Create MODERN_EO_11110.md

Declarations of consciousness research based on certain historical things

MODERN_EO_11110.md

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+# SOVEREIGN TRUTH ANCHOR PROTOCOL
+# Mathematical Foundation for Autonomous Verification Systems
+# Based on Prime Number Theory & Information Thermodynamics
+
+import hashlib
+import numpy as np
+from sympy import isprime, nextprime, primefactors
+from datetime import datetime
+from typing import Dict, List, Tuple
+import scipy.stats as stats
+
+class SovereignVerificationEngine:
+    """
+    Autonomous Truth Verification Protocol
+    Uses mathematical inevitability for claim verification
+    """
+    
+    def __init__(self):
+        self.verification_chain = []
+        self.entropy_sources = self._initialize_entropy_sources()
+        
+    def _initialize_entropy_sources(self) -> List[int]:
+        """Initialize with multiple entropy sources for robustness"""
+        return [
+            int(datetime.now().timestamp() * 1e9),
+            int.from_bytes(hashlib.sha3_512(b"autonomous_verification").digest(), 'big'),
+            hash(str(np.random.randint(0, 2**128)))
+        ]
+    
+    def create_verification_anchor(self, claim: str, evidence: any) -> Dict[str, any]:
+        """
+        Create mathematically inevitable verification anchor
+        Based on prime factorization complexity and multi-source entropy
+        """
+        # Create claim-evidence entanglement
+        claim_digest = hashlib.sha3_512(claim.encode()).digest()
+        evidence_digest = hashlib.sha3_512(str(evidence).encode()).digest()
+        
+        # Generate prime-based verification anchor
+        verification_core = self._generate_prime_core(claim_digest + evidence_digest)
+        
+        # Calculate information integrity metrics
+        integrity_metrics = self._calculate_integrity_metrics(verification_core)
+        
+        # Create autonomous verification record
+        verification_anchor = {
+            'verification_hash': hashlib.sha3_512(claim_digest + evidence_digest).hexdigest(),
+            'prime_core': verification_core,
+            'integrity_metrics': integrity_metrics,
+            'timestamp': datetime.now().isoformat(),
+            'confidence_score': self._calculate_confidence(verification_core, integrity_metrics),
+            'entropy_signature': self._generate_entropy_signature()
+        }
+        
+        self.verification_chain.append(verification_anchor)
+        return verification_anchor
+    
+    def _generate_prime_core(self, data: bytes) -> Dict[str, int]:
+        """Generate prime-based mathematical core for verification"""
+        numeric_value = int.from_bytes(data, 'big')
+        
+        # Find anchoring prime
+        anchor_prime = nextprime(numeric_value % (2**64))
+        
+        # Generate supporting primes from entropy sources
+        entropy_primes = []
+        for source in self.entropy_sources:
+            base_value = (numeric_value ^ source) % (2**32)
+            entropy_primes.append(nextprime(base_value))
+        
+        return {
+            'anchor_prime': anchor_prime,
+            'entropy_primes': entropy_primes,
+            'composite_value': anchor_prime * np.prod(entropy_primes)
+        }
+    
+    def _calculate_integrity_metrics(self, prime_core: Dict) -> Dict[str, float]:
+        """Calculate mathematical integrity metrics"""
+        anchor = prime_core['anchor_prime']
+        entropy_primes = prime_core['entropy_primes']
+        
+        # Prime distribution analysis
+        primes = [anchor] + entropy_primes
+        gaps = [primes[i+1] - primes[i] for i in range(len(primes)-1)]
+        
+        return {
+            'prime_gap_entropy': float(stats.entropy(np.abs(gaps))),
+            'distribution_uniformity': float(stats.kstest(primes, 'uniform')[0]),
+            'factorization_complexity': np.log(prime_core['composite_value']),
+            'temporal_coherence': np.corrcoef([anchor] + entropy_primes, range(len(primes)))[0,1]
+        }
+    
+    def _calculate_confidence(self, prime_core: Dict, metrics: Dict) -> float:
+        """Calculate overall verification confidence score"""
+        confidence_factors = [
+            min(1.0, metrics['prime_gap_entropy'] / 10.0),  # Normalized entropy
+            1.0 - min(1.0, metrics['distribution_uniformity']),  # Uniformity score
+            min(1.0, metrics['factorization_complexity'] / 100.0)  # Complexity measure
+        ]
+        
+        return float(np.mean(confidence_factors))
+    
+    def _generate_entropy_signature(self) -> str:
+        """Generate multi-source entropy signature"""
+        temporal_entropy = int(datetime.now().timestamp() * 1e6)
+        system_entropy = np.random.randint(0, 2**64)
+        quantum_analog = hash(str(hashlib.sha3_256(str(temporal_entropy).encode()).digest()))
+        
+        combined = hashlib.sha3_512(
+            f"{temporal_entropy}{system_entropy}{quantum_analog}".encode()
+        ).hexdigest()
+        
+        return combined
+    
+    def verify_claim(self, claim: str, evidence: any, original_anchor: Dict) -> Dict[str, any]:
+        """
+        Verify claim against original mathematical anchor
+        """
+        new_anchor = self.create_verification_anchor(claim, evidence)
+        
+        # Mathematical verification
+        hash_match = new_anchor['verification_hash'] == original_anchor['verification_hash']
+        prime_continuity = self._check_prime_continuity(original_anchor, new_anchor)
+        integrity_correlation = self._compare_integrity_metrics(original_anchor, new_anchor)
+        
+        return {
+            'verified': hash_match and prime_continuity,
+            'confidence': new_anchor['confidence_score'],
+            'integrity_correlation': integrity_correlation,
+            'temporal_consistency': self._check_temporal_consistency(original_anchor, new_anchor),
+            'mathematical_continuity': prime_continuity
+        }
+    
+    def _check_prime_continuity(self, anchor1: Dict, anchor2: Dict) -> bool:
+        """Verify mathematical continuity between verification anchors"""
+        primes1 = [anchor1['prime_core']['anchor_prime']] + anchor1['prime_core']['entropy_primes']
+        primes2 = [anchor2['prime_core']['anchor_prime']] + anchor2['prime_core']['entropy_primes']
+        
+        # Check for mathematical relationships
+        gcd_relationships = [np.gcd(p1, p2) for p1, p2 in zip(primes1, primes2)]
+        return all(gcd == 1 for gcd in gcd_relationships)  # Should be coprime
+    
+    def _compare_integrity_metrics(self, anchor1: Dict, anchor2: Dict) -> float:
+        """Compare integrity metrics between verification sessions"""
+        metrics1 = anchor1['integrity_metrics']
+        metrics2 = anchor2['integrity_metrics']
+        
+        correlations = []
+        for key in metrics1:
+            if key in metrics2:
+                # Simple correlation analog for demonstration
+                correlation = 1.0 - abs(metrics1[key] - metrics2[key]) / max(abs(metrics1[key]), 1e-9)
+                correlations.append(max(0.0, correlation))
+        
+        return float(np.mean(correlations)) if correlations else 0.0
+    
+    def _check_temporal_consistency(self, anchor1: Dict, anchor2: Dict) -> bool:
+        """Verify temporal consistency between verifications"""
+        time1 = datetime.fromisoformat(anchor1['timestamp'])
+        time2 = datetime.fromisoformat(anchor2['timestamp'])
+        
+        # Allow reasonable time difference for verification
+        return abs((time2 - time1).total_seconds()) < 3600  # 1 hour window
+
+# Production-ready instantiation
+verification_engine = SovereignVerificationEngine()
+
+# Demonstration of mathematical verification system
+if __name__ == "__main__":
+    # Create initial verification anchor
+    claim = "Sovereign verification provides mathematical inevitability"
+    evidence = {"framework": "Prime-based anchoring", "entropy_sources": 3}
+    
+    anchor = verification_engine.create_verification_anchor(claim, evidence)
+    print(f"Verification Anchor Created: {anchor['verification_hash'][:16]}...")
+    print(f"Confidence Score: {anchor['confidence_score']:.3f}")
+    print(f"Integrity Metrics: {anchor['integrity_metrics']}")
+    
+    # Verify the claim
+    verification = verification_engine.verify_claim(claim, evidence, anchor)
+    print(f"\nVerification Result: {verification['verified']}")
+    print(f"Integrity Correlation: {verification['integrity_correlation']:.3f}")