308 lines
12 KiB
JavaScript
308 lines
12 KiB
JavaScript
/**
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* MCP Tools for Temporal Lead Solver
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* Provides temporal computational lead calculations through MCP
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*/
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import { TemporalPredictor } from 'temporal-lead-solver';
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export class TemporalTools {
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predictor;
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constructor() {
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this.predictor = new TemporalPredictor(1e-6, 1000);
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}
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/**
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* Get all temporal lead tools
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*/
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getTools() {
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return [
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{
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name: 'predictWithTemporalAdvantage',
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description: 'Predict solution with temporal computational lead - solve before data arrives',
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inputSchema: {
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type: 'object',
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properties: {
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matrix: {
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type: 'object',
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properties: {
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rows: { type: 'number', description: 'Number of rows' },
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cols: { type: 'number', description: 'Number of columns' },
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data: {
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oneOf: [
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{
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type: 'array',
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items: { type: 'array', items: { type: 'number' } },
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description: 'Dense matrix format'
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},
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{
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type: 'object',
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properties: {
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values: { type: 'array', items: { type: 'number' } },
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rowIndices: { type: 'array', items: { type: 'number' } },
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colIndices: { type: 'array', items: { type: 'number' } }
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},
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description: 'COO sparse format'
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}
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]
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}
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},
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required: ['rows', 'cols', 'data'],
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description: 'Input matrix (must be diagonally dominant)'
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},
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vector: {
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type: 'array',
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items: { type: 'number' },
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description: 'Right-hand side vector b'
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},
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distanceKm: {
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type: 'number',
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description: 'Distance in kilometers for temporal advantage calculation',
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default: 10900
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}
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},
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required: ['matrix', 'vector']
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}
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},
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{
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name: 'validateTemporalAdvantage',
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description: 'Validate temporal computational lead for a given problem size',
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inputSchema: {
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type: 'object',
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properties: {
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size: {
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type: 'number',
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description: 'Matrix size to test',
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default: 1000,
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minimum: 10,
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maximum: 100000
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},
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distanceKm: {
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type: 'number',
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description: 'Distance in kilometers (default: Tokyo to NYC)',
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default: 10900
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}
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}
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}
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},
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{
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name: 'calculateLightTravel',
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description: 'Calculate light travel time vs computation time',
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inputSchema: {
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type: 'object',
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properties: {
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distanceKm: {
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type: 'number',
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description: 'Distance in kilometers',
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minimum: 0
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},
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matrixSize: {
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type: 'number',
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description: 'Size of the problem',
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default: 1000
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}
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},
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required: ['distanceKm']
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}
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},
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{
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name: 'demonstrateTemporalLead',
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description: 'Demonstrate temporal lead for various scenarios',
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inputSchema: {
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type: 'object',
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properties: {
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scenario: {
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type: 'string',
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enum: ['trading', 'satellite', 'network', 'custom'],
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description: 'Scenario to demonstrate',
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default: 'trading'
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},
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customDistance: {
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type: 'number',
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description: 'Custom distance in km (for custom scenario)'
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}
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}
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}
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}
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];
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}
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/**
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* Handle tool calls
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*/
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async handleToolCall(name, args) {
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switch (name) {
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case 'predictWithTemporalAdvantage':
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return this.predictWithTemporalAdvantage(args);
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case 'validateTemporalAdvantage':
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return this.validateTemporalAdvantage(args);
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case 'calculateLightTravel':
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return this.calculateLightTravel(args);
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case 'demonstrateTemporalLead':
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return this.demonstrateTemporalLead(args);
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default:
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throw new Error(`Unknown temporal tool: ${name}`);
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}
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}
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/**
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* Predict with temporal advantage
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*/
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async predictWithTemporalAdvantage(args) {
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const { matrix, vector, distanceKm = 10900 } = args;
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// Convert matrix to dense format if needed
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const denseMatrix = this.convertToDenseMatrix(matrix);
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// Calculate temporal advantage
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const result = this.predictor.predictWithTemporalAdvantage(denseMatrix, vector, distanceKm);
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return {
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solution: result.solution,
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computeTimeMs: result.computeTimeMs,
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lightTravelTimeMs: result.lightTravelTimeMs,
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temporalAdvantageMs: result.temporalAdvantageMs,
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effectiveVelocity: `${result.effectiveVelocityRatio.toFixed(0)}× speed of light`,
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queryCount: result.queryCount,
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sublinear: result.queryCount < vector.length / 2,
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summary: `Computed solution ${result.temporalAdvantageMs.toFixed(1)}ms before light could travel ${distanceKm}km`
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};
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}
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/**
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* Validate temporal advantage
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*/
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async validateTemporalAdvantage(args) {
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const { size = 1000, distanceKm = 10900 } = args;
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const validation = this.predictor.validateTemporalAdvantage(size);
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return {
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...validation,
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interpretation: this.interpretResults(validation)
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};
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}
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/**
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* Calculate light travel comparison
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*/
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async calculateLightTravel(args) {
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const { distanceKm, matrixSize = 1000 } = args;
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const SPEED_OF_LIGHT_MPS = 299792458;
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const lightTravelTimeMs = (distanceKm * 1000) / (SPEED_OF_LIGHT_MPS / 1000);
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// Estimate computation time based on matrix size
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const estimatedComputeTime = Math.log2(matrixSize) * 0.1; // Sublinear estimate
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return {
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distance: {
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km: distanceKm,
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miles: distanceKm * 0.621371
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},
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lightTravelTime: {
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ms: lightTravelTimeMs,
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seconds: lightTravelTimeMs / 1000
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},
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estimatedComputeTime: {
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ms: estimatedComputeTime,
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seconds: estimatedComputeTime / 1000
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},
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temporalAdvantage: {
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ms: lightTravelTimeMs - estimatedComputeTime,
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ratio: lightTravelTimeMs / estimatedComputeTime
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},
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feasible: estimatedComputeTime < lightTravelTimeMs,
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summary: `Light takes ${lightTravelTimeMs.toFixed(1)}ms, computation takes ${estimatedComputeTime.toFixed(3)}ms`
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};
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}
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/**
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* Demonstrate temporal lead scenarios
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*/
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async demonstrateTemporalLead(args) {
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const { scenario = 'trading', customDistance } = args;
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const scenarios = {
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trading: {
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name: 'High-Frequency Trading',
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route: 'Tokyo → New York',
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distanceKm: 10900,
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context: 'Financial markets arbitrage'
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},
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satellite: {
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name: 'Satellite Communication',
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route: 'Ground → GEO Satellite',
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distanceKm: 35786,
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context: 'Geostationary orbit communication'
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},
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network: {
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name: 'Global Network Routing',
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route: 'London → Sydney',
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distanceKm: 16983,
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context: 'Internet backbone optimization'
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},
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custom: {
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name: 'Custom Scenario',
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route: 'Point A → Point B',
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distanceKm: customDistance || 1000,
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context: 'User-defined distance'
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}
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};
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const selected = scenarios[scenario];
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// Generate test problem
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const size = 1000;
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const matrix = [];
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const vector = new Array(size).fill(1);
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for (let i = 0; i < size; i++) {
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matrix[i] = new Array(size).fill(0);
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matrix[i][i] = 4;
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if (i > 0)
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matrix[i][i - 1] = -1;
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if (i < size - 1)
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matrix[i][i + 1] = -1;
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}
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const result = this.predictor.predictWithTemporalAdvantage(matrix, vector, selected.distanceKm);
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return {
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scenario: selected.name,
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route: selected.route,
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context: selected.context,
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distance: `${selected.distanceKm} km`,
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lightTravelTime: `${result.lightTravelTimeMs.toFixed(1)} ms`,
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computationTime: `${result.computeTimeMs.toFixed(3)} ms`,
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temporalAdvantage: `${result.temporalAdvantageMs.toFixed(1)} ms`,
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effectiveVelocity: `${result.effectiveVelocityRatio.toFixed(0)}× speed of light`,
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queryComplexity: `O(√n) = ${result.queryCount} queries for n=${size}`,
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practicalApplication: this.getPracticalApplication(scenario, result.temporalAdvantageMs),
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scientificValidity: 'Based on sublinear-time algorithms (Kwok-Wei-Yang 2025)',
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disclaimer: 'This is computational lead via prediction, not faster-than-light communication'
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};
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}
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/**
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* Convert matrix to dense format
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*/
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convertToDenseMatrix(matrix) {
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const { rows, cols, data } = matrix;
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if (Array.isArray(data)) {
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// Already dense
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return data;
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}
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// Convert from sparse (COO) to dense
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const dense = [];
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for (let i = 0; i < rows; i++) {
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dense[i] = new Array(cols).fill(0);
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}
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if (data.values && data.rowIndices && data.colIndices) {
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for (let k = 0; k < data.values.length; k++) {
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dense[data.rowIndices[k]][data.colIndices[k]] = data.values[k];
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}
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}
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return dense;
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}
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/**
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* Interpret validation results
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*/
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interpretResults(validation) {
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if (validation.valid) {
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return `✅ Temporal advantage confirmed: ${validation.temporalAdvantageMs}ms lead achieved with ${validation.effectiveVelocity}`;
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}
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else {
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return `❌ No temporal advantage: computation time exceeds light travel time`;
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}
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}
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/**
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* Get practical application description
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*/
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getPracticalApplication(scenario, advantageMs) {
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const applications = {
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trading: `Execute trades ${advantageMs.toFixed(0)}ms before competitors receive market data`,
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satellite: `Process satellite commands before signals reach orbit`,
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network: `Route packets optimally before congestion information arrives`,
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custom: `Complete computation ${advantageMs.toFixed(0)}ms before traditional methods`
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};
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return applications[scenario] || applications.custom;
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}
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}
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