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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>ARC Prize 2025 - Technical Overview</title>
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<body>
<nav class="nav">
<div class="nav-content">
<h1>ARC Prize 2025 - Technical Documentation</h1>
<div class="nav-links">
<a href="#architecture">Architecture</a>
<a href="#detectors">Detectors</a>
<a href="#pipeline">Pipeline</a>
<a href="#performance">Performance</a>
</div>
</div>
</nav>
<div class="container">
<section id="architecture" class="section">
<h2>System Architecture</h2>
<div class="architecture">
<h3>Two-Phase Approach</h3>
<p>Our solution leverages unlimited offline computation time on Hailo-8 NPU to pre-analyze all patterns, enabling fast online inference within Kaggle's constraints.</p>
<div class="flow-diagram">
<div class="flow-box">
<h4>Offline Phase</h4>
<p>DELPHI + Hailo-8<br>Unlimited Time</p>
</div>
<span class="flow-arrow">→</span>
<div class="flow-box">
<h4>Pattern Library</h4>
<p>~50MB PKL File<br>Cached Patterns</p>
</div>
<span class="flow-arrow">→</span>
<div class="flow-box">
<h4>Online Phase</h4>
<p>Kaggle Runtime<br>12 Hour Limit</p>
</div>
</div>
</div>
<h3>Key Components</h3>
<table>
<tr>
<th>Component</th>
<th>Purpose</th>
<th>Location</th>
<th>Timing</th>
</tr>
<tr>
<td>Pattern Detectors</td>
<td>Identify transformation patterns</td>
<td>Hailo-8 NPU</td>
<td>Offline</td>
</tr>
<tr>
<td>Pre-computation Engine</td>
<td>Analyze all training tasks</td>
<td>DELPHI Device</td>
<td>2-4 hours</td>
</tr>
<tr>
<td>ARC Solver</td>
<td>Apply patterns to test tasks</td>
<td>Kaggle Platform</td>
<td>~2 hours</td>
</tr>
<tr>
<td>Submission Pipeline</td>
<td>Generate competition output</td>
<td>Kaggle Notebook</td>
<td>Minutes</td>
</tr>
</table>
</section>
<section id="detectors" class="section">
<h2>Pattern Detection Modules</h2>
<p>Eight specialized detectors inspired by the Apollo Nexus architecture, each optimized for specific pattern types found in ARC tasks.</p>
<div class="detector-grid">
<div class="detector-card">
<div class="detector-icon">📐</div>
<h4>GeometricDetector</h4>
<p>Identifies rotations (90°, 180°, 270°), reflections (horizontal/vertical), and translations.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🎨</div>
<h4>ColorDetector</h4>
<p>Analyzes color mappings, replacements, and conditional color transformations.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🔢</div>
<h4>CountingDetector</h4>
<p>Detects numerical patterns, size relationships, and counting-based transformations.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🔀</div>
<h4>LogicalDetector</h4>
<p>Identifies boolean operations (AND, OR, XOR) and conditional rules.</p>
</div>
<div class="detector-card">
<div class="detector-icon">📍</div>
<h4>SpatialDetector</h4>
<p>Recognizes gravity effects, alignment patterns, and boundary operations.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🔄</div>
<h4>SymmetryDetector</h4>
<p>Detects symmetry creation, completion, and multi-axis symmetrical patterns.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🎯</div>
<h4>ObjectDetector</h4>
<p>Handles object extraction, movement, and combination patterns.</p>
</div>
<div class="detector-card">
<div class="detector-icon">🔗</div>
<h4>CompositeDetector</h4>
<p>Identifies complex multi-step transformations combining multiple patterns.</p>
</div>
</div>
</section>
<section id="pipeline" class="section">
<h2>Processing Pipeline</h2>
<h3>Offline Pre-computation</h3>
<div class="code-block">
# Initialize Hailo-8 pattern analyzer
analyzer = HailoPatternAnalyzer(hailo_device_id=0)
# Process all 1000 training tasks in parallel
for task_id, task_data in training_tasks.items():
# Run 8 detectors simultaneously on NPU
patterns = analyzer.analyze_task(task_id, task_data)
# Save pattern library (~50MB)
pickle.dump(pattern_library, 'precomputed_patterns.pkl')
</div>
<h3>Online Solving Process</h3>
<div class="timeline">
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<h4>Step 1: Load Pattern Library</h4>
<p>Load pre-computed patterns from Kaggle dataset</p>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<h4>Step 2: Analyze Test Task</h4>
<p>Extract features and find similar patterns</p>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<h4>Step 3: Apply Strategies</h4>
<p>Use 7 solving strategies based on detected patterns</p>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<h4>Step 4: Generate Predictions</h4>
<p>Create 2 attempts per test input</p>
</div>
</div>
</div>
</section>
<section id="performance" class="section">
<h2>Performance Metrics</h2>
<div class="metrics">
<div class="metric-box">
<div class="metric-value">26</div>
<div class="metric-label">TOPS Processing Power</div>
</div>
<div class="metric-box">
<div class="metric-value">85%</div>
<div class="metric-label">Target Accuracy</div>
</div>
<div class="metric-box">
<div class="metric-value"><100ms</div>
<div class="metric-label">Per Task Detection</div>
</div>
<div class="metric-box">
<div class="metric-value">8</div>
<div class="metric-label">Parallel Detectors</div>
</div>
</div>
<h3>Resource Utilization</h3>
<table>
<tr>
<th>Resource</th>
<th>Offline Phase</th>
<th>Online Phase</th>
</tr>
<tr>
<td>Compute</td>
<td>Hailo-8 NPU (26 TOPS)</td>
<td>Kaggle CPU/GPU</td>
</tr>
<tr>
<td>Memory</td>
<td>~4GB for pattern analysis</td>
<td><1GB for inference</td>
</tr>
<tr>
<td>Time</td>
<td>2-4 hours (unlimited)</td>
<td>~2 hours for 240 tasks</td>
</tr>
<tr>
<td>Storage</td>
<td>~50MB pattern library</td>
<td>Minimal (submission only)</td>
</tr>
</table>
<h3>Optimization Strategies</h3>
<p>Our approach maximizes the <span class="highlight">Hailo-8 NPU advantage</span> through:</p>
<ul style="margin-left: 40px; color: #666666;">
<li>Parallel execution of 8 pattern detectors</li>
<li>Pre-computation of all possible patterns offline</li>
<li>Efficient pattern matching using cached results</li>
<li>Minimal computation during online evaluation</li>
<li>Fast numpy operations for transformations</li>
</ul>
</section>
</div>
</body>
</html>