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