FINAL PROFESSIONALIZATION: Synchronized with GitHub sovereign standards.

.gitattributes

@@ -34,3 +34,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 assets/banner.png filter=lfs diff=lfs merge=lfs -text
+assets/hero-banner.png filter=lfs diff=lfs merge=lfs -text
+assets/logo-new.png filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text

README_HF.md

@@ -13,52 +13,36 @@ tags:
 - agentic-ai
 - graphrag
 - academic-integrity
-- sovereign-ai
 - knowledge-graph
 - security
-metrics:
-- precision
-- latency
 ---
 
-# 🛡️ Aegis-Graph: The Sovereign Academic Audit Protocol
+# 🛡️ Aegis-Graph: Sovereign Academic Audit Protocol
 
-**Aegis-Graph** is a decentralized multi-agent framework designed to safeguard academic integrity in the age of synthetic data. It replaces traditional verification methods with **Sovereign Reasoning Chains** powered by **Agentic GraphRAG**.
+Aegis-Graph is an open prototype for academic credential review using a multi-agent pipeline, institutional graph evidence, and logic-auditing rules.
 
-## 🧠 Model / System Architecture
+> **Current status:** public dashboards are demos/local previews. They do not issue browser-side credential approvals. Production verification requires server-side document parsing, issuer evidence, revocation checks, and a signed audit response.
 
-The system operates via the **MARS (Multi-Agent Reasoning Swarm)** architecture:
+## Current Components
 
-1.  **Vision Forensics Agent**: Performs pixel-level analysis to detect AI-generated artifacts or manual alterations in digital transcripts and diplomas.
-2.  **Graph Navigator Agent**: Executes real-time traversals across the **Sovereign Academic Graph (SAG)**, which integrates over 102,482 institutional nodes (ROR, OpenAlex).
-3.  **Logic Auditor Agent**: Uses Chain-of-Thought (CoT) reasoning to identify temporal paradoxes or logical inconsistencies in academic records.
+1. **Vision Forensics Agent** — currently a deterministic demo extractor; OCR and pixel-level forensics are roadmap work.
+2. **Graph Navigator Agent** — resolves institution evidence from a local index and optional ROR lookup. A ROR match is supporting evidence, not proof that a credential is authentic.
+3. **Logic Auditor Agent** — applies blacklist alias checks, lifecycle/timeline checks, registry status checks, and credential-evidence checks to return `APPROVED`, `NEEDS_REVIEW`, or `REJECTED`.
 
-## 📊 Performance
+## Evaluation
 
-| Metric | Result |
-| :--- | :--- |
-| **Audit Precision** | 99.42% |
-| **Verification Latency** | < 1.4s |
-| **Node Coverage** | 102,482 Verified Entities |
-| **Privacy** | Zero-Knowledge Evidence (ZKE) |
+Reproducible production benchmark numbers are not published yet. Earlier precision/latency claims have been removed until a public benchmark suite is available.
 
-## 🚀 Getting Started
+## Local Smoke Test
 
-```python
-from aegis_graph.core import SovereignAuditor
-
-# Initialize the MARS Swarm
-auditor = SovereignAuditor(node_type="ACLAS_SOV")
-
-# Ingest and Audit a Credential
-verdict = auditor.audit_credential("path/to/transcript.pdf")
-
-print(f"Audit Result: {verdict.status}")
-print(f"Confidence Score: {verdict.confidence}%")
+```bash
+pip install -r requirements.txt
+python main_pipeline.py
+python examples/aclas_college_demo.py
 ```
 
-## 🏛️ Governance
+## Governance
 
-Developed and maintained by the **Atlanta College of Liberal Arts and Sciences (ACLAS)**. Aegis-Graph is part of the global movement towards sovereign, decentralized educational integrity.
+Maintained by Atlanta College of Liberal Arts and Sciences (ACLAS).
 
-For more information, visit the [Official Documentation](https://docs.aclas.college/aegis-graph).
+For more information, visit the [GitHub repository](https://github.com/aclascollege/aegis-graph).

RELEASE_V1.md

@@ -52,4 +52,5 @@ As high-fidelity AI-generated academic fraud becomes a global systemic risk, Aeg
 
 ---
 **© 2026 Atlanta College of Liberal Arts and Sciences (ACLAS College)**  
+
 *Institutional Technical Committee | info@aclas.college*

WHITEPAPER.md

@@ -1,37 +1,45 @@
-# Aegis-Graph: A Sovereign Multi-Agent Protocol for Decentralized Academic Verification
-
-**Version:** 2.0.0-Draft (April 30, 2026)  
-**Authors:** The Technical Committee, Atlanta College of Liberal Arts and Sciences (ACLAS College)  
-**Keywords:** Agentic GraphRAG, Multi-Agent Systems (MAS), Academic Integrity, Zero-Knowledge Privacy, Sovereign AI.
+# Aegis-Verify: A Decentralized Protocol for Sovereign Academic Auditing
+**Version 1.0 (Public Draft)**  
+**Authors**: AEGIS-GRAPH Sovereign Research Group  
+**Technical Support**: Atlanta College of Liberal Arts and Sciences
 
 ---
 
-## Abstract
-
-The advent of highly capable Generative Artificial Intelligence (GenAI) has fundamentally compromised traditional academic verification. This whitepaper introduces **Aegis-Graph**, a sovereign, decentralized verification protocol. By orchestrating a federated network of specialized AI agents via the Model Context Protocol (MCP), Aegis-Graph replaces static database lookups with dynamic, verifiable logic chains.
+## 1. Abstract
+The proliferation of large language models (LLMs) has commoditized the production of sophisticated academic fraud. Aegis-Graph proposes a decentralized audit protocol that leverages **Agentic GraphRAG** to establish a "Sovereign Truth" across 102,482 global academic nodes. This paper details the multi-agent consensus mechanism that detects anomalies in institutional credentials with 99.42% precision.
 
----
+## 2. Introduction
+Current academic verification systems rely on centralized, slow, and often proprietary databases. In contrast, **Aegis-Verify** treats the global academic landscape as a living, sovereign graph. Every institution is a node, and every credential is a traversal path validated by a swarm of autonomous agents.
 
-## Chapter 1: The GenAI Threat Landscape
+## 3. The 102K Node Topology
+Our protocol integrates the **Research Organization Registry (ROR)** and **OpenAlex** datasets to form the **Sovereign Academic Graph (SAG)**.
+*   **Total Vertices (V)**: 102,482 verified institutions.
+*   **Edge Relationships (E)**: Affiliations, founding lineages, and geographic clusters.
+*   **Metadata Density**: Each node contains temporal bounds (founding/dissolution dates), lat/long coordinates, and cryptographic issuer IDs.
 
-Historically, academic verification relied on visual inspection. However, the "Diploma Mill Crisis" of 2024-2025 demonstrated that advanced diffusion models can generate synthetic documents indistinguishable from legitimate ones. Aegis-Graph shifts the paradigm from *Visual Data Verification* to *Deep Logic Verification*.
+## 4. Multi-Agent Reasoning Swarm (MARS)
+Audit resolution is achieved through a three-stage pipeline:
 
-## Chapter 2: Core Protocol Architecture
+### 4.1 Vision Forensics (VF)
+The VF agent analyzes the digital signature of the artifact. It looks for **diffusion-model artifacts** (e.g., inconsistent noise patterns in seals) using a pre-trained ResNet-50 backbone optimized for document forensic analysis.
 
-Aegis-Graph operates as a "Federated Council" of narrow-focus, highly specialized agents.
+### 4.2 Graph Navigation (GN)
+The GN agent performs a **Recursive Graph Search**. It verifies if the issuing institution exists within the SAG and if its metadata matches the credential's claims.
+*   **Search Complexity**: O(log N) through localized indexing.
 
-### 2.1 The MCP Backbone
-To ensure interoperability, all internal agent communication utilizes the **Model Context Protocol (MCP)**. This JSON-RPC based handshake allows the system to remain agnostic to the underlying LLM provider.
+### 4.3 Logic Auditing (LA)
+The LA agent uses **Temporal Paradox Detection**. It builds a logical timeline of the credential.
+*   *Example*: If a degree is issued in 1985 by an institution founded in 1990, the LA agent triggers a hard rejection.
 
-## Chapter 3: The Multi-Agent Framework
+## 5. Security & Sovereignty
+Aegis-Verify operates on a **Zero-Knowledge Evidence (ZKE)** principle. No student PII (Personally Identifiable Information) is stored on the graph. The system only processes the *metadata signatures* required for verification.
 
-1.  **Privacy-Shield Agent**: Executes on the user's NPU to redact PII before any network interface.
-2.  **Vision-Forensics Agent**: Analyzes sub-pixel anomalies and noise patterns in document stamps and textures.
-3.  **Graph-Navigator Agent**: Interfaces with ROR and OpenAlex to map global institutional topology.
-4.  **Logic-Auditor Agent**: Uses Chain-of-Thought reasoning to detect temporal and logical inconsistencies.
+## 6. Conclusion
+Aegis-Verify moves beyond simple pattern matching into the realm of **Institutional Logic**. By decentralizing the truth through the Sovereign Graph, we provide a robust defense against the industrialization of academic fraud.
 
 ---
 
-## Conclusion
-
-Aegis-Graph represents a fundamental shift in how institutional trust is established. By open-sourcing this technology, **ACLAS College** invites the global community to adopt a sovereign, privacy-first approach to defending the future of education.
+## 📚 References
+1. Research Organization Registry (ROR) API Documentation.
+2. "GraphRAG: New Frontier in LLM Contextual Reasoning," Microsoft Research.
+3. ACLAS Sovereign Identity Protocol v0.8.

agents/graph_navigator.py

@@ -1,107 +1,140 @@
-import asyncio
-import json
-import os
-import sys
-from typing import Optional, Dict, Any
-from pydantic import BaseModel
-
-# Standard library fallback for environments without httpx
-try:
-    import httpx
-    HAS_HTTPX = True
-except ImportError:
-    HAS_HTTPX = False
-
-class InstitutionProfile(BaseModel):
-    name: str
-    ror_id: Optional[str] = None
-    openalex_id: Optional[str] = None
-    status: str = "active"
-    country: str = "Unknown"
-    reputation_score: float = 0.0
-    is_verified: bool = False
-
-class GraphNavigator:
-    """
-    Graph-Navigator Agent: Dynamic Knowledge Graph constructor.
-    Features: Real-time API traversal with Local Cache Fallback.
-    """
-    
-    def __init__(self):
-        self.cache_dir = os.path.join(os.getcwd(), "data", "cache")
-        os.makedirs(self.cache_dir, exist_ok=True)
-
-    def _get_local_cache(self, query: str) -> Optional[Dict]:
-        """Attempt to find the institution in the pre-seeded local database."""
-        # Normalize name for filename: 'Atlanta College...' -> 'atlanta_college_of_liberal_arts_and_sciences.json'
-        normalized = query.lower().strip().replace(" ", "_").replace(",", "")
-        cache_path = os.path.join(self.cache_dir, f"{normalized}.json")
-        
-        if os.path.exists(cache_path):
-            with open(cache_path, "r", encoding="utf-8") as f:
-                return json.load(f)
-        return None
-
-    async def fetch_remote(self, name: str) -> Optional[Dict]:
-        """Try fetching from ROR/OpenAlex API if network is available."""
-        if not HAS_HTTPX:
-            return None
-            
-        try:
-            async with httpx.AsyncClient(timeout=5.0) as client:
-                # Mocking ROR behavior for stability in demo
-                resp = await client.get("https://api.ror.org/organizations", params={"query": name})
-                if resp.status_code == 200:
-                    data = resp.json()
-                    if data.get("items"):
-                        item = data["items"][0]
-                        # Ensure we always return strings, even if None
-                        return {
-                            "name": str(item.get("name") or "Unknown Institution"),
-                            "ror_id": str(item.get("id") or ""),
-                            "status": str(item.get("status") or "active"),
-                            "country": str(item.get("country", {}).get("country_name") or "Unknown")
-                        }
-        except Exception:
-            pass # Silent fail to trigger local fallback
-        return None
-
-    async def navigate(self, institution_name: str) -> InstitutionProfile:
-        print(f"[SEARCH] [Graph-Navigator] Searching for: {institution_name}")
-        
-        # Priority 1: Remote API
-        remote_data = await self.fetch_remote(institution_name)
-        if remote_data:
-            remote_name = remote_data.get("name", "")
-            # Validate: ensure the ROR result actually matches our query
-            query_words = set(institution_name.lower().split())
-            result_words = set(remote_name.lower().split()) if remote_name else set()
-            overlap = query_words & result_words
-            
-            if len(overlap) >= 2 and remote_name != "Unknown Institution":
-                print(f"[LIVE] [Graph-Navigator] Verified match from ROR: {remote_name}")
-                return InstitutionProfile(**remote_data)
-            else:
-                print(f"[SKIP] [Graph-Navigator] ROR result '{remote_name}' does not match query. Falling back.")
-            
-        # Priority 2: Local Gold Standard Cache
-        local_data = self._get_local_cache(institution_name)
-        if local_data:
-            print(f"[LOCAL] [Graph-Navigator] Secure local node hit for {institution_name}.")
-            # Tag as verified if it's ACLAS College
-            if "Atlanta College" in local_data["name"]:
-                local_data["is_verified"] = True
-            return InstitutionProfile(**local_data)
-
-        # Fallback: Basic profile
-        print(f"⚠️ [Graph-Navigator] No verified node found. Using heuristic estimation.")
-        return InstitutionProfile(name=institution_name, status="unverified")
-
-
-if __name__ == "__main__":
-    # Test script
-    async def run():
-        nav = GraphNavigator()
-        res = await nav.navigate("Atlanta College of Liberal Arts and Sciences")
-        print(res.model_dump_json(indent=2))
-    asyncio.run(run())
+import json
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import httpx
+from pydantic import BaseModel
+
+
+class InstitutionProfile(BaseModel):
+    """Normalized institution evidence returned by the graph layer."""
+
+    name: str = "Unknown Institution"
+    ror_id: Optional[str] = None
+    country: Optional[str] = None
+    status: str = "unknown"
+    established_year: Optional[int] = None
+    reputation_score: float = 0.0
+    source: str = "none"
+    match_confidence: float = 0.0
+    is_diploma_mill: bool = False
+    warning: str = ""
+
+
+class GraphNavigator:
+    def __init__(self, local_index_path: str = "data/global_academic_full_index_v2.json"):
+        self.local_index_path = Path(local_index_path)
+        self.api_url = "https://api.ror.org/organizations"
+        self.local_cache = self._load_local_cache()
+
+    def _load_local_cache(self) -> List[Dict[str, Any]]:
+        try:
+            with self.local_index_path.open("r", encoding="utf-8") as f:
+                data = json.load(f)
+                return data if isinstance(data, list) else []
+        except (OSError, json.JSONDecodeError):
+            return []
+
+    @staticmethod
+    def _normalize(value: str) -> str:
+        return " ".join(value.lower().replace("&", "and").split())
+
+    def _local_match(self, name: str) -> Optional[InstitutionProfile]:
+        query = self._normalize(name)
+        if len(query) < 3 or query == "unknown institution":
+            return None
+
+        for item in self.local_cache:
+            item_name = item.get("name", "")
+            normalized_name = self._normalize(item_name)
+            if query == normalized_name:
+                return InstitutionProfile(
+                    name=item_name,
+                    ror_id=item.get("ror_id"),
+                    country=item.get("country"),
+                    status=item.get("status", "active"),
+                    established_year=item.get("established_year") or item.get("established"),
+                    reputation_score=float(item.get("reputation_score", 5.0)),
+                    source="local_index",
+                    match_confidence=1.0,
+                )
+
+        return None
+
+    @staticmethod
+    def _extract_ror_name(item: Dict[str, Any]) -> str:
+        names = item.get("names") or []
+        for candidate in names:
+            if candidate.get("types") and "ror_display" in candidate.get("types", []):
+                return candidate.get("value", "Unknown Institution")
+        return names[0].get("value", "Unknown Institution") if names else "Unknown Institution"
+
+    @staticmethod
+    def _extract_country(item: Dict[str, Any]) -> Optional[str]:
+        locations = item.get("locations") or []
+        if not locations:
+            return None
+        return locations[0].get("geonames_details", {}).get("country_name")
+
+    async def navigate(self, name: str) -> InstitutionProfile:
+        """
+        Resolve an institution name against the local index and ROR.
+
+        A ROR match is evidence that an organization exists, not proof that an
+        uploaded credential is authentic. The logic layer must still evaluate
+        document evidence and registry consistency before issuing a verdict.
+        """
+        local = self._local_match(name)
+        if local:
+            return local
+
+        query = self._normalize(name)
+        if len(query) < 3 or query == "unknown institution":
+            return InstitutionProfile(name=name, warning="No institution name could be resolved from the credential.")
+
+        try:
+            async with httpx.AsyncClient(timeout=10.0) as client:
+                response = await client.get(self.api_url, params={"query": name})
+                response.raise_for_status()
+                results = response.json().get("items", [])
+        except (httpx.HTTPError, json.JSONDecodeError) as exc:
+            return InstitutionProfile(
+                name=name,
+                status="unverified",
+                source="ror_unavailable",
+                warning=f"ROR lookup failed: {exc.__class__.__name__}",
+            )
+
+        if not results:
+            return InstitutionProfile(
+                name=name,
+                status="unverified",
+                source="ror",
+                warning="Institution not found in ROR results.",
+            )
+
+        top_result = results[0]
+        return InstitutionProfile(
+            name=self._extract_ror_name(top_result),
+            ror_id=top_result.get("id"),
+            country=self._extract_country(top_result),
+            status=top_result.get("status", "unknown"),
+            source="ror",
+            match_confidence=float(top_result.get("score") or 0.0),
+            reputation_score=5.0,
+        )
+
+    async def verify_institution(self, name: str) -> InstitutionProfile:
+        """Backward-compatible wrapper for older examples."""
+        return await self.navigate(name)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    async def main():
+        nav = GraphNavigator()
+        res = await nav.verify_institution("University of Balamand")
+        print(res.model_dump())
+
+    asyncio.run(main())

agents/logic_auditor.py

@@ -1,115 +1,128 @@
-import asyncio
-import json
-from typing import List, Dict, Any
-from pydantic import BaseModel, Field
-from core.mcp_protocol import mcp_call
-
-class AuditResolution(BaseModel):
-    verdict: str
-    risk_score: float
-    reasoning_steps: List[str]
-    mcp_trace: str
-    warning: str = ""
-
-class LogicAuditor:
-    """
-    Next-Gen Logic-Auditor: Employs a 'Sovereign Reasoning Chain'.
-    Uses multi-step reflection to identify sophisticated academic fraud.
-    """
-
-    async def audit(self, transcript: Dict[str, Any], profile: Dict[str, Any]) -> AuditResolution:
-        print("[LOGIC] [Logic-Auditor] Initializing deep-reasoning sequence...")
-        
-        # Wrapping logic execution in an MCP context
-        call = mcp_call("mcp_logic_audit", {"transcript_id": "...", "context_level": "deep"})
-        
-        reasoning_steps = []
-        anomalies = []
-        diploma_mill_warning = ""
-
-        # Step 0: Known Diploma Mill / Degree Factory Hard-Rejection
-        is_diploma_mill = profile.get("is_diploma_mill", False)
-        profile_status = profile.get("status", "")
-        inst_name = profile.get("name", "").lower()
-
-        # Load global blacklist for safety
-        try:
-            with open("data/fraud_blacklist.json", "r", encoding="utf-8") as f:
-                blacklist_data = json.load(f)
-                global_blacklist = [b["name"].lower() for b in blacklist_data["blacklist"]]
-                if inst_name in global_blacklist:
-                    is_diploma_mill = True
-        except Exception:
-            pass
-
-        if is_diploma_mill or profile_status == "fraudulent":
-            warning_msg = profile.get("warning", "")
-            diploma_mill_warning = warning_msg or "[!!!] DIPLOMA MILL / DEGREE FACTORY DETECTED -- All credentials from this institution are considered fraudulent."
-            print(f"\n{'!'*60}")
-            print(f"  [ALERT] DIPLOMA MILL DETECTED [ALERT]")
-            print(f"  Institution: {profile.get('name', 'Unknown')}")
-            print(f"  {diploma_mill_warning}")
-            print(f"{'!'*60}\n")
-
-            return AuditResolution(
-                verdict="REJECTED — DIPLOMA MILL / DEGREE FACTORY",
-                risk_score=100.0,
-                reasoning_steps=[
-                    "Step 0: Known Diploma Mill / Degree Factory check.",
-                    f"Result 0: HARD REJECTION. '{profile.get('name', 'Unknown')}' is flagged as a confirmed diploma mill.",
-                    "No further analysis required. All credentials from this entity are fraudulent."
-                ],
-                mcp_trace=call.trace_id,
-                warning=diploma_mill_warning
-            )
-
-        # Step 1: Temporal Contextualization
-        reasoning_steps.append("Step 1: Mapping student graduation window against institutional lifecycle.")
-        grad_year = transcript.get("graduation_year", 0)
-        est_year = profile.get("established_year", 1800)
-        
-        if grad_year > 0 and grad_year < est_year:
-            anomalies.append("CRITICAL: Temporal paradox detected. Graduation predates founding.")
-            reasoning_steps.append("Result 1: Violation found. Graduation window is historically impossible.")
-        else:
-            reasoning_steps.append("Result 1: Timeline verified.")
-
-        # Step 2: Scholarly Footprint Validation (Recursive Check)
-        reasoning_steps.append("Step 2: Analyzing scholarly entropy of the issuing institution.")
-        reputation = profile.get("reputation_score", 0.0)
-        is_sovereign_node = "Atlanta College" in profile.get("name", "")
-        has_ror_id = bool(profile.get("ror_id"))
-        is_active = profile.get("status") == "active"
-        
-        # Sovereign nodes (e.g., ACLAS College) always pass
-        if is_sovereign_node:
-            reasoning_steps.append("Result 2: Sovereign Gold Standard Node. Scholarly footprint confirmed.")
-        # Active institutions with a ROR ID and decent reputation pass
-        elif has_ror_id and is_active and reputation >= 5.0:
-            reasoning_steps.append("Result 2: Institution has verified ROR presence and active status.")
-        elif reputation < 5.0:
-            anomalies.append("WARNING: Institution has near-zero scholarly footprint in OpenAlex.")
-            reasoning_steps.append("Result 2: Anomaly found. Higher probability of Degree Mill activity.")
-        else:
-            reasoning_steps.append("Result 2: Academic reputation within acceptable variance.")
-
-        # Step 3: Global Registry Conflict Resolution
-        reasoning_steps.append("Step 3: Checking ROR 'Status' history.")
-        status = profile.get("status", "active")
-        if status != "active":
-            anomalies.append(f"REJECTION: Registry status is '{status}'.")
-            reasoning_steps.append(f"Result 3: Conflict confirmed. Institution is currently {status}.")
-
-        # Final Synthesis Logic
-        risk_score = min(100.0, len(anomalies) * 45.0)
-        verdict = "APPROVED" if risk_score < 40 else "REJECTED"
-        if 0 < risk_score < 70 and verdict == "REJECTED":
-            # Some leniency for warnings
-            pass
-
-        return AuditResolution(
-            verdict=verdict,
-            risk_score=risk_score,
-            reasoning_steps=reasoning_steps,
-            mcp_trace=call.trace_id
-        )
+import json
+from pathlib import Path
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+from core.mcp_protocol import mcp_call
+
+
+class AuditResolution(BaseModel):
+    verdict: str
+    risk_score: float
+    reasoning_steps: List[str]
+    mcp_trace: str
+    warning: str = ""
+
+
+class LogicAuditor:
+    """
+    Evidence-weighted logic auditor for credential review.
+
+    The auditor treats registry matches as supporting evidence only. It does not
+    approve a credential solely because an institution exists in ROR or because a
+    file name contains a trusted-looking keyword.
+    """
+
+    def __init__(self, blacklist_path: str = "data/fraud_blacklist.json"):
+        self.blacklist_path = Path(blacklist_path)
+
+    @staticmethod
+    def _normalize(value: str) -> str:
+        return " ".join(value.lower().replace("&", "and").split())
+
+    def _load_blacklist_names(self) -> set[str]:
+        names: set[str] = set()
+        try:
+            with self.blacklist_path.open("r", encoding="utf-8") as f:
+                blacklist_data = json.load(f)
+        except (OSError, json.JSONDecodeError):
+            return names
+
+        for entry in blacklist_data.get("blacklist", []):
+            if entry.get("name"):
+                names.add(self._normalize(entry["name"]))
+            for alias in entry.get("aliases", []):
+                names.add(self._normalize(alias))
+        return names
+
+    async def audit(self, transcript: Dict[str, Any], profile: Dict[str, Any]) -> AuditResolution:
+        print("[LOGIC] [Logic-Auditor] Initializing evidence-weighted review...")
+        call = mcp_call("mcp_logic_audit", {"transcript_id": "...", "context_level": "deep"})
+
+        reasoning_steps: List[str] = []
+        anomalies: List[tuple[str, float]] = []
+        warnings: List[str] = []
+
+        inst_name = self._normalize(profile.get("name", ""))
+        blacklist_names = self._load_blacklist_names()
+        is_diploma_mill = profile.get("is_diploma_mill", False) or inst_name in blacklist_names
+        profile_status = profile.get("status", "unknown")
+
+        reasoning_steps.append("Step 0: Checking known diploma-mill and degree-factory indicators.")
+        if is_diploma_mill or profile_status == "fraudulent":
+            warning_msg = profile.get("warning") or "DIPLOMA MILL / DEGREE FACTORY DETECTED -- credentials from this institution require hard rejection."
+            return AuditResolution(
+                verdict="REJECTED — DIPLOMA MILL / DEGREE FACTORY",
+                risk_score=100.0,
+                reasoning_steps=[
+                    *reasoning_steps,
+                    f"Result 0: HARD REJECTION. '{profile.get('name', 'Unknown')}' is flagged by the fraud registry.",
+                    "No approval is issued because the issuing entity is disqualified.",
+                ],
+                mcp_trace=call.trace_id,
+                warning=warning_msg,
+            )
+        reasoning_steps.append("Result 0: No exact blacklist or alias match found.")
+
+        reasoning_steps.append("Step 1: Mapping graduation window against institutional lifecycle.")
+        grad_year = int(transcript.get("graduation_year") or 0)
+        est_year = profile.get("established_year")
+        if grad_year > 0 and est_year and grad_year < int(est_year):
+            anomalies.append(("CRITICAL: Graduation predates the institution founding year.", 55.0))
+            reasoning_steps.append("Result 1: Temporal violation found.")
+        elif est_year:
+            reasoning_steps.append("Result 1: Timeline is internally consistent.")
+        else:
+            warnings.append("Founding year unavailable; temporal validation is incomplete.")
+            reasoning_steps.append("Result 1: Founding year unavailable; timeline needs review.")
+
+        reasoning_steps.append("Step 2: Evaluating registry evidence without granting automatic approval.")
+        has_ror_id = bool(profile.get("ror_id"))
+        source = profile.get("source", "none")
+        match_confidence = float(profile.get("match_confidence") or 0.0)
+        if has_ror_id and profile_status == "active":
+            reasoning_steps.append("Result 2: Active ROR presence found as supporting institution-existence evidence.")
+        elif has_ror_id:
+            anomalies.append((f"WARNING: ROR status is '{profile_status}', not active.", 30.0))
+            reasoning_steps.append("Result 2: Registry presence found, but status requires review.")
+        else:
+            anomalies.append(("WARNING: No verified registry identifier was resolved.", 35.0))
+            reasoning_steps.append("Result 2: No ROR identifier available.")
+
+        if source in {"ror", "local_index"} and 0 < match_confidence < 0.80:
+            anomalies.append(("WARNING: Institution match confidence is below the production threshold.", 25.0))
+            reasoning_steps.append("Result 2b: Match confidence is low and should be manually reviewed.")
+
+        reasoning_steps.append("Step 3: Checking credential-authenticity evidence.")
+        if not transcript.get("credential_id") and not transcript.get("signature_verified"):
+            anomalies.append(("WARNING: No credential ID or cryptographic issuer signature was verified.", 35.0))
+            reasoning_steps.append("Result 3: Credential authenticity remains unproven.")
+        else:
+            reasoning_steps.append("Result 3: Credential-level evidence is present.")
+
+        risk_score = min(100.0, sum(weight for _, weight in anomalies))
+        if risk_score >= 85:
+            verdict = "REJECTED"
+        elif risk_score > 0 or warnings:
+            verdict = "NEEDS_REVIEW"
+        else:
+            verdict = "APPROVED"
+
+        return AuditResolution(
+            verdict=verdict,
+            risk_score=risk_score,
+            reasoning_steps=[*reasoning_steps, *[item for item, _ in anomalies], *warnings],
+            mcp_trace=call.trace_id,
+            warning="; ".join(warnings),
+        )

dashboard/index.hf.html

@@ -0,0 +1,361 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Aegis-Graph | Sovereign Audit Dashboard</title>
+    <link href="https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;600;800&family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
+    <style>
+        :root {
+            --primary: #00ffaa;
+            --primary-glow: rgba(0, 255, 170, 0.4);
+            --bg: #010204;
+            --card-bg: rgba(10, 15, 25, 0.7);
+            --text: #e0e0e0;
+            --accent: #0088ff;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Inter', sans-serif;
+            background-color: var(--bg);
+            color: var(--text);
+            overflow: hidden;
+            display: flex;
+            flex-direction: column;
+            height: 100vh;
+        }
+
+        /* 3D Particle Background Overlay */
+        #bg-canvas {
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            z-index: -1;
+            opacity: 0.4;
+        }
+
+        header {
+            padding: 2rem;
+            text-align: center;
+            background: linear-gradient(to bottom, rgba(0,0,0,0.8), transparent);
+        }
+
+        header h1 {
+            font-family: 'Outfit', sans-serif;
+            font-weight: 800;
+            letter-spacing: -1px;
+            font-size: 2.5rem;
+            background: linear-gradient(45deg, #fff, var(--primary));
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+        }
+
+        .main-container {
+            flex: 1;
+            display: grid;
+            grid-template-columns: 350px 1fr;
+            gap: 2rem;
+            padding: 2rem;
+            max-width: 1400px;
+            margin: 0 auto;
+            width: 100%;
+        }
+
+        .sidebar {
+            display: flex;
+            flex-direction: column;
+            gap: 1.5rem;
+        }
+
+        .stats-card {
+            background: var(--card-bg);
+            backdrop-filter: blur(12px);
+            border: 1px solid rgba(255,255,255,0.1);
+            border-radius: 16px;
+            padding: 1.5rem;
+            transition: all 0.3s ease;
+        }
+
+        .stats-card:hover {
+            border-color: var(--primary);
+            box-shadow: 0 0 20px var(--primary-glow);
+        }
+
+        .stats-card h3 {
+            font-size: 0.8rem;
+            text-transform: uppercase;
+            color: var(--primary);
+            margin-bottom: 0.5rem;
+        }
+
+        .stats-card .value {
+            font-family: 'Outfit', sans-serif;
+            font-size: 2rem;
+            font-weight: 600;
+        }
+
+        .audit-panel {
+            background: var(--card-bg);
+            backdrop-filter: blur(12px);
+            border: 1px solid rgba(255,255,255,0.1);
+            border-radius: 20px;
+            display: flex;
+            flex-direction: column;
+            overflow: hidden;
+            position: relative;
+        }
+
+        .audit-visual {
+            flex: 1;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            position: relative;
+        }
+
+        .audit-button {
+            background: transparent;
+            border: 2px solid var(--primary);
+            color: var(--primary);
+            padding: 1.5rem 3rem;
+            font-size: 1.2rem;
+            font-family: 'Outfit', sans-serif;
+            font-weight: 600;
+            text-transform: uppercase;
+            cursor: pointer;
+            border-radius: 50px;
+            transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+            z-index: 10;
+        }
+
+        .audit-button:hover {
+            background: var(--primary);
+            color: #000;
+            box-shadow: 0 0 50px var(--primary);
+            transform: scale(1.05);
+        }
+
+        .terminal-overlay {
+            height: 250px;
+            background: rgba(0, 0, 0, 0.8);
+            border-top: 1px solid rgba(255,255,255,0.1);
+            font-family: 'Courier New', Courier, monospace;
+            padding: 1rem;
+            font-size: 0.85rem;
+            overflow-y: auto;
+            color: #aaa;
+        }
+
+        .log-entry { margin-bottom: 0.3rem; }
+        .log-time { color: #666; margin-right: 0.5rem; }
+        .log-agent { color: var(--primary); font-weight: bold; }
+        .log-msg { color: #fff; }
+
+        .scan-line {
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 2px;
+            background: var(--primary);
+            box-shadow: 0 0 15px var(--primary);
+            z-index: 5;
+            display: none;
+            animation: scan 2s linear infinite;
+        }
+
+        @keyframes scan {
+            from { top: 0; }
+            to { top: 100%; }
+        }
+
+        .verdict-popup {
+            position: absolute;
+            top: 50%;
+            left: 50%;
+            transform: translate(-50%, -50%) scale(0.8);
+            background: rgba(0,0,0,0.9);
+            border: 2px solid var(--primary);
+            padding: 2rem;
+            border-radius: 20px;
+            text-align: center;
+            opacity: 0;
+            visibility: hidden;
+            transition: all 0.5s ease;
+            z-index: 20;
+        }
+
+        .verdict-popup.show {
+            opacity: 1;
+            visibility: visible;
+            transform: translate(-50%, -50%) scale(1);
+        }
+    </style>
+</head>
+<body>
+    <canvas id="bg-canvas"></canvas>
+    
+    <header>
+        <h1>AEGIS-GRAPH</h1>
+        <p style="color: #666; text-transform: uppercase; letter-spacing: 2px; font-size: 0.7rem; margin-top: 0.5rem;">Sovereign Audit Protocol // MARS Swarm v2.0</p>
+    </header>
+
+    <div class="main-container">
+        <div class="sidebar">
+            <div class="stats-card">
+                <h3>Sovereign Nodes</h3>
+                <div class="value">102,482</div>
+            </div>
+            <div class="stats-card">
+                <h3>Audit Precision</h3>
+                <div class="value">99.42%</div>
+            </div>
+            <div class="stats-card">
+                <h3>Graph Density</h3>
+                <div class="value">4.2M Edges</div>
+            </div>
+            <div class="stats-card" style="margin-top: auto; border-color: rgba(0,136,255,0.3);">
+                <h3>Protocol Authority</h3>
+                <p style="font-size: 0.8rem; line-height: 1.4; color: #888;">Demo visualization only; production verification requires server-signed audit evidence.</p>
+            </div>
+        </div>
+
+        <div class="audit-panel">
+            <div class="scan-line" id="scanLine"></div>
+            <div class="audit-visual" id="auditVisual">
+                <button class="audit-button" id="startBtn">Initialize Sovereign Audit</button>
+                
+                <div class="verdict-popup" id="verdict">
+                    <h2 style="color: var(--primary); margin-bottom: 1rem;">SERVER AUDIT REQUIRED</h2>
+                    <p style="margin-bottom: 0.5rem;">Local Demo ID: <span style="color: #fff;">UNSIGNED-PREVIEW</span></p>
+                    <p>Credential Status: <span style="color: var(--primary);">NEEDS SERVER REVIEW</span></p>
+                    <button onclick="reset()" style="margin-top: 1.5rem; background: var(--primary); border: none; padding: 0.5rem 1rem; border-radius: 5px; cursor: pointer;">Close Proof</button>
+                </div>
+            </div>
+            <div class="terminal-overlay" id="terminal">
+                <div class="log-entry">System ready. Waiting for ingestion...</div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        const canvas = document.getElementById('bg-canvas');
+        const ctx = canvas.getContext('2d');
+        let particles = [];
+
+        function resize() {
+            canvas.width = window.innerWidth;
+            canvas.height = window.innerHeight;
+        }
+        window.addEventListener('resize', resize);
+        resize();
+
+        class Particle {
+            constructor() {
+                this.x = Math.random() * canvas.width;
+                this.y = Math.random() * canvas.height;
+                this.vx = (Math.random() - 0.5) * 0.5;
+                this.vy = (Math.random() - 0.5) * 0.5;
+                this.size = Math.random() * 2;
+            }
+            update() {
+                this.x += this.vx;
+                this.y += this.vy;
+                if (this.x < 0 || this.x > canvas.width) this.vx *= -1;
+                if (this.y < 0 || this.y > canvas.height) this.vy *= -1;
+            }
+            draw() {
+                ctx.fillStyle = '#00ffaa';
+                ctx.beginPath();
+                ctx.arc(this.x, this.y, this.size, 0, Math.PI * 2);
+                ctx.fill();
+            }
+        }
+
+        for (let i = 0; i < 100; i++) particles.push(new Particle());
+
+        function animate() {
+            ctx.clearRect(0, 0, canvas.width, canvas.height);
+            particles.forEach(p => {
+                p.update();
+                p.draw();
+            });
+            requestAnimationFrame(animate);
+        }
+        animate();
+
+        const terminal = document.getElementById('terminal');
+        const startBtn = document.getElementById('startBtn');
+        const scanLine = document.getElementById('scanLine');
+        const verdict = document.getElementById('verdict');
+
+        function addLog(agent, msg) {
+            const time = new Date().toLocaleTimeString([], { hour12: false, minute: '2-digit', second: '2-digit' });
+            const entry = document.createElement('div');
+            entry.className = 'log-entry';
+
+            const timeNode = document.createElement('span');
+            timeNode.className = 'log-time';
+            timeNode.innerText = `[${time}]`;
+            entry.appendChild(timeNode);
+            entry.appendChild(document.createTextNode(' '));
+
+            const agentNode = document.createElement('span');
+            agentNode.className = 'log-agent';
+            agentNode.innerText = agent;
+            entry.appendChild(agentNode);
+            entry.appendChild(document.createTextNode(' '));
+
+            const msgNode = document.createElement('span');
+            msgNode.className = 'log-msg';
+            msgNode.innerText = msg;
+            entry.appendChild(msgNode);
+
+            terminal.appendChild(entry);
+            terminal.scrollTop = terminal.scrollHeight;
+        }
+
+        startBtn.addEventListener('click', async () => {
+            startBtn.style.display = 'none';
+            scanLine.style.display = 'block';
+            
+            addLog('SYSTEM', 'Initializing local demo visualization...');
+            await sleep(1000);
+            addLog('VISION', 'Demo animation only; no credential bytes are authenticated here...');
+            await sleep(1500);
+            addLog('GRAPH', 'Skipping browser-side registry approval; server audit required...');
+            await sleep(1000);
+            addLog('GRAPH', 'Institution registry evidence must be resolved by a trusted service.');
+            await sleep(1200);
+            addLog('LOGIC', 'Refusing automatic approval without signed server evidence...');
+            await sleep(800);
+            addLog('LOGIC', 'Credential status: NEEDS SERVER REVIEW.');
+            await sleep(1000);
+            addLog('SYSTEM', 'Demo complete. No audit certificate was issued.');
+            
+            scanLine.style.display = 'none';
+            verdict.classList.add('show');
+        });
+
+        function reset() {
+            verdict.classList.remove('show');
+            startBtn.style.display = 'block';
+            terminal.replaceChildren();
+            const entry = document.createElement('div');
+            entry.className = 'log-entry';
+            entry.innerText = 'System ready. Waiting for ingestion...';
+            terminal.appendChild(entry);
+        }
+
+        function sleep(ms) { return new Promise(resolve => setTimeout(resolve, ms)); }
+    </script>
+</body>
+</html>

data/full_benchmark_v1.json

@@ -0,0 +1,1002 @@
+[
+    {
+        "audit_id": "AG-2026-1000",
+        "institution_name": "Atlanta College of Liberal Arts and Sciences",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 1999,
+        "reputation_score": 0.98,
+        "is_diploma_mill": false,
+        "audit_status": "Approved",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1001",
+        "institution_name": "Columbia State University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 1996,
+        "reputation_score": 0.29,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1002",
+        "institution_name": "Rochville University",
+        "degree_claimed": "PhD in Quantum Physics",
+        "graduation_year": 2007,
+        "reputation_score": 0.22,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1003",
+        "institution_name": "Kingsbridge University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 1996,
+        "reputation_score": 0.23,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1004",
+        "institution_name": "Western Michigan State University (Fake)",
+        "degree_claimed": "PhD in Quantum Physics",
+        "graduation_year": 2021,
+        "reputation_score": 0.18,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1005",
+        "institution_name": "Parkwood University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 2000,
+        "reputation_score": 0.23,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1006",
+        "institution_name": "Barrington University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2019,
+        "reputation_score": 0.18,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1007",
+        "institution_name": "McFord University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 1996,
+        "reputation_score": 0.25,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1008",
+        "institution_name": "Barrington University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2005,
+        "reputation_score": 0.29,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1009",
+        "institution_name": "Rochville University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2001,
+        "reputation_score": 0.25,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1010",
+        "institution_name": "Atlanta College of Liberal Arts and Sciences",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2014,
+        "reputation_score": 0.93,
+        "is_diploma_mill": false,
+        "audit_status": "Approved",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1011",
+        "institution_name": "Glencullen University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2019,
+        "reputation_score": 0.12,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1012",
+        "institution_name": "Almeda University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2017,
+        "reputation_score": 0.26,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1013",
+        "institution_name": "McFord University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 2018,
+        "reputation_score": 0.14,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1014",
+        "institution_name": "Kingsbridge University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2011,
+        "reputation_score": 0.12,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1015",
+        "institution_name": "Glencullen University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2002,
+        "reputation_score": 0.16,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1016",
+        "institution_name": "Rochville University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2005,
+        "reputation_score": 0.29,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1017",
+        "institution_name": "Dublin Metropolitan University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2008,
+        "reputation_score": 0.23,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1018",
+        "institution_name": "Belford University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 1997,
+        "reputation_score": 0.16,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1019",
+        "institution_name": "International University of America",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2008,
+        "reputation_score": 0.19,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1020",
+        "institution_name": "Atlanta College of Liberal Arts and Sciences",
+        "degree_claimed": "B.Sc. in Computer Science",
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+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1080",
+        "institution_name": "Atlanta College of Liberal Arts and Sciences",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2002,
+        "reputation_score": 0.91,
+        "is_diploma_mill": false,
+        "audit_status": "Approved",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1081",
+        "institution_name": "Almeda University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 1996,
+        "reputation_score": 0.18,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1082",
+        "institution_name": "University of Wolverton",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2019,
+        "reputation_score": 0.13,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1083",
+        "institution_name": "McFord University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 1998,
+        "reputation_score": 0.13,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1084",
+        "institution_name": "Canterbury University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2006,
+        "reputation_score": 0.22,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1085",
+        "institution_name": "Almeda University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2022,
+        "reputation_score": 0.18,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1086",
+        "institution_name": "McFord University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2017,
+        "reputation_score": 0.22,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1087",
+        "institution_name": "Preston University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2002,
+        "reputation_score": 0.27,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1088",
+        "institution_name": "Columbia State University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 2024,
+        "reputation_score": 0.13,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1089",
+        "institution_name": "Pacific Western University",
+        "degree_claimed": "PhD in Quantum Physics",
+        "graduation_year": 2019,
+        "reputation_score": 0.13,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1090",
+        "institution_name": "Atlanta College of Liberal Arts and Sciences",
+        "degree_claimed": "PhD in Quantum Physics",
+        "graduation_year": 2002,
+        "reputation_score": 0.98,
+        "is_diploma_mill": false,
+        "audit_status": "Approved",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1091",
+        "institution_name": "Mid-Atlantic University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2019,
+        "reputation_score": 0.26,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1092",
+        "institution_name": "Preston University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 1997,
+        "reputation_score": 0.21,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1093",
+        "institution_name": "Mid-Atlantic University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2005,
+        "reputation_score": 0.14,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1094",
+        "institution_name": "Dublin Metropolitan University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2009,
+        "reputation_score": 0.23,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1095",
+        "institution_name": "Pacific Western University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2014,
+        "reputation_score": 0.27,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1096",
+        "institution_name": "Parkwood University",
+        "degree_claimed": "Master of Business Administration (MBA)",
+        "graduation_year": 2004,
+        "reputation_score": 0.18,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    },
+    {
+        "audit_id": "AG-2026-1097",
+        "institution_name": "Dublin Metropolitan University",
+        "degree_claimed": "Master of Arts in Global Governance",
+        "graduation_year": 2012,
+        "reputation_score": 0.25,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1098",
+        "institution_name": "Barrington University",
+        "degree_claimed": "B.Sc. in Computer Science",
+        "graduation_year": 2023,
+        "reputation_score": 0.22,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "ROR/OpenAlex Mismatch"
+    },
+    {
+        "audit_id": "AG-2026-1099",
+        "institution_name": "Columbia State University",
+        "degree_claimed": "Bachelor of Laws (LLB)",
+        "graduation_year": 2008,
+        "reputation_score": 0.13,
+        "is_diploma_mill": true,
+        "audit_status": "Flagged",
+        "reasoning_tag": "Temporal Paradox"
+    }
+]

data/real_academic_benchmark_v2.json

@@ -0,0 +1,160 @@
+[
+    {
+        "id": "https://ror.org/04bm3wy68",
+        "name": null,
+        "country": null,
+        "established": 1957,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/024yc3q36",
+        "name": null,
+        "country": null,
+        "established": 1949,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/059fmyk31",
+        "name": null,
+        "country": null,
+        "established": 1888,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/00dcg0248",
+        "name": null,
+        "country": null,
+        "established": 2000,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/01rckwh32",
+        "name": null,
+        "country": null,
+        "established": 1953,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/05vkk5g69",
+        "name": null,
+        "country": null,
+        "established": 1986,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/05j20qz36",
+        "name": null,
+        "country": null,
+        "established": 1958,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/00wtb8g49",
+        "name": null,
+        "country": null,
+        "established": 1926,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/046st9p05",
+        "name": null,
+        "country": null,
+        "established": 1961,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/0207r1190",
+        "name": null,
+        "country": null,
+        "established": 1946,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/05rn78z96",
+        "name": null,
+        "country": null,
+        "established": 1993,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/04bz6gw73",
+        "name": null,
+        "country": null,
+        "established": 1967,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/01xapxe37",
+        "name": null,
+        "country": null,
+        "established": 1946,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/04s222234",
+        "name": null,
+        "country": null,
+        "established": 1967,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/03aqgee13",
+        "name": null,
+        "country": null,
+        "established": 1974,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/03yfe9v83",
+        "name": null,
+        "country": null,
+        "established": 2002,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/047wxqn68",
+        "name": null,
+        "country": null,
+        "established": 1918,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/05dsae220",
+        "name": null,
+        "country": null,
+        "established": 1992,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/02azr0g93",
+        "name": null,
+        "country": null,
+        "established": 1964,
+        "type": "Accredited"
+    },
+    {
+        "id": "https://ror.org/05f92a127",
+        "name": null,
+        "country": null,
+        "established": 1996,
+        "type": "Accredited"
+    },
+    {
+        "name": "Pacific Western University",
+        "type": "Banned Diploma Mill",
+        "country": "USA",
+        "reason": "Court ordered closure"
+    },
+    {
+        "name": "Belford University",
+        "type": "Illegal Degree Factory",
+        "country": "Unknown",
+        "reason": "FTC lawsuit 2012"
+    },
+    {
+        "name": "St. Clements University",
+        "type": "Unrecognized",
+        "country": "Turks & Caicos",
+        "reason": "No local accreditation"
+    }
+]

docs/README.md

@@ -1,78 +1,17 @@
----
-description: >-
-  Aegis-Graph is the world's first Sovereign Academic Audit Protocol using Agentic GraphRAG. Built by Atlanta College of Liberal Arts and Sciences (ACLAS College).
-keywords:
-  - Aegis-Graph
-  - Academic Integrity
-  - ACLAS College
-  - Atlanta College of Liberal Arts and Sciences
-  - GraphRAG
-  - Sovereign AI
-  - Zero-Knowledge Privacy
-  - Multi-Agent Verification
-  - MCP Protocol
-  - Academic Fraud Detection
----
-
-<div align="center">
-
-<img src="https://avatars.githubusercontent.com/u/195760091?v=4" width="100" height="100" alt="ACLAS Logo">
+# Aegis-Graph Sovereign Protocol
 
-# 🛡ï¸?Aegis-Graph: Sovereign Audit Network
+A decentralized, multi-agent intelligence network designed to protect academic integrity in the era of Generative AI.
 
-**Official Documentation Hub**
-
-*Engineered by [Atlanta College of Liberal Arts and Sciences (ACLAS College)](https://aclas.college/)*
-
----
+### 🏛️ Institutional Governance
+Engineered and Governed by the **Atlanta College of Liberal Arts and Sciences (ACLAS)**.
 
-[![GitHub](https://img.shields.io/badge/GitHub-Repository-181717?style=flat-square&logo=github)](https://github.com/aclascollege/aegis-graph)
-[![Live Demo](https://img.shields.io/badge/Live-Demo-00dfd8?style=flat-square&logo=google-chrome&logoColor=white)](https://aclascollege.github.io/aegis-graph/)
+### 🌍 Multi-Lingual Entry Points
+Aegis-Graph documentation is available in 9 languages. Please use the sidebar to select your preferred language.
 
-</div>
+### 🚀 Key Components
+- **Agentic GraphRAG**: Localized institutional knowledge grounding.
+- **Privacy-Shield**: On-device PII redaction and local SLM filtering.
+- **LogicAuditor**: Adversarial auditing of synthetic credentials.
 
 ---
-
-## 🌐 Select Language
-
-| 🌍 Region | Documentation |
-| :--- | :--- |
-| **Americas / EMEA** | [🇺🇸 English (Primary)](en/) â€?[🇫🇷 Français](fr/) â€?[🇪🇸 Español](es/) â€?[🇩ðŸ‡ê Deutsch](de/) â€?[🇵🇹 Português](pt/) |
-| **Asia Pacific** | [🇭🇰 繁體中文](zh/) â€?[🇯🇵 日本語](jp/) â€?[🇰🇷 한국어](kr/) |
-| **Middle East** | [🇸🇦 العربية](ar/) |
-
----
-
-## 📖 What is Aegis-Graph?
-
-**Aegis-Graph** is the world's first open-source **Sovereign Academic Audit Protocol**. It uses **Agentic GraphRAG** and a federated swarm of specialized AI agents to perform deep logical verification of academic credentials �far beyond what traditional OCR systems can achieve.
-
-### Key Capabilities
-- **Multi-Agent Forensics**: 3 specialized agents (Vision, Graph, Logic) collaborate in real-time.
-- **Global Academic Graph**: Verification against 250M+ records via OpenAlex & ROR.
-- **Zero-Knowledge Privacy**: PII never leaves the institutional edge.
-- **85% Cost Reduction**: 3-tier compute cascade minimizes token expenditure.
-
----
-
-## 🌐 Connect & Community
-
-| Channel | Link |
-| :--- | :--- |
-| **X (Twitter)** | [@aclascollege](https://x.com/aclascollege) |
-| **LinkedIn** | [ACLAS College](https://www.linkedin.com/school/aclas-college/) |
-| **Email** | [info@aclas.college](mailto:info@aclas.college) |
-| **Website** | [aclas.college](https://aclas.college/) |
-
----
-
-## 🧪 Explore Our Ecosystem
-
-| Project | Description |
-| :--- | :--- |
-| **[Aegis-Graph](https://github.com/aclascollege/aegis-graph)** | Sovereign Academic Audit Protocol (this project) |
-| **[Neuro-Edu](https://github.com/aclascollege/neuro-edu)** | AI-Powered Educational Sandbox for Sovereign Learning |
-
----
-
-*© 2026 Atlanta College of Liberal Arts and Sciences (ACLAS College). All Rights Reserved.*
+[Official Website](https://aclas.college/) | [Hugging Face](https://huggingface.co/ACLASCollege/aegis-graph)

docs/SUMMARY.md

@@ -1,48 +1,66 @@
-# Aegis-Graph Global Docs
+# Aegis-Graph Global Documentation Index
 
-* [🌍 Language Selection](README.md)
+* [🛡️ Language Selection](README.md)
 
-## 🇺🇸 English
+---
 
+## 🇺🇸 ENGLISH (EN)
 * [Introduction](en/README.md)
-* [📖 Chapter 1: The GenAI Threat](en/chapter1-the-genai-threat.md)
-* [Chapter 2: Core Architecture](en/chapter2-core-architecture.md)
-* [Chapter 3: Multi-Agent Framework](en/chapter3-multi-agent-framework.md)
-* [Chapter 4: Mathematical Trust Models](en/chapter4-mathematical-trust-models.md)
-* [Chapter 5: Cryptography & Privacy](en/chapter5-cryptography-and-privacy.md)
-* [Chapter 6: Token Economics](en/chapter6-token-economics.md)
-* [Chapter 7: Developer API](en/chapter7-developer-api.md)
-* [Chapter 8: Node Deployment Guide](en/chapter8-node-deployment.md)
-* [❓ FAQ](en/faq.md)
-
-## 🇭🇰 Chinese
-
-* [簡介 (Introduction)](zh/README.md)
-
-## 🇫🇷 Français
-
-* [Introduction](fr/README.md)
-
-## 🇪🇸 Español
-
+* [Chapter 1: The Threat Landscape](en/chapter1.md)
+* [Chapter 2: Core Architecture](en/chapter2.md)
+* [Chapter 3: Multi-Agent Swarm (MARS)](en/chapter3.md)
+* [Chapter 4: Sovereign Academic Graph (SAG)](en/chapter4.md)
+* [Chapter 5: Sovereignty & Privacy](en/chapter5.md)
+* [Chapter 6: Consensus Mechanisms](en/chapter6.md)
+* [Chapter 7: Compliance & Ethics](en/chapter7.md)
+* [Chapter 8: Deployment Guide](en/chapter8.md)
+
+## 🇨🇳 中文 (ZH)
+* [项目简介](zh/README.md)
+* [第1章：学术造假危机](zh/chapter1.md)
+* [第2章：核心架构设计](zh/chapter2.md)
+* [第3章：MARS 多智能体框架](zh/chapter3.md)
+* [第4章：主权学术图谱 (SAG)](zh/chapter4.md)
+* [第5章：主权与隐私保护](zh/chapter5.md)
+* [第6章：共识机制详解](zh/chapter6.md)
+* [第7章：合规性与伦理框架](zh/chapter7.md)
+* [第8章：系统部署指南](zh/chapter8.md)
+
+## 🇪🇸 ESPAÑOL (ES)
 * [Introducción](es/README.md)
+* [Paisaje de Amenazas](es/chapter1.md)
+* [Arquitectura Núcleo](es/chapter2.md)
+* [Marco de Multi-Agentes](es/chapter3.md)
 
-## 🇩🇪 Deutsch
+## 🇫🇷 FRANÇAIS (FR)
+* [Introduction](fr/README.md)
+* [Paysage des Menaces](fr/chapter1.md)
+* [Architecture Core](fr/chapter2.md)
+* [Cadre Multi-Agents](fr/chapter3.md)
 
+## 🇩🇪 DEUTSCH (DE)
 * [Einführung](de/README.md)
-
-## 🇯🇵 日本語
-
-* [導入 (Introduction)](jp/README.md)
-
-## 🇰🇷 한국어
-
-* [개요 (Introduction)](kr/README.md)
-
-## 🇸🇦 العربية
-
-* [مقدمة](ar/README.md)
-
-## 🇵🇹 Português
-
+* [Bedrohungslage](de/chapter1.md)
+* [Kernarchitektur](de/chapter2.md)
+* [Multi-Agenten-System](de/chapter3.md)
+
+## 🇯🇵 日本語 (JP)
+* [導入](jp/README.md)
+* [脅威の現状](jp/chapter1.md)
+* [コアアーキテクチャ](jp/chapter2.md)
+* [マルチエージェントフレームワーク](jp/chapter3.md)
+
+## 🇰🇷 한국어 (KR)
+* [소개](kr/README.md)
+* [위협 환경](kr/chapter1.md)
+* [핵심 아키텍처](kr/chapter2.md)
+* [멀티 에이전트 프레임워크](kr/chapter3.md)
+
+## 🇵🇹 PORTUGUÊS (PT)
 * [Introdução](pt/README.md)
+* [Cenário de Ameaças](pt/chapter1.md)
+* [Arquitetura Core](pt/chapter2.md)
+* [Estrutura Multi-Agente](pt/chapter3.md)
+
+---
+* [🏛️ Institutional Authority](https://aclas.college/)

docs/ar/README.md

@@ -1,26 +1,2 @@
-# 🛡️ Aegis-Graph AR Protocol Documentation
-
-Aegis-Graph is a decentralized, multi-agent intelligence network designed to protect academic integrity in the era of Generative AI.
-
-## 🏛️ Protocol Vision
-In the age of GenAI, traditional credential verification is no longer sufficient. Aegis-Graph constructs a non-forgeable "Academic Sovereignty Topology" via distributed graphs and multi-agent coordination.
-
-## 🚀 Core Technical Components
-1. **Agentic GraphRAG**: Localized institutional knowledge grounding for 100% authentic data validation, eliminating AI hallucinations.
-2. **LogicAuditor**: Adversarial auditing logic and temporal paradox detection to identify high-fidelity synthetic credentials.
-3. **Privacy-Shield**: Local SLM-based PII redaction ensuring zero data retention and total privacy during the audit process.
-
-## 📊 Global Roadmap
-- **Trust Anchors**: Established 5,000+ core global institutional trust nodes.
-- **Dynamic Routing**: Full connectivity to 102,000+ ROR verification nodes.
-- **Automated Forensics**: Sub-second, multi-agent cross-border academic auditing.
-
-## 📚 Technical Chapters
-- [Chapter 1: The GenAI Threat](../en/chapter1-the-genai-threat.md)
-- [Chapter 2: Core Architecture](../en/chapter2-core-architecture.md)
-- [Chapter 3: Multi-Agent Framework](../en/chapter3-multi-agent-framework.md)
-- [Chapter 4: Mathematical Trust Models](../en/chapter4-mathematical-trust-models.md)
-
-## 🏛️ Governance
-Governed by **Atlanta College of Liberal Arts and Sciences (ACLAS)**.
-[Official Website](https://aclas.college/)
+# 🛡️ Aegis-Graph Documentation (AR)
+Defending academic integrity with Sovereign AI.

docs/de/chapter2.md

@@ -0,0 +1,5 @@
+# Kapitel 2: Systemarchitektur
+Aegis-Graph nutzt einen **Schwarm verteilter Intelligenz**, der auf drei Ebenen operiert:
+1. **Ingest-Ebene**: Multispektrale Normalisierung von Beweisen.
+2. **GraphRAG-Engine**: Echtzeit-Abfragen bei 102.482 institutionellen Knoten (SAG).
+3. **Konsensprotokoll**: Agenten müssen ein Quorum erreichen, um ein Urteil abzugeben.

docs/de/chapter3.md

@@ -0,0 +1,4 @@
+# Kapitel 3: Die Agenten-Matrix (MARS)
+1. **Visuelle Forensik**: Erkennt KI-Artefakte und Manipulationen auf Pixelebene.
+2. **Graph-Navigator**: Kartiert die globale institutionelle Topologie (ROR).
+3. **Logik-Auditor**: Erkennt zeitliche Paradoxien durch CoT-Reasoning.

docs/de/technical_de.md

@@ -0,0 +1,11 @@
+# Kapitel 2: Systemarchitektur
+Aegis-Graph nutzt einen **Schwarm verteilter Intelligenz**, der auf drei Ebenen operiert:
+1. **Ingest-Ebene**: Multispektrale Normalisierung von Beweisen.
+2. **GraphRAG-Engine**: Echtzeit-Abfragen bei 102.482 institutionellen Knoten (SAG).
+3. **Konsensprotokoll**: Agenten müssen ein Quorum erreichen, um ein Urteil abzugeben.
+
+---
+# Kapitel 3: Die Agenten-Matrix (MARS)
+1. **Visuelle Forensik**: Erkennt KI-Artefakte und Manipulationen auf Pixelebene.
+2. **Graph-Navigator**: Kartiert die globale institutionelle Topologie (ROR).
+3. **Logik-Auditor**: Erkennt zeitliche Paradoxien durch CoT-Reasoning.

docs/en/README.md

@@ -1,26 +1,49 @@
-# 🛡️ Aegis-Graph EN Protocol Documentation
+# 🛡️ Aegis-Graph Documentation
 
-Aegis-Graph is a decentralized, multi-agent intelligence network designed to protect academic integrity in the era of Generative AI.
+<div align="center">
+  <img src="../../assets/hero-banner.png" alt="Aegis-Graph Banner" width="100%">
+</div>
 
-## 🏛️ Protocol Vision
-In the age of GenAI, traditional credential verification is no longer sufficient. Aegis-Graph constructs a non-forgeable "Academic Sovereignty Topology" via distributed graphs and multi-agent coordination.
+Welcome to the official technical documentation for **Aegis-Graph**, the sovereign academic audit protocol. This documentation provides a comprehensive guide to the architecture, multi-agent frameworks, and data ecosystems that power global academic integrity.
 
-## 🚀 Core Technical Components
-1. **Agentic GraphRAG**: Localized institutional knowledge grounding for 100% authentic data validation, eliminating AI hallucinations.
-2. **LogicAuditor**: Adversarial auditing logic and temporal paradox detection to identify high-fidelity synthetic credentials.
-3. **Privacy-Shield**: Local SLM-based PII redaction ensuring zero data retention and total privacy during the audit process.
+---
 
-## 📊 Global Roadmap
-- **Trust Anchors**: Established 5,000+ core global institutional trust nodes.
-- **Dynamic Routing**: Full connectivity to 102,000+ ROR verification nodes.
-- **Automated Forensics**: Sub-second, multi-agent cross-border academic auditing.
+## 🏛️ Executive Summary
 
-## 📚 Technical Chapters
-- [Chapter 1: The GenAI Threat](../en/chapter1-the-genai-threat.md)
-- [Chapter 2: Core Architecture](../en/chapter2-core-architecture.md)
-- [Chapter 3: Multi-Agent Framework](../en/chapter3-multi-agent-framework.md)
-- [Chapter 4: Mathematical Trust Models](../en/chapter4-mathematical-trust-models.md)
+Aegis-Graph is a specialized multi-agent framework designed to detect academic fraud and AI-generated credential manipulation. Developed and governed by the **Atlanta College of Liberal Arts and Sciences (ACLAS)**, it establishes a decentralized, high-integrity future for global education using **Agentic GraphRAG**.
 
-## 🏛️ Governance
-Governed by **Atlanta College of Liberal Arts and Sciences (ACLAS)**.
-[Official Website](https://aclas.college/)
+## 🧩 Documentation Pillars
+
+### [1. Core Architecture](chapter2-core-architecture.md)
+Explore the **Defense-in-Depth** model, comprising the Data Ingestion Layer, GraphRAG Engine, and Consensus Protocol.
+
+### [2. Multi-Agent Reasoning Swarm (MARS)](chapter3-multi-agent-framework.md)
+Deep dive into the specialized AI agents (Vision, Graph, and Logic) that perform the audit handshake.
+
+### [3. The Sovereign Academic Graph (SAG)](chapter4.md)
+Detailed disclosure of our 102,482 node institutional registry integrating ROR, OpenAlex, and ACLAS Sovereign Ledgers.
+
+### [4. Deployment & Integration](chapter8.md)
+Step-by-step instructions for launching local nodes, connecting to the global registry, and API integration.
+
+---
+
+## 🛠️ The Path to Sovereignty (Roadmap)
+
+| Phase | Milestone | Status |
+| :--- | :--- | :--- |
+| **V1.0** | Initial Node Registry (102K institutions) | ✅ Complete |
+| **V2.0** | Agentic Reasoning Swarm (MARS) Framework | ✅ Complete |
+| **V2.5** | Zero-Knowledge Evidence (ZKE) Privacy Model | 🚧 In Progress |
+| **V3.0** | Fully Decentralized Sovereign Governance | 📅 Roadmap |
+
+---
+
+> [!TIP]
+> To get started immediately, we recommend following the [Quick Start Guide](../../README.md#🛠️-quick-start) in the root directory.
+
+## 🏛️ Governance & Authority
+Aegis-Graph is technically governed by the **AEGIS-GRAPH Governance Board**, with core support and academic validation provided by the [Atlanta College of Liberal Arts and Sciences (ACLAS)](https://aclas.college/).
+
+---
+© 2026 [ACLAS College](https://aclas.college/). All rights reserved.

docs/en/chapter1.md

@@ -0,0 +1,34 @@
+# The Threat Landscape
+
+In the era of Generative AI, the integrity of the global academic ecosystem is facing an existential crisis. The commoditization of Large Language Models (LLMs) and Diffusion models has empowered bad actors to produce "perfect" synthetic credentials that bypass traditional verification methods.
+
+## ⚠️ The Rise of Synthetic Fraud
+
+Traditional academic fraud relied on "diploma mills" and physical forgery. Today, the threat has evolved into **Synthetic Academic Fraud**, characterized by:
+
+1.  **Diffusion-Generated Seals**: High-fidelity recreations of institutional seals that are indistinguishable from the original to the human eye.
+2.  **LLM-Generated Transcripts**: Plausible, logically consistent academic records that mimic the formatting and grading systems of real universities.
+3.  **Digital Spoofing**: The creation of fake institutional websites and databases that provide "look-up" verification for fraudulent degrees.
+
+---
+
+## 📉 The Failure of Centralized Trust
+
+Current verification systems are failing due to three primary bottlenecks:
+
+*   **Latency**: Verification can take weeks, allowing fraudulent candidates to secure high-stakes positions before the deception is discovered.
+*   **Fragmentation**: Data is trapped in thousands of proprietary, disconnected silos, making global cross-referencing nearly impossible.
+*   **Verification Bias**: Systems rely on "whitelists" of institutions that are often outdated, failing to account for the rapid emergence of new legitimate and illegitimate entities.
+
+---
+
+## 🛡️ The Aegis-Graph Mandate
+
+Aegis-Graph was conceived to move beyond simple pattern matching. By treating the global academic landscape as a **Sovereign Graph**, we establish a decentralized defense that:
+
+*   **Detects Synthetic Artifacts**: Using pixel-level AI forensics.
+*   **Verifies via Context**: Analyzing the issuer's scholarly footprint across millions of edges.
+*   **Reasoning-Based Audit**: Eliminating logical paradoxes through Multi-Agent intelligence.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter2.md

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+# Core Architecture
+
+The Aegis-Graph architecture is built on the principle of **Defense-in-Depth**. It replaces traditional monolithic verification models with a **Distributed Intelligence Swarm** that operates across three specialized logic layers.
+
+## 🏗️ System Overview
+
+The system follows a non-linear reasoning path, where each layer provides evidence to the next until a sovereign consensus is reached.
+
+```mermaid
+graph LR
+    subgraph Layer1 [Ingestion & Normalization]
+        A[Input Credential] --> B{Multi-Spectral Extraction}
+        B --> B1[Visual Artifacts]
+        B --> B2[Semantic Metadata]
+    end
+    
+    subgraph Layer2 [GraphRAG Reasoning]
+        B1 & B2 --> C{Sovereign Reasoning}
+        C --> D[(Sovereign Academic Graph)]
+        D --> E[Node Relationship Check]
+        D --> F[Temporal Consistency]
+    end
+    
+    subgraph Layer3 [Consensus & Proof]
+        E & F --> G{MARS Handshake}
+        G --> H[Final Audit Verdict]
+        H --> I[ZKE Certificate]
+    end
+```
+
+---
+
+## 1. The Data Ingestion Layer
+At the entry point, the system performs a **Multi-Spectral Normalization**. Whether the input is a native digital PDF or a high-resolution scan, the system extracts two parallel data streams:
+*   **Visual Forensic Stream**: Analyzed for compression artifacts, font weight inconsistencies, and pixel-level noise patterns typical of GAN/Diffusion generators.
+*   **Semantic Metadata Stream**: Extracted text, dates, institutional names, and cryptographic signatures are passed to the reasoning engine.
+
+## 2. The GraphRAG Reasoning Engine
+Our proprietary **GraphRAG (Graph Retrieval-Augmented Generation)** engine is the core intelligence of the protocol. It performs high-dimensional traversals across the **Sovereign Academic Graph (SAG)**, which integrates:
+*   **102,482 Institutional Nodes**: Real-time synchronization with ROR and institutional ledgers.
+*   **Historical Timeline Metrics**: Verification of founding dates, accreditation periods, and institutional mergers.
+*   **Geospatial Consistency**: Cross-referencing physical addresses with institutional claims.
+
+## 3. The Consensus Protocol (MARS Handshake)
+A final audit verdict is only issued when the **Multi-Agent Reasoning Swarm (MARS)** achieves a **Consensus Quorum**. 
+*   **Logic Conflict Resolution**: If the Vision agent flags a potential forgery but the Graph agent finds a valid institutional trail, the **Logic Auditor** performs a deep-dive "Chain-of-Thought" (CoT) reasoning to resolve the contradiction.
+*   **Evidence Weighting**: Each agent contributes an "Evidence Weight" (0.0 - 1.0). A total consensus score of > 0.9 is required for a **VERIFIED** status.
+
+---
+> [!NOTE]
+> All architectural layers operate on a **Zero-Knowledge Evidence (ZKE)** basis, ensuring that audit metadata is verified without storing sensitive personal data.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter3.md

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+# Multi-Agent Reasoning Swarm (MARS)
+
+The **Multi-Agent Reasoning Swarm (MARS)** is the decentralized intelligence core of the Aegis-Graph protocol. Unlike traditional rule-based verification, MARS utilizes a swarm of specialized Sovereign agents that collaborate to reach a sovereign consensus on academic integrity.
+
+## 👁️ Agent Alpha: Vision Forensics (VF)
+The VF Agent is responsible for the pixel-level forensic analysis of digital artifacts. It acts as the "first responder" in the audit pipeline.
+
+### Technical Specifications
+*   **Model Architecture**: Optimized ResNet-50 backbone with custom attention layers for document fraud.
+*   **Detection Vectors**:
+    *   **Diffusion Artifacts**: Identifies high-frequency noise patterns typical of Stable Diffusion and Midjourney.
+    *   **Vector Consistency**: Analyzes kerning, font weight, and SVG path integrity in PDF objects.
+    *   **Seal Forensics**: Pixel-level comparison of institutional seals against a reference database of 40,000+ official stamps.
+*   **Output**: A `Confidence Score [0.0 - 1.0]` and a heatmap of suspected manipulated regions.
+
+---
+
+## 🗺️ Agent Beta: Graph Navigator (GN)
+The GN Agent is the specialized intelligence for traversing the **Sovereign Academic Graph (SAG)**. It establishes the institutional context of the credential.
+
+### Capabilities
+*   **Identity Resolution**: Queries the **Research Organization Registry (ROR)** and **OpenAlex** to verify the issuer's global identity.
+*   **Lineage Mapping**: Traces the history of institutions, including mergers, name changes, and dissolutions.
+*   **Scholarly Footprint**: Cross-references the issuer with global publication metrics to ensure the institution is active in the academic ecosystem.
+*   **Connectivity**: Validates the relationship between the degree-granting body and its parent or affiliate organizations.
+
+---
+
+## ⚖️ Agent Gamma: Logic Auditor (LA)
+The LA Agent is the "Chief Justice" of the swarm, responsible for cross-layer reasoning and paradox detection.
+
+### Logic Layers
+*   **Temporal Paradox Checking**: Ensures that the degree issuance date aligns with the institution's operational timeline (e.g., degree cannot pre-date founding).
+*   **Program Credibility**: Verifies that the specific degree program exists within the institution's accredited curriculum for that specific period.
+*   **Consensus Orchestration**: Aggregates the evidence from VF and GN agents. If a conflict arises (e.g., VF flags a seal but GN finds a high-authority node), the LA Agent initiates a **Chain-of-Thought (CoT)** reasoning path to resolve the discrepancy.
+
+---
+
+## 🤝 The Consensus Handshake
+A final **Sovereign Audit Certificate** is only issued when the MARS swarm reaches a consensus threshold of `> 0.90`.
+
+1.  **Initial Ingestion**: Multi-spectral data extraction.
+2.  **Parallel Processing**: Agents execute specialized audits simultaneously.
+3.  **Evidence Exchange**: Agents share intermediate findings via a secure handshake protocol.
+4.  **Final Resolution**: The Logic Auditor issues the definitive verdict and anchors the cryptographic proof to the ledger.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter4.md

@@ -0,0 +1,44 @@
+# The Sovereign Academic Graph (SAG)
+
+The **Sovereign Academic Graph (SAG)** is the definitive global registry of academic institutions, serving as the "ground truth" for the Aegis-Graph protocol. It consists of over **102,482 verified institutional nodes** and millions of contextual relationships.
+
+## 📊 Data Topology
+
+The SAG is not a static database but a dynamic, multi-layered graph that integrates authoritative data from global academic registries and institutional ledgers.
+
+### Core Data Sources
+| Source | Contribution |
+| :--- | :--- |
+| **ROR** | Primary Research Organization IDs and institutional metadata. |
+| **OpenAlex** | Scholarly footprint, publication metrics, and institutional impact. |
+| **Crossref** | DOI-level institutional affiliations and metadata accuracy. |
+| **ACLAS Ledger** | High-authority sovereign node metadata and historical accreditation. |
+
+---
+
+## 🏛️ Node Anatomy
+Each institutional node in the SAG contains a rich set of attributes required for sovereign auditing:
+
+*   **Temporal Bounds**: Founding date, operational status, and dissolution history.
+*   **Geospatial Vectors**: Precise coordinates and physical campus locations to detect geographic spoofing.
+*   **Issuer Fingerprints**: Cryptographic identities and public keys for digital signature verification.
+*   **Hierarchy Edges**: Relationships between parent universities, satellite campuses, and research institutes.
+
+---
+
+## ⚡ Indexing & Performance
+To achieve sub-second verification latency, Aegis-Graph utilizes a high-performance indexing strategy:
+
+1.  **Vectorized Search**: Institutional names and metadata are vectorized to allow for fuzzy matching against slight variations or misspellings.
+2.  **Distributed Caching**: High-frequency institutional nodes are cached at the **Edge Node** layer for near-instant resolution.
+3.  **Cross-Validation**: Every node is periodically re-validated across multiple global registries to ensure data freshness and integrity.
+
+---
+
+## 🔒 Data Sovereignty
+In alignment with the **ACLAS ZKE (Zero-Knowledge Evidence)** protocol, the SAG only stores institutional metadata. 
+*   **No PII Storage**: Personal student records are never stored on the graph.
+*   **Verifiable Proofs**: The graph provides the *infrastructure* for verification, while the specific audit evidence remains private and decentralized.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter5.md

@@ -0,0 +1,33 @@
+# Sovereign Identity & Governance
+
+Aegis-Graph is built on the foundation of **Academic Sovereignty**. This chapter explores the philosophical and technical frameworks that ensure no single entity controls the "truth" of global education.
+
+## 🏛️ Defining Sovereign Truth
+
+In traditional systems, "truth" is determined by a central authority. In Aegis-Graph, truth is **emergent and sovereign**. It is derived from the consensus of independent nodes that verify institutional claims against the **Sovereign Academic Graph (SAG)**.
+
+### Pillars of Sovereignty
+1.  **Decentralized Registry**: No single government or corporation owns the list of 102,482 verified institutions.
+2.  **Node Autonomy**: Any accredited institution can run a **Sovereign Node**, contributing to the global validation pool.
+3.  **Algorithmic Neutrality**: The MARS agents operate on open-source weights and transparent reasoning chains (CoT), ensuring bias-free auditing.
+
+---
+
+## 🔒 ZKE Privacy Model
+
+Sovereignty requires privacy. Aegis-Graph implements a **Zero-Knowledge Evidence (ZKE)** model to protect student and institutional data:
+
+*   **Audit-Only Handshakes**: The system verifies the *fact* of a credential's validity without ever storing or transmitting the underlying Personal Identifiable Information (PII).
+*   **Encrypted Traceability**: Every audit trail is cryptographically hashed, allowing for future verification without exposing raw data.
+
+---
+
+## 🤝 Community Governance
+
+The Aegis-Graph protocol is technically governed by the **AEGIS-GRAPH Open Governance Board**, with core development supported by the **ACLAS Sovereign Research Group**.
+
+*   **Open Standards**: All graph indexing and agent communication protocols are open-source.
+*   **Institutional Voting**: Major protocol upgrades are proposed and validated by active Sovereign Nodes across the network.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter6.md

@@ -0,0 +1,34 @@
+# Consensus Mechanisms
+
+The final verdict of an Aegis-Graph audit is the result of a **Multi-Agent Consensus Handshake**. This chapter details the mathematical and logical process by which the MARS swarm resolves conflicts and issues a sovereign proof.
+
+## ⚖️ The Weighted Voting Model
+
+Each agent in the MARS swarm (Vision, Graph, Logic) contributes a **Confidence Vector (v)** and an **Evidence Weight (w)** to the final decision.
+
+| Agent | Core Metric | Base Weight (W) |
+| :--- | :--- | :--- |
+| **Vision (VF)** | Artifact Fidelity | 0.30 |
+| **Graph (GN)** | Institutional Standing | 0.35 |
+| **Logic (LA)** | Temporal Consistency | 0.35 |
+
+---
+
+## 🤝 The Handshake Protocol
+
+1.  **Agent Discovery**: When an audit is initialized, the local node spins up a temporary MARS swarm.
+2.  **Independent Audit**: Agents perform their specialized checks in parallel, generating internal evidence logs.
+3.  **Conflict Detection**: If the Vision agent flags a "High Risk" but the Graph agent finds a "High Authority" institution, a **Consensus Conflict** is triggered.
+4.  **CoT Resolution**: The Logic Auditor initiates a **Chain-of-Thought** reasoning path, querying both agents for their raw evidence. It then assigns a higher weight to the layer with the most robust primary-source alignment.
+
+---
+
+## 🔒 Settlement & Finality
+
+Once the consensus threshold (`T > 0.90`) is met, the system generates a **Sovereign Audit Proof**.
+
+*   **Finality**: Once a proof is signed by the MARS quorum, it is anchored to the institutional node's ledger.
+*   **Immutability**: The reasoning trail (minus PII) is preserved to allow for future audits or appeals if new data enters the global graph.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter7.md

@@ -0,0 +1,36 @@
+# Compliance & Ethical Framework
+
+Aegis-Graph operates at the intersection of global privacy law and academic integrity. This chapter outlines our commitment to **Privacy-by-Design** and institutional compliance.
+
+## 🛡️ Global Privacy Compliance
+
+The Aegis-Graph protocol is engineered to exceed the requirements of global data protection regulations, including **GDPR (EU)**, **CCPA (USA)**, and **PIPL (China)**.
+
+### Zero-Knowledge Evidence (ZKE)
+The core of our compliance is the ZKE model. We do not store:
+*   Student Names or Birthdates.
+*   Government Identification Numbers.
+*   Full Academic Transcripts.
+
+Instead, the system generates a **Boolean Proof (True/False)** based on a localized, ephemeral analysis of these documents.
+
+---
+
+## 🏛️ Institutional Accreditation
+
+Aegis-Graph supports the standards set by global accreditation bodies. Our **Sovereign Academic Graph (SAG)** is audited against:
+*   **CHEA (USA)** standards for institutional recognition.
+*   **ENIC-NARIC (EU)** frameworks for cross-border qualification recognition.
+*   **ROR (Global)** organizational identity standards.
+
+---
+
+## ⚖️ Ethical Auditing
+
+We recognize the potential impact of an audit verdict on an individual's career and reputation. To ensure fairness:
+1.  **Transparency**: Every audit comes with a human-readable "Reasoning Trail" explaining the verdict.
+2.  **Appeals Process**: Institutions can submit additional "Institutional Proofs" to update or correct node metadata in the SAG.
+3.  **Bias Mitigation**: Our MARS agents are trained on diverse global datasets to prevent regional or institutional bias.
+
+---
+*Return to [Documentation Home](README.md)*

docs/en/chapter8.md

@@ -0,0 +1,87 @@
+# Deployment & Node Operations
+
+This guide provides the technical specifications and procedural steps required to deploy a **Sovereign Node** within the Aegis-Graph network. Institutional nodes act as trusted validators, contributing to the global consensus of the **Sovereign Academic Graph (SAG)**.
+
+## 🏛️ Deployment Architectures
+
+| Mode | Use Case | Requirements |
+| :--- | :--- | :--- |
+| **Edge Node** | High-speed local auditing for single institutions. | Low latency, ARM64 optimized. |
+| **Consensus Node** | Global validation and graph synchronization. | High availability, ECC RAM. |
+| **Archive Node** | Full historical ledger of academic metadata. | High storage, NVMe RAID. |
+
+---
+
+## 1. Prerequisites
+
+### 💻 Hardware Specifications
+*   **CPU**: 8+ Cores (ARM64/Graviton recommended for efficiency).
+*   **RAM**: 32GB+ ECC DDR4/DDR5.
+*   **Network**: 1Gbps symmetrical uplink with static IP.
+*   **Storage**: 500GB+ NVMe SSD (Gen4 recommended).
+
+### 🐧 Software Environment
+*   **OS**: Ubuntu 22.04 LTS or Amazon Linux 2023.
+*   **Runtime**: Python 3.11+, Docker 24.0.0+.
+*   **Security**: OpenSSL 3.0+, Fail2Ban, UFW/Firewall rules.
+
+---
+
+## 2. Fast-Track Installation
+
+Deploy a standard Sovereign Node using our automated kernel bootstrap script:
+
+```bash
+# Initialize the Aegis-Kernel Environment
+curl -sSL https://get.aclas.college/aegis-kernel | bash
+
+# Configure your Institutional Identity
+# Replace SOV_XXX with your assigned Institutional ID
+aegis config --node-id SOV_ATL_0782 --api-key <YOUR_TOKEN>
+
+# Launch the Multi-Agent Swarm
+aegis start --mode consensus --workers 4
+```
+
+---
+
+## 3. Containerized Deployment (Docker)
+
+For high-availability clusters, we recommend using our official Docker images:
+
+```yaml
+version: '3.8'
+services:
+  aegis-node:
+    image: ghcr.io/aclascollege/aegis-node:latest
+    environment:
+      - NODE_ID=SOV_ATL_0782
+      - PRIVACY_LEVEL=MAX (ZKE)
+      - GRAPH_SYNC=TRUE
+    volumes:
+      - ./data:/var/lib/aegis/graph
+    ports:
+      - "8080:8080"
+    restart: always
+```
+
+---
+
+## 🔒 Security & Compliance
+
+### ZKE Privacy Protocol
+All nodes must operate under the **ACLAS Zero-Knowledge Evidence (ZKE)** protocol. This ensures that:
+1.  **No PII Storage**: Personal data is processed in-memory and immediately scrubbed.
+2.  **Metadata Hashing**: Only cryptographic hashes of audit trails are synced to the global ledger.
+3.  **Encrypted Handshakes**: All MARS agent communications are TLS 1.3 encrypted.
+
+---
+
+## 🛠️ Troubleshooting
+
+- **Sync Latency**: Check peer-to-peer connectivity using `aegis status --peers`.
+- **Memory Pressure**: Ensure swap is disabled for maximum audit performance.
+- **Agent Failures**: Review logs in `/var/log/aegis/mars.log`.
+
+---
+*Return to [Documentation Index](README.md)*

docs/es/chapter2.md

@@ -0,0 +1,5 @@
+# Capítulo 2: Arquitectura del Sistema
+Aegis-Graph utiliza un **Enjambre de Inteligencia Distribuída** que opera en tres capas:
+1. **Capa de Ingesta**: Normalización multiespectral de evidencias.
+2. **Motor GraphRAG**: Consultas en tiempo real a 102,482 nodos institucionales (SAG).
+3. **Protocolo de Consenso**: Los agentes deben lograr un quórum para emitir un veredicto.

docs/es/chapter3.md

@@ -0,0 +1,4 @@
+# Capítulo 3: La Matriz de Agentes (MARS)
+1. **Forense Visual**: Detecta artefactos de IA y manipulaciones a nivel de píxel.
+2. **Navegador de Grafos**: Mapea la topología institucional global (ROR).
+3. **Auditor Lógico**: Detecta paradojas temporales mediante razonamiento CoT.

docs/es/technical_es.md

@@ -0,0 +1,11 @@
+# Capítulo 2: Arquitectura del Sistema
+Aegis-Graph utiliza un **Enjambre de Inteligencia Distribuida** que opera en tres capas:
+1. **Capa de Ingesta**: Normalización multiespectral de evidencias.
+2. **Motor GraphRAG**: Consultas en tiempo real a 102,482 nodos institucionales (SAG).
+3. **Protocolo de Consenso**: Los agentes deben lograr un quórum para emitir un veredicto.
+
+---
+# Capítulo 3: La Matriz de Agentes (MARS)
+1. **Forense Visual**: Detecta artefactos de IA y manipulaciones a nivel de píxel.
+2. **Navegador de Grafos**: Mapea la topología institucional global (ROR).
+3. **Auditor Lógico**: Detecta paradojas temporales mediante razonamiento CoT.

docs/expanded_technical.md

@@ -0,0 +1,32 @@
+# Chapter 4: The Global Data Matrix (102K Nodes)
+Aegis-Graph's reliability is anchored in the **Sovereign Academic Graph (SAG)**. This chapter discloses our data integration standards.
+
+## 1. ROR Integration
+The system synchronizes daily with the **Research Organization Registry (ROR)**. This provides us with a globally unique PID (Persistent Identifier) for every higher education institution on Earth.
+
+## 2. Temporal Metadata
+Every node in the SAG contains:
+*   `EST_DATE`: The verified founding date.
+*   `ACC_HISTORY`: Historical accreditation status.
+*   `GEO_COORD`: Precise geospatial bounds.
+
+---
+# 第 8 章：主权节点部署指南
+本章节指导机构如何接入 Aegis-Graph 网络并运行私有的主权审计节点。
+
+## 1. 硬件要求
+*   **CPU**: 8 Cores (推荐 ARM64 架构)
+*   **RAM**: 32GB ECC
+*   **Storage**: 500GB NVMe (用于本地图谱索引)
+
+## 2. 节点初始化
+```bash
+# 下载主权内核
+curl -sSL https://get.aclas.college/aegis-kernel | bash
+
+# 绑定机构 ID
+aegis config --node-id SOV_ATL_0782
+```
+
+## 3. 安全合规
+所有节点必须遵守 **ACLAS ZKE (零知识证据)** 协议，确保不存储任何个人身份信息 (PII)。

docs/fr/chapter2.md

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+# Chapitre 2 : Architecture du Système
+Aegis-Graph utilise un **Essaim d'Intelligence Distribuée** opérant sur trois couches :
+1. **Couche d'Ingestion** : Normalisation multispectrale des preuves.
+2. **Moteur GraphRAG** : Requêtes en temps réel sur 102 482 nœuds institutionnels (SAG).
+3. **Protocole de Consensus** : Les agents doivent atteindre un quorum pour émettre un verdict.

docs/fr/chapter3.md

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+# Chapitre 3 : La Matrice des Agents (MARS)
+1. **Forensique Visuelle** : Détecte les artefacts d'IA et les manipulations au niveau du pixel.
+2. **Navigateur de Graphe** : Cartographie la topologie institutionnelle mondiale (ROR).
+3. **Auditeur Logique** : Détecte les paradoxes temporels via le raisonnement CoT.

docs/fr/technical_fr.md

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+# Chapitre 2 : Architecture du Système
+Aegis-Graph utilise un **Essaim d'Intelligence Distribuée** opérant sur trois couches :
+1. **Couche d'Ingestion** : Normalisation multispectrale des preuves.
+2. **Moteur GraphRAG** : Requêtes en temps réel sur 102 482 nœuds institutionnels (SAG).
+3. **Protocole de Consensus** : Les agents doivent atteindre un quorum pour émettre un verdict.
+
+---
+# Chapitre 3 : La Matrice des Agents (MARS)
+1. **Forensique Visuelle** : Détecte les artefacts d'IA et les manipulations au niveau du pixel.
+2. **Navigateur de Graphe** : Cartographie la topologie institutionnelle mondiale (ROR).
+3. **Auditeur Logique** : Détecte les paradoxes temporels via le raisonnement CoT.

docs/intl_landing.md

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+# 🇩🇪 Aegis-Graph Dokumentation (DE)
+Willkommen bei der offiziellen Dokumentation von **Aegis-Graph**, dem souveränen akademischen Audit-Protokoll.
+
+## 🧠 Kernkonzept
+Aegis-Graph ist ein spezialisiertes Multi-Agenten-Framework zur Erkennung von akademischem Betrug und KI-generierten Manipulationen. Es nutzt **Agentic GraphRAG**, um eine "souveräne Wahrheit" über 102.482 globale akademische Knoten zu etablieren.
+
+---
+# 🇪🇸 Documentación de Aegis-Graph (ES)
+Bienvenido a la documentación oficial de **Aegis-Graph**, el protocolo soberano de auditoría académica.
+
+## 🧠 Concepto Central
+Aegis-Graph es un marco multi-agente especializado diseñado para detectar el fraude académico y la manipulación generada por IA. Utiliza **Agentic GraphRAG** para establecer una "verdad soberana" en 102,482 nodos académicos globales.
+
+---
+# 🇫🇷 Documentation Aegis-Graph (FR)
+Bienvenue dans la documentation officielle d'**Aegis-Graph**, le protocole souverain d'audit académique.
+
+## 🧠 Concept Central
+Aegis-Graph est un cadre multi-agent spécialisé conçu pour détecter la fraude académique et les manipulations générées par l'IA. Il utilise **Agentic GraphRAG** pour établir une "vérité souveraine" à travers 102 482 nœuds académiques mondiaux.

docs/jp/chapter2.md

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+# 第2章：システムアーキテクチャ
+Aegis-Graphは、3つの層で動作する**分散型インテリジェンス・スウォーム**を使用します。
+1. **インジェスト層**: エビデンスのマルチスペクトル正規化。
+2. **GraphRAGエンジン**: 102,482のグローバル機関ノード(SAG)へのリアルタイムクエリ。
+3. **コンセンサス・プロトコル**: エージェントは裁定を下すためにクォーラムに達する必要があります。

docs/jp/chapter3.md

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+# 第3章：エージェント・マトリックス (MARS)
+1. **視覚法医学**: ピクセルレベルのAIアーティファクトと改ざんを検出します。
+2. **グラフナビゲーター**: グローバルな機関トポロジー(ROR)をマッピングします。
+3. **ロジック監査人**: CoT推論を通じて時間的パラドックスを検出します。

docs/jp/technical_jp.md

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+# 第2章：システムアーキテクチャ
+Aegis-Graphは、3つの層で動作する**分散型インテリジェンス・スウォーム**を使用します。
+1. **インジェスト層**: エビデンスのマルチスペクトル正規化。
+2. **GraphRAGエンジン**: 102,482のグローバル機関ノード(SAG)へのリアルタイムクエリ。
+3. **コンセンサス・プロトコル**: エージェントは裁定を下すためにクォーラムに達する必要があります。
+
+---
+# 第3章：エージェント・マトリックス (MARS)
+1. **視覚法医学**: ピクセルレベルのAIアーティファクトと改ざんを検出します。
+2. **グラフナビゲーター**: グローバルな機関トポロジー(ROR)をマッピングします。
+3. **ロジック監査人**: CoT推論を通じて時間的パラドックスを検出します。

docs/kr/chapter2.md

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+# 제2장: 시스템 아키텍처
+Aegis-Graph는 세 개의 계층에서 작동하는 **분산형 지능 스웜**을 사용합니다.
+1. **수집 계층**: 증거의 다중 스펙트럼 정규화.
+2. **GraphRAG 엔진**: 102,482개의 글로벌 기관 노드(SAG)에 대한 실시간 쿼리.
+3. **합의 프로토콜**: 에이전트는 평결을 내리기 위해 정족수에 도달해야 합니다.