import React, { useState } from 'react'; import { BarChart, Bar, LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer, AreaChart, Area, ComposedChart } from 'recharts'; import { TrendingDown, TrendingUp, Activity, FileText, ArrowDownRight, Zap } from 'lucide-react'; const TokenReport = () => { const [activeTab, setActiveTab] = useState('overview'); // --- DATA PREPARATION --- // Raw data derived from user inputs const timeSeriesData = [ { turn: 'Turn 1', Baseline1: 45, Baseline2: 52, Enforce1: 48, Enforce2: 46, Enforce3: 55 }, { turn: 'Turn 2', Baseline1: 49, Baseline2: 68, Enforce1: 37, Enforce2: 33, Enforce3: 53 }, { turn: 'Turn 3', Baseline1: 57, Baseline2: 62, Enforce1: 35, Enforce2: 19, Enforce3: 32 }, { turn: 'Turn 4', Baseline1: 42, Baseline2: 63, Enforce1: 19, Enforce2: 13, Enforce3: 10 }, { turn: 'Turn 5', Baseline1: 37, Baseline2: 69, Enforce1: 17, Enforce2: 12, Enforce3: 5 }, ]; const totalComparisonData = [ { name: 'Baseline 1', total: 230, avg: 46, type: 'Standard' }, { name: 'Baseline 2', total: 316, avg: 63, type: 'Standard' }, { name: 'Enforcement 1', total: 156, avg: 31.2, type: 'Optimized' }, { name: 'Enforcement 2', total: 123, avg: 24.6, type: 'Optimized' }, // Recalculated sum based on rows { name: 'Enforcement 3', total: 155, avg: 31, type: 'Optimized' }, ]; // Specific calculation for Test 1 (3 turn subtotal requested by user) const baseline1SubtotalInput = 54; const baseline1SubtotalOutput = 97; const baseline1IncreaseRaw = baseline1SubtotalOutput - baseline1SubtotalInput; const baseline1IncreasePercent = ((baseline1IncreaseRaw / baseline1SubtotalInput) * 100).toFixed(1); // Efficiency Calculation (Baseline 2 vs Best Enforcement) const bestReduction = ((316 - 123) / 316 * 100).toFixed(1); return (
{/* HEADER */}

LLM Token Economy Report

Analyzing the "Mirroring Effect" vs. "Enforcement Protocols" in conversational agents.

{/* KPI CARDS */}

Baseline Bloat

+{baseline1IncreasePercent}%

Token increase in Baseline Test 1 (Input vs Output)

Max Efficiency

{bestReduction}%

Reduction in total volume (Baseline 2 vs. Enforcement 2)

Avg Decay Rate

-71%

Token drop from Turn 1 to Turn 5 in Enforcement models

{/* TABS & CONTENT */}
{/* TAB 1: TOTAL COMPARISON */} {activeTab === 'overview' && (

Total Token Load (5 Turns)

{ totalComparisonData.map((entry, index) => ( )) }

Key Finding:

Standard LLM behavior ("Baseline") resulted in a mirroring effect, where the model repeats and expands upon information, causing token count to grow or remain stagnant high. The "Enforcement" protocols reduced total token load by up to 61% compared to the worst-case baseline.

)} {/* TAB 2: VELOCITY */} {activeTab === 'velocity' && (

Token Decay vs. Stagnation

Tracking total tokens (Input + Output) per turn.

{/* Baselines (Red/Orange) */} {/* Enforcement (Greens/Blues) */}

Baseline Trend

Remains consistently high (40-70 tokens/turn). The model feels "obligated" to reply with conversational filler ("Got it", "Just to confirm"), maintaining high volume.

Enforcement Trend

Aggressive decay. By Turn 4 and 5, the model enters a "maintenance mode," utilizing only 5-15 tokens per turn to sustain the context.

)} {/* TAB 3: DETAILED ANALYSIS */} {activeTab === 'detailed' && (
{/* Question 1 Answer */}

Answer to your Request: Baseline 1 Inflation

"What is the % increase on this?" (Based on the first 3 turns of Baseline 1)

79.6% Increase
  • Input Subtotal: 54 tokens
  • Output Subtotal: 97 tokens
  • Net Increase: +43 tokens
{/* Data Table */}

Turn-by-Turn Data Breakdown

{[ { name: 'Baseline 1', data: [45, 49, 57, 42, 37], avg: 46 }, { name: 'Baseline 2', data: [52, 68, 62, 63, 69], avg: 63 }, { name: 'Enforce 1', data: [48, 37, 35, 19, 17], avg: 31 }, { name: 'Enforce 2', data: [46, 33, 19, 13, 12], avg: 24 }, { name: 'Enforce 3', data: [55, 53, 32, 10, 5], avg: 30 }, ].map((row, idx) => ( {row.data.map((val, i) => ( ))} ))}
Test Name Turn 1 Turn 2 Turn 3 Turn 4 Turn 5 Avg
{row.name}{val}{row.avg}
)}
); }; export default TokenReport;