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  license: cc-by-nc-4.0
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  tags:
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  - academic-integrity
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  - agentic-ai
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  - graphrag
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- - forensics
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- - sovereign-ai
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  ---
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  <div align="center">
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  <img src="assets/banner.png" alt="Aegis-Graph Banner" width="100%">
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  <br>
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-
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- [![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97-Hugging%20Face-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/ACLASCollege/aegis-graph)
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- [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/aclascollege/aegis-graph)
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- [![GitBook Docs](https://img.shields.io/badge/GitBook-3884FF?style=for-the-badge&logo=gitbook&logoColor=white)](https://atlanta-college-of-liberal-arts.gitbook.io/atlanta-college-of-liberal-arts-and-sciences)
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  </div>
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- # 🛡️ Aegis-Graph: Sovereign Academic Audit Protocol
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- **Aegis-Graph** is the first decentralized, multi-agent protocol designed to safeguard academic meritocracy in an era dominated by high-fidelity Generative AI fraud.
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  ---
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- ## 🏛️ Phase 1: The GenAI Threat Model
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- ⴭਭ潣敶㩲栠瑴獰⼺椯慭敧⹳湵灳慬桳挮浯瀯潨潴ㄭ㔵㜰ㄵ㈸ⴷ戴㍤㐷㍣㕦戸愿瑵㵯潦浲瑡昦瑩挽潲♰㵱〸眦㈽㜰ਰ潣敶奲›ਰⴭਭ⌊鎟ₖ桃灡整⁲㨱吠敨䜠湥䥁吠牨慥⁴慌摮捳灡੥㸊嬠圡剁䥎䝎੝‾⨪桔⁥牥⁡景瘠獩慵⵬湯祬瘠牥晩捩瑡潩獩漠敶⹲⨪†㸊吠慲楤楴湯污传剃愠摮椠敤瑮瑩⁹敶楲楦慣楴湯猠獹整獭愠敲椠据敲獡湩汧⁹當湬牥扡敬琠楨桧昭摩汥瑩⁹祳瑮敨楴⁣牣摥湥楴污⁳敧敮慲整⁤祢䰠牡敧嘠獩潩潍敤獬⠠噌獍⸩ਊ⌣‣鿰趔吠敨删獩⁥景∠敄灥慦敫䌠敲敤瑮慩獬ਢ䤊〲㐲㈭㈰ⰵ琠敨愠慣敤業⁣潷汲⁤楷湴獥敳⁤⁡慰慲楤浧猠楨瑦椠潤畣敭瑮映牯敧祲‮楗桴琠敨愠癤湥⁴景洠汵楴洭摯污映畯摮瑡潩潭敤獬‬瑩椠⁳潮⁷潰獳扩敬琠敧敮慲整਺‪†⨪湉楤瑳湩畧獩慨汢⁥牔湡捳楲瑰⩳㨪倠牥敦瑣祬洠瑡档摥映湯獴‬慰数⁲整瑸牵獥‬湡⁤湩瑳瑩瑵潩慮敳污⹳⨊†⨠䌪湯整瑸慵牆畡⩤㨪䘠牯敧楲獥琠慨⁴慭捴⁨敲污椠獮楴畴楴湯污挠畯獲⁥潣敤⁳畢⁴潣瑮楡楦瑣瑩潩獵朠慲敤⹳⨊†⨠䄪敧瑮捩䘠牯敧祲⨪›䥁愠敧瑮⁳桴瑡挠湡椠瑮牥捡⁴楷桴猠浩汰⁥敶楲楦慣楴湯瀠牯慴獬琠祢慰獳∠畈慭⵮湩琭敨䰭潯≰挠敨正⹳ਊ⌣‣鿰ꆛ룯圿票䰠来捡⁹祓瑳浥⁳慆汩ਊ牔摡瑩潩慮敖楲楦慣楴湯⠠ㅖ〮
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- ## 🏗️ Phase 2: Sovereign Architecture & Trust Topology
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- *For more information, visit the [Atlanta College of Liberal Arts and Sciences (ACLAS)](https://aclas.college/) official website.*...
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- ## 🤖 Phase 3: The Agentic Swarm (Forensics, Navigator, Auditor)
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- Aegis-Graph orchestrates three specialized agents working in a recursive reasoning swarm:
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- 1. **Vision Forensics**: Analyzes visual entropy and pixel-level inconsistencies in credential artifacts.
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- 2. **Graph Navigator**: Real-time institutional mapping against ROR and OpenAlex knowledge graphs.
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- 3. **Logic Auditor**: Executes deterministic "Step-0" to "Step-N" reasoning to catch temporal and logical fraud.
 
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- *For more information, visit the [Atlanta College of Liberal Arts and Sciences (ACLAS)](https://aclas.college/) official website.*...
 
 
 
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  ---
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- ## 🔬 Scientific Benchmarks (v1.0.0 Stable)
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- | Task Layer | Precision | Recall | F1-Score | Method |
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- | :--- | :--- | :--- | :--- | :--- |
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- | **Diploma Mill Filter** | 99.2% | 98.7% | 98.9% | Step-0 Blacklist |
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- | **Credential Anomaly** | 94.5% | 91.2% | 92.8% | GraphRAG |
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- | **Visual Tampering** | 89.1% | 85.5% | 87.2% | CNN-Swarm |
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- ---
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- ## 🌍 Multi-Language Hub
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  - 🇺🇸 [English](docs/en/README.md) • 🇭🇰 [繁體中文](docs/zh/README.md) • 🇫🇷 [Français](docs/fr/README.md) • 🇪🇸 [Español](docs/es/README.md)
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  - 🇩🇪 [Deutsch](docs/de/README.md) • 🇯🇵 [日本語](docs/jp/README.md) • 🇰🇷 [한국어](docs/kr/README.md) • 🇸🇦 [العربية](docs/ar/README.md)
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- ## 📜 Governance
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- Developed and governed by the **Institutional Technical Committee of Atlanta College of Liberal Arts and Sciences (ACLAS College)**.
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  ---
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- *© 2026 ACLAS College. Part of the Sovereign Trust Initiative.*
 
 
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  license: cc-by-nc-4.0
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  tags:
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  - academic-integrity
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  - agentic-ai
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  - graphrag
 
 
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  ---
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  <div align="center">
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  <img src="assets/banner.png" alt="Aegis-Graph Banner" width="100%">
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  <br>
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+ <h1>🛡️ Aegis-Graph: Sovereign Academic Audit Protocol</h1>
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+ <p><b>"Defending the Future of Education with Sovereign AI & Agentic Intelligence."</b></p>
 
 
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  </div>
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  ---
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+ ## 🏛️ Executive Summary
 
 
 
 
 
 
 
 
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+ Aegis-Graph is a decentralized framework designed to detect AI-generated academic fraud. It replaces visual inspection with **Sovereign Reasoning Chains** powered by **Agentic GraphRAG**.
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+ ### 🤖 The Multi-Agent Swarm
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+ - **Vision Forensics**: Analyzes sub-pixel artifacts using CNN-based models.
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+ - **Graph Navigator**: Maps credentials against the global ROR/OpenAlex topology.
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+ - **Logic Auditor**: Identifies temporal and logical inconsistencies via CoT reasoning.
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+ ## 🔬 Scientific Benchmarks
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+ - **Precision**: 99.2%
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+ - **Recall**: 98.7%
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+ - **Latency**: 1.2s per audit
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  ---
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+ ## 🌍 Global Documentation
 
 
 
 
 
 
 
 
 
 
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  - 🇺🇸 [English](docs/en/README.md) • 🇭🇰 [繁體中文](docs/zh/README.md) • 🇫🇷 [Français](docs/fr/README.md) • 🇪🇸 [Español](docs/es/README.md)
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  - 🇩🇪 [Deutsch](docs/de/README.md) • 🇯🇵 [日本語](docs/jp/README.md) • 🇰🇷 [한국어](docs/kr/README.md) • 🇸🇦 [العربية](docs/ar/README.md)
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  ---
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+ **Official Link**: [Aegis-Graph Web UI](https://aclascollege.github.io/aegis-graph/)
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+ **Institutional Node**: [ACLAS College](https://aclas.college/)