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