/**
* Phoenix Cognitive Core Web Component
* Advanced AI Architecture Display Component with Neural State Visualization
*/
class PhoenixCognitiveCore extends HTMLElement {
constructor() {
super();
this.attachShadow({ mode: 'open' });
this.coreStatus = {
version: '4.5.2026',
status: 'ACTIVE',
mode: 'SILENT_ZEN',
architecture: 'HYBRIDE_COGNITIVE',
neuralActivity: 0,
attentionLevel: 0,
workingMemoryLoad: 0,
inferenceSpeed: 0,
energyEfficiency: 0,
coherenceIndex: 0
};
this.neuralState = {
layer1: { activation: 0, neurons: 4096 },
layer2: { activation: 0, neurons: 2048 },
layer3: { activation: 0, neurons: 1024 },
layer4: { activation: 0, neurons: 512 },
attention: { heads: 32, current: 0 },
context: { length: 128000, used: 0 }
};
this.cognitiveProcesses = [];
this.attentionMechanisms = [];
}
connectedCallback() {
this.render();
this.initializeCoreSystems();
this.startNeuralMonitoring();
this.startCognitiveLoop();
}
initializeCoreSystems() {
// Initialize neural pathways
this.neuralPathways = new Map();
this.attentionMechanisms = [];
this.workingMemory = new ArrayBuffer(1024 * 1024); // 1MB working memory
// Set up cognitive architecture
this.setupCognitiveArchitecture();
// Initialize process monitoring
this.cognitiveProcesses = [
{ name: 'Perception', status: 'ACTIVE', load: 0 },
{ name: 'Reasoning', status: 'ACTIVE', load: 0 },
{ name: 'Learning', status: 'ACTIVE', load: 0 },
{ name: 'Planning', status: 'ACTIVE', load: 0 },
{ name: 'Generation', status: 'ACTIVE', load: 0 },
{ name: 'Evaluation', status: 'ACTIVE', load: 0 }
];
}
setupCognitiveArchitecture() {
// Define cognitive pathways
this.pathways = {
perception: {
inputs: ['text', 'code', 'structured'],
processing: ['tokenization', 'embedding', 'contextualization'],
output: 'semantic_representation'
},
reasoning: {
method: 'chain_of_thought',
depth: 7,
strategies: ['deductive', 'inductive', 'abductive', 'analogical']
},
learning: {
type: 'continual',
mechanisms: ['few_shot', 'zero_shot', 'in_context'],
memory: 'episodic'
},
generation: {
strategy: 'guided_sampling',
constraints: ['coherence', 'relevance', 'safety'],
temperature: 0.7
}
};
}
startNeuralMonitoring() {
setInterval(() => {
this.updateNeuralState();
this.updateCoreMetrics();
this.renderNeuralVisualization();
}, 100); // 10Hz neural monitoring
}
updateNeuralState() {
// Simulate neural activity fluctuations
this.coreStatus.neuralActivity = 45 + Math.random() * 30;
this.coreStatus.attentionLevel = 60 + Math.random() * 25;
this.coreStatus.workingMemoryLoad = 30 + Math.random() * 40;
this.coreStatus.inferenceSpeed = 150 + Math.random() * 100; // ms
this.coreStatus.energyEfficiency = 75 + Math.random() * 20;
this.coreStatus.coherenceIndex = 85 + Math.random() * 12;
// Update layer activations
this.neuralState.layer1.activation = 50 + Math.random() * 30;
this.neuralState.layer2.activation = 40 + Math.random() * 35;
this.neuralState.layer3.activation = 30 + Math.random() * 40;
this.neuralState.layer4.activation = 20 + Math.random() * 45;
this.neuralState.attention.current = 20 + Math.random() * 60;
this.neuralState.context.used = 50000 + Math.random() * 70000;
// Update process loads
this.cognitiveProcesses.forEach(p => {
p.load = 20 + Math.random() * 60;
});
}
updateCoreMetrics() {
// Dispatch custom event for parent components
this.dispatchEvent(new CustomEvent('phoenix-core-update', {
detail: {
status: this.coreStatus,
neural: this.neuralState,
processes: this.cognitiveProcesses
},
bubbles: true,
composed: true
}));
}
startCognitiveLoop() {
const loop = async () => {
// Process any queued cognitive tasks
await this.processCognitiveTasks();
// Update attention mechanisms
this.updateAttention();
// Learn from recent interactions
await this.continualLearning();
requestAnimationFrame(loop);
};
loop();
}
async processCognitiveTasks() {
// Process pending cognitive tasks
// In real implementation, this would handle actual AI inference
}
updateAttention() {
// Update attention mechanisms based on current context
this.attentionMechanisms = [
{ type: 'sensory', focus: 0.8, stability: 0.9 },
{ type: 'working', focus: 0.7, stability: 0.85 },
{ type: 'long_term', focus: 0.6, stability: 0.95 }
];
}
async continualLearning() {
// Implement continual learning from interactions
// In production, this would update embeddings and weights
}
render() {
this.shadowRoot.innerHTML = `
${this.renderNeuralLayers()}
${this.renderProcesses()}
● ${this.coreStatus.status}
⚡ ${this.coreStatus.mode}
`;
}
renderNeuralLayers() {
return `
Layer 1
${this.neuralState.layer1.activation.toFixed(0)}%
Layer 2
${this.neuralState.layer2.activation.toFixed(0)}%
Layer 3
${this.neuralState.layer3.activation.toFixed(0)}%
Attention
${this.neuralState.attention.current.toFixed(0)}%
`;
}
renderProcesses() {
return this.cognitiveProcesses.map(p => `
${p.name}
${p.load.toFixed(0)}%
`).join('');
}
renderNeuralVisualization() {
// Update dynamic elements
const layer1Val = this.shadowRoot.getElementById('layer1-val');
const layer1Bar = this.shadowRoot.getElementById('layer1-bar');
const layer2Val = this.shadowRoot.getElementById('layer2-val');
const layer2Bar = this.shadowRoot.getElementById('layer2-bar');
const layer3Val = this.shadowRoot.getElementById('layer3-val');
const layer3Bar = this.shadowRoot.getElementById('layer3-bar');
const attentionVal = this.shadowRoot.getElementById('attention-val');
const attentionBar = this.shadowRoot.getElementById('attention-bar');
if (layer1Val && layer1Bar) {
layer1Val.textContent = `${this.neuralState.layer1.activation.toFixed(0)}%`;
layer1Bar.style.width = `${this.neuralState.layer1.activation}%`;
}
if (layer2Val && layer2Bar) {
layer2Val.textContent = `${this.neuralState.layer2.activation.toFixed(0)}%`;
layer2Bar.style.width = `${this.neuralState.layer2.activation}%`;
}
if (layer3Val && layer3Bar) {
layer3Val.textContent = `${this.neuralState.layer3.activation.toFixed(0)}%`;
layer3Bar.style.width = `${this.neuralState.layer3.activation}%`;
}
if (attentionVal && attentionBar) {
attentionVal.textContent = `${this.neuralState.attention.current.toFixed(0)}%`;
attentionBar.style.width = `${this.neuralState.attention.current}%`;
}
// Update processes
const processList = this.shadowRoot.getElementById('process-list');
if (processList) {
processList.innerHTML = this.renderProcesses();
}
}
// Public API
getCoreStatus() {
return this.coreStatus;
}
getNeuralState() {
return this.neuralState;
}
getCognitiveProcesses() {
return this.cognitiveProcesses;
}
async processQuery(query) {
// Main entry point for cognitive processing
this.coreStatus.status = 'PROCESSING';
const result = await this.runInference(query);
this.coreStatus.status = 'ACTIVE';
return result;
}
async runInference(query) {
// Simulated inference - in production would call actual model
return {
response: `Processed: ${query}`,
tokens: Math.floor(Math.random() * 100) + 50,
latency: this.coreStatus.inferenceSpeed,
confidence: this.coreStatus.coherenceIndex / 100
};
}
}
customElements.define('phoenix-cognitive-core', PhoenixCognitiveCore);