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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>Awesome Self-Evolving Agents</title> |
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"> |
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<style> |
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:root { |
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--primary: #6366f1; |
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--primary-dark: #4f46e5; |
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--secondary: #10b981; |
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--dark: #0f172a; |
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--light: #f8fafc; |
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--gray: #94a3b8; |
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--card-bg: #1e293b; |
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--transition: all 0.3s ease; |
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} |
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* { |
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margin: 0; |
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padding: 0; |
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box-sizing: border-box; |
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} |
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body { |
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font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif; |
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background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%); |
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color: var(--light); |
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line-height: 1.6; |
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overflow-x: hidden; |
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} |
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.container { |
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width: 90%; |
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max-width: 1200px; |
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margin: 0 auto; |
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padding: 0 20px; |
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} |
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header { |
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background: rgba(15, 23, 42, 0.8); |
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backdrop-filter: blur(10px); |
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padding: 20px 0; |
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position: sticky; |
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top: 0; |
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z-index: 100; |
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border-bottom: 1px solid rgba(148, 163, 184, 0.1); |
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} |
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.navbar { |
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display: flex; |
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justify-content: space-between; |
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align-items: center; |
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} |
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.logo { |
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display: flex; |
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align-items: center; |
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gap: 12px; |
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font-size: 1.8rem; |
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font-weight: 700; |
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color: var(--light); |
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text-decoration: none; |
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} |
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.logo i { |
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color: var(--secondary); |
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} |
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.nav-links { |
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display: flex; |
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gap: 30px; |
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} |
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.nav-links a { |
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color: var(--gray); |
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text-decoration: none; |
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font-weight: 500; |
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transition: var(--transition); |
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position: relative; |
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} |
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.nav-links a:hover { |
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color: var(--light); |
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} |
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.nav-links a::after { |
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content: ''; |
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position: absolute; |
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bottom: -5px; |
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left: 0; |
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width: 0; |
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height: 2px; |
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background: var(--primary); |
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transition: var(--transition); |
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} |
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.nav-links a:hover::after { |
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width: 100%; |
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} |
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.hero { |
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padding: 100px 0; |
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text-align: center; |
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position: relative; |
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} |
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.hero::before { |
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content: ''; |
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position: absolute; |
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top: 0; |
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left: 0; |
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width: 100%; |
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height: 100%; |
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background: radial-gradient(circle at center, rgba(99, 102, 241, 0.1) 0%, transparent 70%); |
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z-index: -1; |
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} |
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.hero h1 { |
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font-size: clamp(2.5rem, 5vw, 4rem); |
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font-weight: 800; |
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margin-bottom: 20px; |
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background: linear-gradient(to right, #818cf8, #60a5fa); |
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-webkit-background-clip: text; |
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background-clip: text; |
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color: transparent; |
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line-height: 1.2; |
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} |
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.hero p { |
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font-size: clamp(1.1rem, 2vw, 1.3rem); |
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max-width: 700px; |
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margin: 0 auto 40px; |
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color: var(--gray); |
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} |
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.btn { |
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display: inline-block; |
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padding: 15px 35px; |
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background: var(--primary); |
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color: white; |
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border-radius: 50px; |
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text-decoration: none; |
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font-weight: 600; |
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transition: var(--transition); |
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border: none; |
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cursor: pointer; |
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font-size: 1rem; |
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box-shadow: 0 4px 20px rgba(99, 102, 241, 0.3); |
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} |
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.btn:hover { |
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background: var(--primary-dark); |
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transform: translateY(-3px); |
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box-shadow: 0 8px 25px rgba(99, 102, 241, 0.4); |
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} |
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.btn-secondary { |
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background: transparent; |
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border: 2px solid var(--primary); |
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color: var(--primary); |
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margin-left: 15px; |
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} |
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.btn-secondary:hover { |
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background: var(--primary); |
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color: white; |
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} |
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.section { |
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padding: 80px 0; |
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} |
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.section-title { |
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text-align: center; |
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font-size: 2.5rem; |
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margin-bottom: 40px; |
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position: relative; |
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} |
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.section-title::after { |
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content: ''; |
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position: absolute; |
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bottom: -15px; |
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left: 50%; |
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transform: translateX(-50%); |
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width: 80px; |
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height: 4px; |
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background: var(--secondary); |
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border-radius: 2px; |
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} |
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.features { |
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display: grid; |
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grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); |
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gap: 30px; |
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margin-top: 50px; |
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} |
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.feature-card { |
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background: var(--card-bg); |
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border-radius: 15px; |
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padding: 30px; |
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transition: var(--transition); |
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border: 1px solid rgba(148, 163, 184, 0.1); |
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} |
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.feature-card:hover { |
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transform: translateY(-10px); |
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box-shadow: 0 10px 30px rgba(0, 0, 0, 0.3); |
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border-color: rgba(99, 102, 241, 0.3); |
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} |
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.feature-card i { |
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font-size: 2.5rem; |
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color: var(--primary); |
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margin-bottom: 20px; |
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} |
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.feature-card h3 { |
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font-size: 1.5rem; |
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margin-bottom: 15px; |
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} |
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.feature-card p { |
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color: var(--gray); |
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} |
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.paper-section { |
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background: rgba(30, 41, 59, 0.5); |
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border-radius: 20px; |
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padding: 50px; |
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margin: 50px 0; |
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border: 1px solid rgba(148, 163, 184, 0.2); |
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} |
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.paper-content { |
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display: grid; |
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grid-template-columns: 1fr 300px; |
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gap: 40px; |
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align-items: center; |
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} |
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.paper-info h2 { |
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font-size: 2rem; |
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margin-bottom: 20px; |
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} |
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.paper-info p { |
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margin-bottom: 25px; |
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color: var(--gray); |
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} |
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.paper-actions { |
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display: flex; |
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gap: 15px; |
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flex-wrap: wrap; |
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} |
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.paper-image { |
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border-radius: 10px; |
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overflow: hidden; |
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box-shadow: 0 15px 35px rgba(0, 0, 0, 0.4); |
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} |
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.paper-image img { |
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width: 100%; |
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height: auto; |
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display: block; |
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transition: var(--transition); |
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} |
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.paper-image img:hover { |
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transform: scale(1.03); |
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} |
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.github-stats { |
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display: flex; |
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justify-content: center; |
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gap: 30px; |
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margin-top: 40px; |
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flex-wrap: wrap; |
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} |
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.stat-card { |
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background: var(--card-bg); |
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border-radius: 15px; |
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padding: 25px; |
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text-align: center; |
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min-width: 150px; |
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border: 1px solid rgba(148, 163, 184, 0.1); |
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} |
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.stat-card i { |
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font-size: 2rem; |
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color: var(--secondary); |
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margin-bottom: 15px; |
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} |
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.stat-card .number { |
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font-size: 2rem; |
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font-weight: 700; |
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margin-bottom: 5px; |
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} |
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.stat-card .label { |
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color: var(--gray); |
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font-size: 0.9rem; |
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} |
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.team { |
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display: grid; |
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grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); |
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gap: 30px; |
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margin-top: 50px; |
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} |
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.team-member { |
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background: var(--card-bg); |
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border-radius: 15px; |
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padding: 30px; |
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text-align: center; |
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transition: var(--transition); |
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} |
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.team-member:hover { |
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transform: translateY(-10px); |
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box-shadow: 0 10px 30px rgba(0, 0, 0, 0.3); |
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} |
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.team-member img { |
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width: 120px; |
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height: 120px; |
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border-radius: 50%; |
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object-fit: cover; |
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margin-bottom: 20px; |
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border: 3px solid var(--primary); |
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} |
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.team-member h3 { |
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font-size: 1.5rem; |
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margin-bottom: 10px; |
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} |
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.team-member p { |
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color: var(--gray); |
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margin-bottom: 15px; |
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} |
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.abstract { |
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background: var(--card-bg); |
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border-radius: 15px; |
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padding: 30px; |
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margin: 0 auto; |
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max-width: 1000px; |
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border-left: 4px solid var(--primary); |
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text-align: justify; |
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} |
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.abstract h3 { |
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text-align: center; |
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font-weight: bold; |
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margin-bottom: 1rem; |
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} |
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.abstract p { |
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text-indent: 2em; |
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margin-bottom: 1rem; |
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} |
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.abstract em { |
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font-style: italic; |
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} |
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.social-links { |
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display: flex; |
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justify-content: center; |
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gap: 15px; |
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} |
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.social-links a { |
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display: inline-flex; |
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align-items: center; |
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justify-content: center; |
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width: 40px; |
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height: 40px; |
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border-radius: 50%; |
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background: rgba(148, 163, 184, 0.1); |
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color: var(--light); |
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transition: var(--transition); |
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} |
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.social-links a:hover { |
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background: var(--primary); |
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transform: translateY(-3px); |
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} |
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footer { |
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background: rgba(15, 23, 42, 0.9); |
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padding: 50px 0 20px; |
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text-align: center; |
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border-top: 1px solid rgba(148, 163, 184, 0.1); |
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} |
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.footer-content { |
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margin-bottom: 40px; |
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} |
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.footer-logo { |
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font-size: 2rem; |
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font-weight: 700; |
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margin-bottom: 20px; |
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display: inline-block; |
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} |
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.footer-links { |
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display: flex; |
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justify-content: center; |
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gap: 30px; |
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margin-bottom: 30px; |
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flex-wrap: wrap; |
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} |
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.footer-links a { |
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color: var(--gray); |
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text-decoration: none; |
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transition: var(--transition); |
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} |
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.footer-links a:hover { |
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color: var(--light); |
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} |
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.copyright { |
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color: var(--gray); |
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font-size: 0.9rem; |
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padding-top: 20px; |
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border-top: 1px solid rgba(148, 163, 184, 0.1); |
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} |
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@media (max-width: 768px) { |
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.navbar { |
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flex-direction: column; |
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gap: 20px; |
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} |
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.nav-links { |
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gap: 15px; |
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flex-wrap: wrap; |
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justify-content: center; |
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} |
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.hero { |
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padding: 60px 0; |
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} |
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.paper-content { |
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grid-template-columns: 1fr; |
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text-align: center; |
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} |
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.paper-actions { |
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justify-content: center; |
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} |
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.paper-section { |
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padding: 30px; |
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} |
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.section { |
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padding: 50px 0; |
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} |
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} |
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.highlight { |
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color: var(--secondary); |
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font-weight: 600; |
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} |
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.code-block { |
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background: rgba(30, 41, 59, 0.8); |
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border-radius: 10px; |
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padding: 20px; |
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margin: 20px 0; |
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font-family: monospace; |
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overflow-x: auto; |
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border: 1px solid rgba(148, 163, 184, 0.2); |
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} |
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.authors-list { |
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display: flex; |
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flex-wrap: wrap; |
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justify-content: center; |
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gap: 10px; |
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margin: 20px 0; |
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} |
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.author-item { |
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background: rgba(99, 102, 241, 0.1); |
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padding: 8px 15px; |
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border-radius: 20px; |
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font-size: 0.9rem; |
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} |
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.institutions-list { |
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display: flex; |
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flex-wrap: wrap; |
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justify-content: center; |
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gap: 15px; |
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margin-top: 15px; |
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font-size: 0.85rem; |
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color: var(--gray); |
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} |
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.survey-icon { |
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font-size: 3rem; |
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color: var(--primary); |
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text-align: center; |
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margin-bottom: 20px; |
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} |
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</style> |
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</head> |
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<body id="top"> |
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<header> |
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<div class="container"> |
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<nav class="navbar"> |
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<a href="#" class="logo"> |
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<i class="fa-solid fa-robot"></i> |
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Self-Evolving AI Agents Survey |
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</a> |
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<div class="nav-links"> |
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<a href="#overview">Overview</a> |
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<a href="#framework">Framework</a> |
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<a href="#authors">Authors</a> |
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<a href="#github">GitHub</a> |
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</div> |
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</nav> |
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</div> |
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</header> |
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<section class="hero"> |
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<div class="container"> |
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<div class="survey-icon"> |
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<i class="fas fa-book-open"></i> |
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</div> |
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<h1>A Comprehensive Survey of Self-Evolving AI Agents</h1> |
|
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<p>A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems</p> |
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<div class="hero-actions"> |
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<a href="https://arxiv.org/abs/2508.07407" class="btn" target="_blank">Read Paper</a> |
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<a href="https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents" class="btn btn-secondary" target="_blank">View on GitHub</a> |
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</div> |
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</div> |
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</section> |
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|
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<section id="overview" class="section"> |
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<div class="container"> |
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<h2 class="section-title">Overview</h2> |
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<div class="abstract"> |
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<h3>Abstract</h3> |
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<p> |
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Recent advances in large language models (LLMs) have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To address this limitation, recent research has explored agent <em>evolution</em> techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for <em>self-evolving AI agents</em>, which bridge the static capabilities of foundation models with the continuous adaptability required by <em>lifelong agentic systems</em>. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems. |
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Specifically, we first introduce a <em>unified conceptual framework</em> that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: <em>System inputs</em>, <em>Agent System</em>, <em>Environment</em>, and <em>Optimisers</em>, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system, including foundation models, agent prompts, memory, tools, workflows, and communication mechanisms across agents. |
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We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where agent behaviour and optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the <em>evaluation, safety, and ethical considerations</em> for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability. |
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This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems. |
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</p> |
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</div> |
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</div> |
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</section> |
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<section id="framework" class="section"> |
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<div class="container"> |
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<h2 class="section-title">Conceptual Framework</h2> |
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<div class="features"> |
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<div class="feature-card"> |
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<i class="fas fa-sign-in-alt"></i> |
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<h3>System Inputs</h3> |
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<p>Dynamic data sources that provide information to the agent system. Includes user queries, environmental observations, and interaction data.</p> |
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</div> |
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<div class="feature-card"> |
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<i class="fas fa-robot"></i> |
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<h3>Agent System</h3> |
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<p>The core AI agent components including foundation models, planning modules, memory systems, tools, workflows, and communication mechanisms.</p> |
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</div> |
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<div class="feature-card"> |
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<i class="fas fa-globe"></i> |
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<h3>Environment</h3> |
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<p>The external world or specialized domain where agents operate, providing feedback and constraints that drive evolution.</p> |
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</div> |
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<div class="feature-card"> |
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<i class="fas fa-cogs"></i> |
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<h3>Optimisers</h3> |
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<p>Mechanisms that enable agents to self-evolve by analyzing performance, identifying deficiencies, and implementing improvements.</p> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section id="authors" class="section"> |
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<div class="container"> |
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<h2 class="section-title">Research Team</h2> |
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<div class="authors-list"> |
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<span class="author-item">Jinyuan Fang* (1)</span> |
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<span class="author-item">Yanwen Peng* (2)</span> |
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<span class="author-item">Xi Zhang* (1)</span> |
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<span class="author-item">Yingxu Wang* (3)</span> |
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<span class="author-item">Xinhao Yi (1)</span> |
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<span class="author-item">Guibin Zhang (4)</span> |
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<span class="author-item">Yi Xu (5)</span> |
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<span class="author-item">Bin Wu (6)</span> |
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<span class="author-item">Siwei Liu (7)</span> |
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<span class="author-item">Zihao Li (1)</span> |
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<span class="author-item">Zhaochun Ren (8)</span> |
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<span class="author-item">Nikos Aletras (2)</span> |
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<span class="author-item">Xi Wang (2)</span> |
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<span class="author-item">Han Zhou (5)</span> |
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<span class="author-item">Zaiqiao Meng✉ (1)</span> |
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</div> |
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<div class="institutions-list"> |
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<span>(1) University of Glasgow</span> |
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<span>(2) University of Sheffield</span> |
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<span>(3) Mohamed bin Zayed University of Artificial Intelligence</span> |
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<span>(4) National University of Singapore</span> |
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<span>(5) University of Cambridge</span> |
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<span>(6) University College London</span> |
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<span>(7) University of Aberdeen</span> |
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<span>(8) Leiden University</span> |
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</div> |
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<p style="text-align: center; margin-top: 30px; color: var(--gray);"> |
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* Equal Contributor | ✉ Corresponding Author |
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</p> |
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</section> |
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<section id="github" class="section"> |
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<div class="container"> |
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<h2 class="section-title">Awesome Self-Evolving Agents GitHub Repository</h2> |
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<div class="paper-section"> |
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<h3 style="text-align: center; margin-bottom: 20px;">Awesome Self-Evolving Agents Repository</h3> |
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<p style="text-align: center;">This repository contains a comprehensive collection of resources related to self-evolving AI agents, including:</p> |
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<ul style="max-width: 600px; margin: 20px auto; padding-left: 20px;"> |
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<li>Papers and research articles on agent evolution techniques</li> |
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<li>Frameworks and methodologies for self-evolving systems</li> |
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<li>Case studies in domain-specific applications</li> |
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<li>Evaluation metrics and benchmarks</li> |
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<li>Safety and ethical considerations resources</li> |
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</ul> |
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<div style="text-align: center;"> |
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<a href="https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents" class="btn" target="_blank">Explore Repository</a> |
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<i class="fas fa-book"></i> |
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Self-Evolving AI Agents Survey |
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</a> |
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<p>A comprehensive survey of techniques enabling AI agents to autonomously evolve their behaviors</p> |
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<a href="#overview">Overview</a> |
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<a href="#framework">Framework</a> |
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