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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>OLYMPUS Ensemble - ARC Prize 2025</title>
<style>
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</head>
<body>
<nav class="nav">
<div class="nav-content">
<h1>OLYMPUS Ensemble</h1>
<div class="nav-links">
<a href="#overview">Overview</a>
<a href="#models">Models</a>
<a href="#strategy">Strategy</a>
<a href="#performance">Performance</a>
</div>
</div>
</nav>
<div class="hero">
<h1>OLYMPUS Neural Ensemble</h1>
<p class="subtitle">Five specialized models working in concert to solve abstract reasoning challenges. Each model brings unique capabilities, creating a comprehensive solution for the ARC Prize 2025.</p>
</div>
<div class="container">
<section id="overview" class="section">
<h2>Architecture Overview</h2>
<div class="architecture-overview">
<p>The OLYMPUS ensemble represents a sophisticated approach to abstract reasoning, combining specialized neural architectures that each excel at different aspects of pattern recognition and transformation.</p>
<div class="performance-metrics">
<div class="metric-card">
<div class="metric-value">5</div>
<div class="metric-label">Specialized Models</div>
</div>
<div class="metric-card">
<div class="metric-value">256</div>
<div class="metric-label">Hidden Dimensions</div>
</div>
<div class="metric-card">
<div class="metric-value">30×30</div>
<div class="metric-label">Max Grid Size</div>
</div>
<div class="metric-card">
<div class="metric-value">10</div>
<div class="metric-label">Color Classes</div>
</div>
</div>
</div>
</section>
<section id="models" class="section">
<h2>Model Specifications</h2>
<div class="models-grid">
<div class="model-detail">
<div class="model-header">
<div class="model-icon">🧠</div>
<h4>MINERVA</h4>
</div>
<p class="model-type">Strategic Pattern Analysis</p>
<p>Advanced grid reasoning with attention mechanisms for understanding complex spatial relationships and transformations.</p>
<div class="architecture-specs">
<ul class="spec-list">
<li>
<span class="spec-label">Architecture:</span>
<span class="spec-value">Grid Attention + Relational</span>
</li>
<li>
<span class="spec-label">Key Features:</span>
<span class="spec-value">Object Detection, Pattern Memory</span>
</li>
<li>
<span class="spec-label">Parameters:</span>
<span class="spec-value">~2.1M</span>
</li>
</ul>
</div>
</div>
<div class="model-detail">
<div class="model-header">
<div class="model-icon">🗺️</div>
<h4>ATLAS</h4>
</div>
<p class="model-type">Spatial Transformation</p>
<p>Specialized in geometric transformations including rotations, reflections, and affine mappings.</p>
<div class="architecture-specs">
<ul class="spec-list">
<li>
<span class="spec-label">Architecture:</span>
<span class="spec-value">Spatial Transformer Network</span>
</li>
<li>
<span class="spec-label">Key Features:</span>
<span class="spec-value">Affine Transform, Discrete Ops</span>
</li>
<li>
<span class="spec-label">Parameters:</span>
<span class="spec-value">~1.5M</span>
</li>
</ul>
</div>
</div>
<div class="model-detail">
<div class="model-header">
<div class="model-icon">🎨</div>
<h4>IRIS</h4>
</div>
<p class="model-type">Color Pattern Recognition</p>
<p>Expert in color relationships, mappings, and conditional color transformations.</p>
<div class="architecture-specs">
<ul class="spec-list">
<li>
<span class="spec-label">Architecture:</span>
<span class="spec-value">Color Attention + Mapping</span>
</li>
<li>
<span class="spec-label">Key Features:</span>
<span class="spec-value">Color Embeddings, Rule Learning</span>
</li>
<li>
<span class="spec-label">Parameters:</span>
<span class="spec-value">~0.8M</span>
</li>
</ul>
</div>
</div>
<div class="model-detail">
<div class="model-header">
<div class="model-icon">⏱️</div>
<h4>CHRONOS</h4>
</div>
<p class="model-type">Temporal Sequence Analysis</p>
<p>Analyzes sequential patterns and predicts movements or evolutionary transformations.</p>
<div class="architecture-specs">
<ul class="spec-list">
<li>
<span class="spec-label">Architecture:</span>
<span class="spec-value">LSTM + Temporal Attention</span>
</li>
<li>
<span class="spec-label">Key Features:</span>
<span class="spec-value">Sequence Modeling, Movement</span>
</li>
<li>
<span class="spec-label">Parameters:</span>
<span class="spec-value">~1.8M</span>
</li>
</ul>
</div>
</div>
<div class="model-detail">
<div class="model-header">
<div class="model-icon">🔥</div>
<h4>PROMETHEUS</h4>
</div>
<p class="model-type">Creative Pattern Generation</p>
<p>Generative model for complex transformations and novel pattern synthesis.</p>
<div class="architecture-specs">
<ul class="spec-list">
<li>
<span class="spec-label">Architecture:</span>
<span class="spec-value">Variational Autoencoder</span>
</li>
<li>
<span class="spec-label">Key Features:</span>
<span class="spec-value">Latent Space, Rule Generation</span>
</li>
<li>
<span class="spec-label">Parameters:</span>
<span class="spec-value">~1.2M</span>
</li>
</ul>
</div>
</div>
</div>
</section>
<section id="strategy" class="section">
<div class="ensemble-strategy">
<h2>Ensemble Strategy</h2>
<p style="color: white; opacity: 0.9;">Our ensemble leverages multiple approaches to achieve robust predictions across diverse pattern types.</p>
<div class="strategy-grid">
<div class="strategy-item">
<div class="strategy-number">01</div>
<div class="strategy-title">Parallel Processing</div>
<div class="strategy-desc">All models process inputs simultaneously for maximum efficiency</div>
</div>
<div class="strategy-item">
<div class="strategy-number">02</div>
<div class="strategy-title">Intelligent Weighted Voting</div>
<div class="strategy-desc">Task-aware weighting based on pattern type and model expertise</div>
</div>
<div class="strategy-item">
<div class="strategy-number">03</div>
<div class="strategy-title">Uncertainty Quantification</div>
<div class="strategy-desc">Multiple forward passes to estimate prediction uncertainty</div>
</div>
</div>
</div>
<h3 style="margin-top: 40px;">Weighted Voting Mechanism - The Smart Part</h3>
<p>The ensemble's final predictions use an intelligent weighted voting system that adapts to each task's characteristics:</p>
<div class="voting-process" style="background: #f5f5f5; padding: 30px; border-radius: 15px; margin: 20px 0;">
<h4 style="margin-bottom: 20px;">Step-by-Step Voting Process:</h4>
<ol style="line-height: 2;">
<li><strong>Generate N Solutions:</strong> Run input through all 5 trained models to get 1-5 unique output grids</li>
<li><strong>Count Base Votes:</strong> For each unique grid, count how many models proposed it</li>
<li><strong>Apply Task-Aware Weights:</strong> Analyze the input-output pattern and adjust votes:
<ul style="margin: 10px 0 10px 20px;">
<li>Geometric transformation detected → ATLAS gets 1.5x weight, IRIS gets 0.8x</li>
<li>Pure color mapping → IRIS gets 1.5x weight, ATLAS gets 0.8x</li>
<li>Sequential/movement pattern → CHRONOS gets 1.3x weight</li>
<li>Complex multi-object → MINERVA gets 1.4x weight</li>
<li>Novel/creative pattern → PROMETHEUS gets 1.2x weight</li>
</ul>
</li>
<li><strong>Calculate Final Scores:</strong> Sum weighted votes for each unique solution</li>
<li><strong>Select Winners:</strong>
<ul style="margin: 10px 0 10px 20px;">
<li>Submission #1: Grid with highest weighted score</li>
<li>Submission #2: Grid with second-highest score</li>
</ul>
</li>
</ol>
<div style="background: #e8f9f5; padding: 20px; border-radius: 10px; margin-top: 20px;">
<strong>Example:</strong> For a rotation task, if ATLAS, MINERVA, and CHRONOS all predict Grid A, while IRIS and PROMETHEUS predict Grid B:
<br>• Grid A: (1.5 × 1) + (1.0 × 1) + (1.0 × 1) = 3.5 weighted votes
<br>• Grid B: (0.8 × 1) + (1.0 × 1) = 1.8 weighted votes
<br>→ Grid A wins with 3.5 vs 1.8
</div>
</div>
</section>
<section id="implementation" class="section">
<h2>Technical Implementation</h2>
<h3>Key Features & Innovations</h3>
<ul class="feature-list">
<li>Mixed precision training with FP16 for A100 GPU optimization</li>
<li>Curriculum learning with 3 progressive difficulty stages (easy → medium → hard)</li>
<li><strong>Transformation Penalty:</strong> Novel loss component that actively penalizes models for copying input, forcing true transformations</li>
<li><strong>Exact Match Bonus:</strong> Large sparse reward (5x) for pixel-perfect solutions, providing powerful reinforcement signal</li>
<li>Identity-aware loss: Automatically detects when copying IS correct (identity tasks)</li>
<li>Advanced focal loss for hard-to-classify pixels (gamma=1.5)</li>
<li>Edge-aware weighting (2x on boundaries) for precise object delineation</li>
<li>Learnable residual connections with mixing parameters</li>
<li>CosineAnnealing scheduler for smooth convergence over 300 epochs</li>
</ul>
<h3>V4 Mega-Scale Training Configuration</h3>
<div class="code-block">
# V4 MEGA-SCALE hyperparameters for A100 80GB
BATCH_SIZE = 512
LEARNING_RATE = 0.01
NUM_EPOCHS = 300
GRADIENT_ACCUMULATION = 4 # Effective batch: 2048!
OPTIMIZER = SGD with Nesterov momentum
SCHEDULER = CosineAnnealingLR
# Critical loss innovations
RECONSTRUCTION_WEIGHT = 1.0
TRANSFORMATION_PENALTY = 0.5 # Penalizes copying input
EXACT_MATCH_BONUS = 5.0 # Rewards perfect solutions
EDGE_WEIGHT = 0.3
COLOR_BALANCE_WEIGHT = 0.2
STRUCTURE_WEIGHT = 0.3
</div>
<h3>Model Integration</h3>
<table>
<tr>
<th>Component</th>
<th>Purpose</th>
<th>Implementation</th>
</tr>
<tr>
<td>Ensemble Predictor</td>
<td>Combines model outputs</td>
<td>Weighted average with uncertainty</td>
</tr>
<tr>
<td>Pattern Matcher</td>
<td>Identifies similar training examples</td>
<td>Cosine similarity in feature space</td>
</tr>
<tr>
<td>Reconstruction Loss</td>
<td>Ensures pixel-perfect accuracy</td>
<td>Focal + Edge-aware + Structure</td>
</tr>
<tr>
<td>Validation Pipeline</td>
<td>Tracks exact match accuracy</td>
<td>Comprehensive metrics suite</td>
</tr>
</table>
</section>
<section id="performance" class="section">
<h2>Performance Analysis</h2>
<div class="architecture-overview">
<h3>Metrics Tracking</h3>
<p>Our comprehensive monitoring system tracks multiple accuracy metrics to ensure progress toward the 85% target.</p>
<ul class="feature-list">
<li><strong>Exact Match Accuracy:</strong> Complete grid reconstruction (competition metric)</li>
<li><strong>Pixel-wise Accuracy:</strong> Individual pixel correctness</li>
<li><strong>Active Region Accuracy:</strong> Non-background pixel accuracy</li>
<li><strong>Structure Score:</strong> Object connectivity preservation</li>
</ul>
<h3>Optimization Strategies</h3>
<p>The ensemble is optimized for the competition's exact match requirement through:</p>
<ul class="feature-list">
<li>Edge-aware loss with 3x weighting on boundary pixels</li>
<li>Color balance loss to ensure correct color distributions</li>
<li>Structure preservation loss for maintaining object integrity</li>
<li>Curriculum learning starting with simple transformations</li>
</ul>
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