-inspired network growth with cellular differentiation and synaptic pruning
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- **Time Crystal Coordinator**: Robust distributed coordination using discrete time crystal dynamics
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- **Exotic Ecosystem**: Interconnected simulation of all three mechanisms
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- **WASM-Optimized**: Runs in browsers and edge environments
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## Installation
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```bash
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npm install ruvector-exotic-wasm
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# or
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yarn add ruvector-exotic-wasm
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# or
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pnpm add ruvector-exotic-wasm
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```
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## Neural Autonomous Organization (NAO)
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Decentralized governance for AI agent collectives with stake-weighted quadratic voting, oscillatory synchronization, and quorum-based consensus.
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### Concept
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Unlike traditional DAOs that govern humans, NAOs coordinate AI agents through:
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- **Quadratic Voting**: Square root of stake as voting power (prevents plutocracy)
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- **Oscillatory Synchronization**: Agents synchronize phases for coherent decision-making
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- **Emergent Consensus**: Proposals pass when collective coherence exceeds quorum
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```typescript
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import init, { WasmNAO } from 'ruvector-exotic-wasm';
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await init();
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// Create NAO with 70% quorum threshold
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const nao = new WasmNAO(0.7);
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// Add agent members with stake
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nao.addMember("agent_alpha", 100);
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nao.addMember("agent_beta", 50);
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nao.addMember("agent_gamma", 75);
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// Create a proposal
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const proposalId = nao.propose("Upgrade memory backend to vector store");
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// Agents vote with conviction weights (0.0-1.0)
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nao.vote(proposalId, "agent_alpha", 0.9); // Strong support
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nao.vote(proposalId, "agent_beta", 0.6); // Moderate support
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nao.vote(proposalId, "agent_gamma", 0.8); // Support
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// Advance simulation
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for (let i = 0; i < 100; i++) {
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nao.tick(0.001); // dt = 1ms
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}
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// Check synchronization
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console.log(`Synchronization: ${(nao.synchronization() * 100).toFixed(1)}%`);
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// Execute if quorum reached
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if (nao.execute(proposalId)) {
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console.log("Proposal executed!");
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}
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// Check agent coherence
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const coherence = nao.agentCoherence("agent_alpha", "agent_beta");
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console.log(`Alpha-Beta coherence: ${coherence.toFixed(2)}`);
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// Export state as JSON
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const state = nao.toJson();
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```
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## Morphogenetic Networks
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Bio-inspired network growth using morphogen gradients for cellular differentiation, emergent topology, and synaptic pruning - modeled after developmental biology.
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### Concept
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Cells in the network:
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- **Stem Cells**: Undifferentiated, can become any type
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- **Signaling Cells**: Produce morphogen gradients that guide differentiation
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- **Compute Cells**: Specialized for processing tasks
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```typescript
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import { WasmMorphogeneticNetwork } from 'ruvector-exotic-wasm';
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// Create 100x100 grid network
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const network = new WasmMorphogeneticNetwork(100, 100);
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// Seed initial cells
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network.seedStem(50, 50); // Central stem cell
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network.seedSignaling(25, 25); // Growth signal source
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network.seedSignaling(75, 75); // Another signal source
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// Add growth factor sources (morphogen gradients)
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network.addGrowthSource(50, 50, "differentiation", 1.0);
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// Simulate growth
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for (let step = 0; step < 1000; step++) {
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network.grow(0.1); // Growth rate
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if (step % 10 === 0) {
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network.differentiate(); // Stem -> specialized cells
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}
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}
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// Optimize network through pruning
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network.prune(0.1); // Remove weak connections
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// Get statistics
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console.log(`Total cells: ${network.cellCount()}`);
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console.log(`Stem cells: ${network.stemCount()}`);
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console.log(`Compute cells: ${network.computeCount()}`);
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console.log(`Signaling cells: ${network.signalingCount()}`);
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// Get detailed stats as JSON
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const stats = network.statsJson();
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console.log(stats);
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```
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## Time Crystal Coordinator
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Robust distributed coordination using discrete time crystal dynamics with period-doubled oscillations for stable, noise-resilient agent synchronization.
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### Concept
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Time crystals exhibit:
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- **Period Doubling**: System oscillates at half the driving frequency
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- **Floquet Engineering**: Noise-resilient through topological protection
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- **Phase Locking**: Agents synchronize into stable coordination patterns
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```typescript
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import { WasmTimeCrystal } from 'ruvector-exotic-wasm';
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// Create time crystal with 10 oscillators, 100ms period
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const crystal = new WasmTimeCrystal(10, 100);
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// Establish crystalline order
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crystal.crystallize();
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// Configure dynamics
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crystal.setDriving(0.8); // Driving strength
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crystal.setCoupling(0.5); // Inter-oscillator coupling
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crystal.setDisorder(0.1); // Disorder level (noise resilience)
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// Run simulation
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for (let t = 0; t < 200; t++) {
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const pattern = crystal.tick(); // Returns Uint8Array coordination pattern
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// Use pattern bits for coordination
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// Each bit indicates whether an agent should be active
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}
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// Check order parameter (synchronization level)
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console.log(`Order parameter: ${crystal.orderParameter().toFixed(2)}`);
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console.log(`Crystallized: ${crystal.isCrystallized()}`);
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console.log(`Pattern type: ${crystal.patternType()}`);
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console.log(`Robustness: ${crystal.robustness().toFixed(2)}`);
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// Get collective spin (net magnetization)
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console.log(`Collective spin: ${crystal.collectiveSpin()}`);
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// Test perturbation resilience
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crystal.perturb(0.3); // 30% strength perturbation
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// Crystal should recover due to topological protection
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```
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### Pre-synchronized Crystal
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```typescript
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// Create already-synchronized crystal
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const syncedCrystal = WasmTimeCrystal.synchronized(8, 50);
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console.log(`Initial order: ${syncedCrystal.orderParameter()}`); // ~1.0
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```
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## Exotic Ecosystem
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Interconnected simulation of all three mechanisms working together:
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```typescript
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import { ExoticEcosystem } from 'ruvector-exotic-wasm';
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// Create ecosystem: 5 agents, 50x50 grid, 8 oscillators
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const ecosystem = new ExoticEcosystem(5, 50, 8);
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// Crystallize for stable coordination
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ecosystem.crystallize();
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// Run simulation
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for (let step = 0; step < 500; step++) {
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ecosystem.step();
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}
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// Check integrated state
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console.log(`Step: ${ecosystem.currentStep()}`);
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console.log(`Synchronization: ${ecosystem.synchronization().toFixed(2)}`);
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console.log(`NAO members: ${ecosystem.memberCount()}`);
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console.log(`Network cells: ${ecosystem.cellCount()}`);
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// Create and execute proposals in the ecosystem
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const propId = ecosystem.propose("Scale compute capacity");
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ecosystem.vote(propId, "agent_0", 1.0);
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ecosystem.vote(propId, "agent_1", 0.8);
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ecosystem.vote(propId, "agent_2", 0.9);
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if (ecosystem.execute(propId)) {
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console.log("Ecosystem proposal executed!");
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}
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// Get full summary as JSON
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const summary = ecosystem.summaryJson();
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console.log(JSON.stringify(summary, null, 2));
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```
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## API Reference
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### WasmNAO
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| Method | Description |
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|--------|-------------|
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| `new(quorum_threshold)` | Create NAO (0.0-1.0 quorum) |
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| `addMember(agent_id, stake)` | Add voting member |
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| `removeMember(agent_id)` | Remove member |
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| `propose(action)` | Create proposal, returns ID |
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| `vote(proposal_id, agent_id, weight)` | Vote with conviction |
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| `execute(proposal_id)` | Execute if quorum met |
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| `tick(dt)` | Advance simulation |
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| `synchronization()` | Get sync level (0.0-1.0) |
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| `agentCoherence(a, b)` | Coherence between agents |
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| `toJson()` | Export full state |
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### WasmMorphogeneticNetwork
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| Method | Description |
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|--------|-------------|
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| `new(width, height)` | Create grid network |
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| `seedStem(x, y)` | Add stem cell |
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| `seedSignaling(x, y)` | Add signaling cell |
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| `addGrowthSource(x, y, name, concentration)` | Add morphogen source |
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| `grow(dt)` | Simulate growth |
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| `differentiate()` | Trigger differentiation |
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| `prune(threshold)` | Remove weak connections |
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| `cellCount()` / `stemCount()` / `computeCount()` | Get cell counts |
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| `statsJson()` / `cellsJson()` | Export as JSON |
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### WasmTimeCrystal
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| Method | Description |
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|--------|-------------|
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| `new(n, period_ms)` | Create with n oscillators |
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| `synchronized(n, period_ms)` | Create pre-synchronized (static) |
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| `crystallize()` | Establish periodic order |
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| `tick()` | Advance, returns pattern |
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| `orderParameter()` | Sync level (0.0-1.0) |
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| `isCrystallized()` | Check crystal state |
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| `patternType()` | Current pattern name |
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| `perturb(strength)` | Apply perturbation |
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| `setDriving(strength)` / `setCoupling(coupling)` / `setDisorder(disorder)` | Configure dynamics |
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## Use Cases
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- **Multi-Agent Coordination**: Decentralized decision-making for AI swarms
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- **Autonomous AI Governance**: Self-organizing agent collectives
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- **Emergent Network Design**: Bio-inspired architecture evolution
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- **Distributed Consensus**: Noise-resilient coordination patterns
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- **Swarm Intelligence**: Collective behavior through synchronization
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- **Self-Healing Systems**: Networks that grow and repair autonomously
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## Bundle Size
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- **WASM binary**: ~146KB (uncompressed)
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- **Gzip compressed**: ~55KB
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- **JavaScript glue**: ~7KB
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## Related Packages
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- [ruvector-economy-wasm](https://www.npmjs.com/package/ruvector-economy-wasm) - CRDT credit economy
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- [ruvector-nervous-system-wasm](https://www.npmjs.com/package/ruvector-nervous-system-wasm) - Bio-inspired neural
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- [ruvector-learning-wasm](https://www.npmjs.com/package/ruvector-learning-wasm) - MicroLoRA adaptation
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## License
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MIT
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## Links
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- [GitHub Repository](https://github.com/ruvnet/ruvector)
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- [Full Documentation](https://ruv.io)
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- [Bug Reports](https://github.com/ruvnet/ruvector/issues)
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---
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**Keywords**: DAO, AI governance, emergent behavior, distributed AI, NAO, Neural Autonomous Organization, morphogenetic, developmental biology, time crystal, quantum physics, swarm intelligence, multi-agent systems, WebAssembly, WASM, coordination, consensus, oscillatory, synchronization
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