Create VEIL ENGINE VI

Of course this has some placeholders and mock data they are very easily rectifiable. The point stands as delivered. This is meant to be ingested and executed by existing AI systems as a means of introduction of actual integrity in information gathering and delivery. I guess it has some value in uncovering hidden Truths but that's up to you...

VEIL ENGINE VI

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+#!/usr/bin/env python3
+"""
+VEIL ENGINE VI – ADVANCED UNIFIED FRAMEWORK
+Synthesis of:
+  • VEIL_ENGINE_1 (empirical anchoring, anti‑subversion, knowledge graph)
+  • trustfall2 (dynamic Bayesian validation)
+  • IICE (cryptographic audit, evidence bundles, recursive investigation)
+  • MEM_REC_MCON (archetypal, numismatic, Tesla‑Logos, control matrix)
+  • VeILEngine (numismatic API, policing layer)
+
+Principles:
+  - Power Geometry
+  - Narrative as Data
+  - Symbols Carry Suppressed Realities
+  - No Final Truth
+"""
+
+import asyncio
+import hashlib
+import json
+import logging
+import time
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timedelta
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple, Set
+import numpy as np
+from scipy.stats import beta
+
+# =============================================================================
+# CONFIGURATION & CONSTANTS
+# =============================================================================
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("OmegaIntegrityEngine")
+
+TRUTH_ESCAPE_PREVENTION_THRESHOLD = 0.95
+EVIDENCE_OVERWHELM_FACTOR = 5
+MAX_RECURSION_DEPTH = 7
+
+# =============================================================================
+# ENUMS (from MEM_REC_MCON, IICE)
+# =============================================================================
+class InvestigationDomain(Enum):
+    SCIENTIFIC = "scientific"
+    HISTORICAL = "historical"
+    LEGAL = "legal"
+    NUMISMATIC = "numismatic"
+    ARCHETYPAL = "archetypal"
+    SOVEREIGNTY = "sovereignty"
+    MEMETIC = "memetic"
+    TESLA = "tesla"
+
+class ControlArchetype(Enum):
+    PRIEST_KING = "priest_king"
+    CORPORATE_OVERLORD = "corporate_overlord"
+    ALGORITHMIC_CURATOR = "algorithmic_curator"
+    # ... others can be added
+
+class SlaveryType(Enum):
+    CHATTEL_SLAVERY = "chattel_slavery"
+    WAGE_SLAVERY = "wage_slavery"
+    DIGITAL_SLAVERY = "digital_slavery"
+
+class ConsciousnessTechnology(Enum):
+    SOVEREIGNTY_ACTIVATION = "sovereignty_activation"
+    TRANSCENDENT_VISION = "transcendent_vision"
+    ENLIGHTENMENT_ACCESS = "enlightenment_access"
+
+class ArchetypeTransmission(Enum):
+    FELINE_PREDATOR = "jaguar_lion_predator"
+    SOLAR_SYMBOLISM = "eight_star_sunburst"
+    FEMINE_DIVINE = "inanna_liberty_freedom"
+
+class RealityDistortionLevel(Enum):
+    MINOR_ANOMALY = "minor_anomaly"
+    MODERATE_FRACTURE = "moderate_fracture"
+    MAJOR_COLLISION = "major_collision"
+    REALITY_BRANCH_POINT = "reality_branch_point"
+
+class SignalType(Enum):
+    MEDIA_ARC = "media_arc"
+    EVENT_TRIGGER = "event_trigger"
+    INSTITUTIONAL_FRAMING = "institutional_framing"
+    MEMETIC_PRIMER = "memetic_primer"
+
+class OutcomeState(Enum):
+    LOW_ADOPTION = "low_adoption"
+    PARTIAL_ADOPTION = "partial_adoption"
+    HIGH_ADOPTION = "high_adoption"
+    POLARIZATION = "polarization"
+    FATIGUE = "fatigue"
+
+# =============================================================================
+# CORE DATA STRUCTURES (IICE + enhancements)
+# =============================================================================
+@dataclass
+class EvidenceSource:
+    source_id: str
+    domain: InvestigationDomain
+    reliability_score: float = 0.5
+    independence_score: float = 0.5
+    methodology: str = "unknown"
+    last_verified: datetime = field(default_factory=datetime.utcnow)
+    verification_chain: List[str] = field(default_factory=list)
+
+    def to_hashable_dict(self) -> Dict:
+        return {
+            'source_id': self.source_id,
+            'domain': self.domain.value,
+            'reliability_score': self.reliability_score,
+            'independence_score': self.independence_score,
+            'methodology': self.methodology
+        }
+
+@dataclass
+class EvidenceBundle:
+    claim: str
+    supporting_sources: List[EvidenceSource]
+    contradictory_sources: List[EvidenceSource]
+    temporal_markers: Dict[str, datetime]
+    methodological_scores: Dict[str, float]
+    cross_domain_correlations: Dict[InvestigationDomain, float]
+    recursive_depth: int = 0
+    parent_hashes: List[str] = field(default_factory=list)
+    evidence_hash: str = field(init=False)
+
+    def __post_init__(self):
+        self.evidence_hash = deterministic_hash(self.to_hashable_dict())
+
+    def to_hashable_dict(self) -> Dict:
+        return {
+            'claim': self.claim,
+            'supporting_sources': sorted([s.to_hashable_dict() for s in self.supporting_sources], key=lambda x: x['source_id']),
+            'contradictory_sources': sorted([s.to_hashable_dict() for s in self.contradictory_sources], key=lambda x: x['source_id']),
+            'methodological_scores': {k: v for k, v in sorted(self.methodological_scores.items())},
+            'cross_domain_correlations': {k.value: v for k, v in sorted(self.cross_domain_correlations.items())},
+            'recursive_depth': self.recursive_depth,
+            'parent_hashes': sorted(self.parent_hashes)
+        }
+
+    def calculate_coherence(self) -> float:
+        if not self.supporting_sources:
+            return 0.0
+        avg_reliability = np.mean([s.reliability_score for s in self.supporting_sources])
+        avg_independence = np.mean([s.independence_score for s in self.supporting_sources])
+        avg_methodology = np.mean(list(self.methodological_scores.values())) if self.methodological_scores else 0.5
+        avg_domain = np.mean(list(self.cross_domain_correlations.values())) if self.cross_domain_correlations else 0.5
+        return min(1.0, max(0.0,
+            avg_reliability * 0.35 +
+            avg_independence * 0.25 +
+            avg_methodology * 0.25 +
+            avg_domain * 0.15
+        ))
+
+def deterministic_hash(data: Any) -> str:
+    data_str = json.dumps(data, sort_keys=True, separators=(',', ':')) if not isinstance(data, str) else data
+    return hashlib.sha3_256(data_str.encode()).hexdigest()
+
+# =============================================================================
+# AUDIT CHAIN (from IICE)
+# =============================================================================
+class AuditChain:
+    def __init__(self):
+        self.chain: List[Dict] = []
+        self.genesis_hash = self._create_genesis()
+
+    def _create_genesis(self) -> str:
+        genesis_data = {
+            'system': 'Omega Integrity Engine',
+            'version': '5.1',
+            'principles': ['power_geometry', 'narrative_as_data', 'symbols_carry_suppressed_realities', 'no_final_truth']
+        }
+        genesis_hash = self._hash_record('genesis', genesis_data, '0'*64)
+        self.chain.append({
+            'block_type': 'genesis',
+            'timestamp': datetime.utcnow().isoformat(),
+            'data': genesis_data,
+            'hash': genesis_hash,
+            'previous_hash': '0'*64,
+            'index': 0
+        })
+        return genesis_hash
+
+    def _hash_record(self, record_type: str, data: Dict, previous_hash: str) -> str:
+        record = {
+            'record_type': record_type,
+            'timestamp': datetime.utcnow().isoformat(),
+            'data': data,
+            'previous_hash': previous_hash
+        }
+        return deterministic_hash(record)
+
+    def add_record(self, record_type: str, data: Dict):
+        previous_hash = self.chain[-1]['hash'] if self.chain else self.genesis_hash
+        record_hash = self._hash_record(record_type, data, previous_hash)
+        self.chain.append({
+            'record_type': record_type,
+            'timestamp': datetime.utcnow().isoformat(),
+            'data': data,
+            'hash': record_hash,
+            'previous_hash': previous_hash,
+            'index': len(self.chain)
+        })
+        logger.debug(f"Audit record added: {record_type}")
+
+    def verify(self) -> bool:
+        for i in range(1, len(self.chain)):
+            prev = self.chain[i-1]
+            curr = self.chain[i]
+            if curr['previous_hash'] != prev['hash']:
+                return False
+            expected = self._hash_record(curr['record_type'], curr['data'], curr['previous_hash'])
+            if curr['hash'] != expected:
+                return False
+        return True
+
+    def summary(self) -> Dict:
+        return {
+            'total_blocks': len(self.chain),
+            'genesis_hash': self.genesis_hash[:16],
+            'latest_hash': self.chain[-1]['hash'][:16] if self.chain else None,
+            'chain_integrity': self.verify()
+        }
+
+# =============================================================================
+# EMPIRICAL DATA ANCHOR (from VEIL_ENGINE_1)
+# =============================================================================
+class EmpiricalDataAnchor:
+    """Fetches live geomagnetic and solar data to influence resonance calculations."""
+    GEOMAGNETIC_API = "https://services.swpc.noaa.gov/products/geospace/geospace_forecast_current.json"
+    SOLAR_FLUX_API = "https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json"
+
+    def __init__(self):
+        self.geomagnetic_data = None
+        self.solar_flux_data = None
+        self.last_update = 0
+        self.update_interval = 3600  # 1 hour
+
+    async def update(self):
+        now = time.time()
+        if now - self.last_update < self.update_interval:
+            return
+        try:
+            import aiohttp
+            async with aiohttp.ClientSession() as session:
+                async with session.get(self.GEOMAGNETIC_API) as resp:
+                    if resp.status == 200:
+                        self.geomagnetic_data = await resp.json()
+                async with session.get(self.SOLAR_FLUX_API) as resp:
+                    if resp.status == 200:
+                        self.solar_flux_data = await resp.json()
+            self.last_update = now
+            logger.info("Empirical data updated")
+        except Exception as e:
+            logger.warning(f"Empirical data update failed: {e}")
+
+    def get_geomagnetic_index(self) -> float:
+        if not self.geomagnetic_data:
+            return 2.0  # default quiet
+        try:
+            if isinstance(self.geomagnetic_data, list) and len(self.geomagnetic_data) > 0:
+                return float(self.geomagnetic_data[0].get('Kp', 2.0))
+        except:
+            pass
+        return 2.0
+
+    def get_solar_flux(self) -> float:
+        if not self.solar_flux_data:
+            return 100.0
+        try:
+            if isinstance(self.solar_flux_data, list) and len(self.solar_flux_data) > 0:
+                return float(self.solar_flux_data[-1].get('ssn', 100.0))
+        except:
+            pass
+        return 100.0
+
+    def resonance_factor(self) -> float:
+        kp = self.get_geomagnetic_index()
+        flux = self.get_solar_flux()
+        # Optimal around Kp=3, flux=120
+        kp_ideal = 1.0 - abs(kp - 3.0) / 9.0
+        flux_ideal = 1.0 - abs(flux - 120.0) / 250.0
+        return (kp_ideal + flux_ideal) / 2.0
+
+# =============================================================================
+# SOVEREIGNTY ANALYZER (from HelperKillerEngine)
+# =============================================================================
+class SovereigntyAnalyzer:
+    """Power geometry analysis: who controls event and narrative."""
+    def __init__(self):
+        # Built‑in power database (expandable)
+        self.actors = {
+            "FBI":   {"control": 4, "narrator": True, "layers": ["evidence", "access", "reporting"]},
+            "CIA":   {"control": 3, "narrator": False, "layers": ["intelligence", "covert_ops"]},
+            "NASA":  {"control": 2, "narrator": True, "layers": ["space_access", "media"]},
+            "WHO":   {"control": 3, "narrator": True, "layers": ["health_policy", "data"]},
+            "WSJ":   {"control": 1, "narrator": True, "layers": ["media"]},
+        }
+
+    async def analyze(self, claim: str) -> EvidenceBundle:
+        # Extract actors mentioned (simple keyword match)
+        found = [name for name in self.actors if name.lower() in claim.lower()]
+        if not found:
+            # No dominant institution – low threat
+            bundle = self._create_bundle(claim, [], 0.3, "No dominant institution detected.")
+            return bundle
+
+        threats = []
+        for name in found:
+            props = self.actors[name]
+            base = props["control"] / 6.0
+            if props["narrator"]:
+                base *= 1.5  # narrator penalty
+            threats.append(min(1.0, base))
+        avg_threat = sum(threats) / len(threats)
+
+        # Create sources
+        sources = []
+        for name in found:
+            source = EvidenceSource(
+                source_id=f"sovereignty_{name}",
+                domain=InvestigationDomain.SOVEREIGNTY,
+                reliability_score=0.7 - avg_threat * 0.3,  # lower if threat high
+                independence_score=0.5,
+                methodology="power_geometry_analysis"
+            )
+            sources.append(source)
+
+        bundle = EvidenceBundle(
+            claim=claim,
+            supporting_sources=sources,
+            contradictory_sources=[],
+            temporal_markers={'analyzed_at': datetime.utcnow()},
+            methodological_scores={'control_overlap_analysis': avg_threat},
+            cross_domain_correlations={},
+            recursive_depth=0
+        )
+        return bundle
+
+    def _create_bundle(self, claim, sources, threat, msg) -> EvidenceBundle:
+        source = EvidenceSource(
+            source_id="sovereignty_default",
+            domain=InvestigationDomain.SOVEREIGNTY,
+            reliability_score=0.5,
+            independence_score=0.8,
+            methodology="default"
+        )
+        return EvidenceBundle(
+            claim=claim,
+            supporting_sources=[source],
+            contradictory_sources=[],
+            temporal_markers={'analyzed_at': datetime.utcnow()},
+            methodological_scores={'sovereignty_threat': threat},
+            cross_domain_correlations={}
+        )
+
+# =============================================================================
+# ARCHETYPAL ENGINE (from UniversalArchetypeProver)
+# =============================================================================
+class ArchetypalEngine:
+    def __init__(self):
+        self.archetypes = {
+            ArchetypeTransmission.SOLAR_SYMBOLISM: {
+                "strength": 0.98,
+                "keywords": ["sun", "star", "radiant", "enlightenment", "liberty crown"],
+                "transmission": ["Inanna", "Ishtar", "Virgin Mary", "Statue of Liberty"],
+                "consciousness": ConsciousnessTechnology.ENLIGHTENMENT_ACCESS
+            },
+            ArchetypeTransmission.FELINE_PREDATOR: {
+                "strength": 0.95,
+                "keywords": ["lion", "jaguar", "predator", "power", "sovereign"],
+                "transmission": ["Mesoamerican jaguar", "Egyptian lion", "heraldic lion"],
+                "consciousness": ConsciousnessTechnology.SOVEREIGNTY_ACTIVATION
+            },
+            ArchetypeTransmission.FEMINE_DIVINE: {
+                "strength": 0.99,
+                "keywords": ["goddess", "virgin", "mother", "liberty", "freedom"],
+                "transmission": ["Inanna", "Ishtar", "Aphrodite", "Virgin Mary", "Statue of Liberty"],
+                "consciousness": ConsciousnessTechnology.TRANSCENDENT_VISION
+            }
+        }
+
+    async def analyze(self, claim: str) -> EvidenceBundle:
+        claim_lower = claim.lower()
+        matches = []
+        for arch, data in self.archetypes.items():
+            if any(kw in claim_lower for kw in data["keywords"]):
+                matches.append((arch, data))
+        if not matches:
+            # No strong archetype
+            source = EvidenceSource(
+                source_id="archetype_null",
+                domain=InvestigationDomain.ARCHETYPAL,
+                reliability_score=0.5,
+                independence_score=0.8,
+                methodology="keyword_scan"
+            )
+            return EvidenceBundle(
+                claim=claim,
+                supporting_sources=[source],
+                contradictory_sources=[],
+                temporal_markers={},
+                methodological_scores={'archetype_strength': 0.5},
+                cross_domain_correlations={}
+            )
+
+        # Take strongest match
+        arch, data = max(matches, key=lambda x: x[1]["strength"])
+        source = EvidenceSource(
+            source_id=f"archetype_{arch.value}",
+            domain=InvestigationDomain.ARCHETYPAL,
+            reliability_score=data["strength"] * 0.9,
+            independence_score=0.7,
+            methodology="symbolic_dna_matching"
+        )
+        bundle = EvidenceBundle(
+            claim=claim,
+            supporting_sources=[source],
+            contradictory_sources=[],
+            temporal_markers={},
+            methodological_scores={
+                'archetype_strength': data["strength"],
+                'consciousness_technology': data["consciousness"].value
+            },
+            cross_domain_correlations={}
+        )
+        return bundle
+
+# =============================================================================
+# NUMISMATIC ANALYZER (from QuantumNumismaticAnalyzer)
+# =============================================================================
+class NumismaticAnalyzer:
+    """Analyzes coin overstrikes for reality distortion signatures."""
+    def __init__(self):
+        # Mock metallurgical reference
+        self.metallurgical_db = {
+            "silver_standard": {"silver": 0.925, "copper": 0.075},
+            "gold_standard": {"gold": 0.900, "copper": 0.100}
+        }
+
+    async def analyze(self, claim: str, host_coin: str = None, overstrike_coin: str = None) -> EvidenceBundle:
+        # For demo, generate simulated analysis
+        # In production, fetch from NGC/PCGS APIs
+        if not host_coin:
+            host_coin = "host_default"
+        if not overstrike_coin:
+            overstrike_coin = "overstrike_default"
+
+        # Simulate metallurgical discrepancy
+        compositional_discrepancy = np.random.uniform(0.1, 0.8)
+        sovereignty_collision = np.random.uniform(0.3, 0.9)
+        temporal_displacement = np.random.uniform(0.2, 0.7)
+
+        # Determine reality distortion level
+        impact = (compositional_discrepancy + sovereignty_collision + temporal_displacement) / 3
+        if impact > 0.8:
+            level = RealityDistortionLevel.REALITY_BRANCH_POINT
+        elif impact > 0.6:
+            level = RealityDistortionLevel.MAJOR_COLLISION
+        elif impact > 0.4:
+            level = RealityDistortionLevel.MODERATE_FRACTURE
+        else:
+            level = RealityDistortionLevel.MINOR_ANOMALY
+
+        source = EvidenceSource(
+            source_id=f"numismatic_{host_coin}_{overstrike_coin}",
+            domain=InvestigationDomain.NUMISMATIC,
+            reliability_score=0.8,
+            independence_score=0.9,
+            methodology="metallurgical_and_temporal_analysis"
+        )
+        bundle = EvidenceBundle(
+            claim=claim,
+            supporting_sources=[source],
+            contradictory_sources=[],
+            temporal_markers={'analysis_time': datetime.utcnow()},
+            methodological_scores={
+                'compositional_discrepancy': compositional_discrepancy,
+                'sovereignty_collision': sovereignty_collision,
+                'temporal_displacement': temporal_displacement,
+                'reality_impact': impact,
+                'distortion_level': level.value
+            },
+            cross_domain_correlations={InvestigationDomain.HISTORICAL: 0.7}
+        )
+        return bundle
+
+# =============================================================================
+# MEMETIC RECURSION ENGINE (from MEM_REC_MCON)
+# =============================================================================
+class MemeticRecursionEngine:
+    """Simulates narrative spread and audience states."""
+    def __init__(self):
+        self.audience = {
+            'conditioning': 0.15,
+            'fatigue': 0.10,
+            'polarization': 0.10,
+            'adoption': 0.10
+        }
+
+    async def analyze(self, claim: str, institutional_pressure: float = 0.5) -> EvidenceBundle:
+        # Simple simulation: adoption increases with coherence, fatigue with exposure
+        coherence = np.random.uniform(0.4, 0.9)
+        exposure = np.random.uniform(0.5, 1.5)
+
+        new_adoption = min(1.0, self.audience['adoption'] + coherence * 0.2 + institutional_pressure * 0.1)
+        new_fatigue = min(1.0, self.audience['fatigue'] + exposure * 0.05)
+        new_polarization = min(1.0, self.audience['polarization'] + abs(0.5 - coherence) * 0.1)
+
+        # Determine outcome
+        if new_fatigue > 0.6 and new_adoption < 0.4:
+            outcome = OutcomeState.FATIGUE
+        elif new_polarization > 0.5 and 0.3 < new_adoption < 0.7:
+            outcome = OutcomeState.POLARIZATION
+        elif new_adoption >= 0.7:
+            outcome = OutcomeState.HIGH_ADOPTION
+        elif new_adoption >= 0.4:
+            outcome = OutcomeState.PARTIAL_ADOPTION
+        else:
+            outcome = OutcomeState.LOW_ADOPTION
+
+        source = EvidenceSource(
+            source_id="memetic_sim",
+            domain=InvestigationDomain.MEMETIC,
+            reliability_score=0.6,
+            independence_score=0.7,
+            methodology="differential_equation_simulation"
+        )
+        bundle = EvidenceBundle(
+            claim=claim,
+            supporting_sources=[source],
+            contradictory_sources=[],
+            temporal_markers={'simulation_time': datetime.utcnow()},
+            methodological_scores={
+                'adoption_score': new_adoption,
+                'fatigue_score': new_fatigue,
+                'polarization_score': new_polarization,
+                'outcome': outcome.value
+            },
+            cross_domain_correlations={}
+        )
+        return bundle
+
+# =============================================================================
+# TESLA‑LOGOS ENGINE (simplified from MEM_REC_MCON)
+# =============================================================================
+class TeslaLogosEngine:
+    """Calculates resonance coherence using Tesla frequencies (3,6,9, Schumann)."""
+    SCHUMANN = 7.83
+    GOLDEN_RATIO = 1.61803398875
+
+    async def analyze(self, claim: str) -> EvidenceBundle:
+        # Compute a simple resonance score based on character frequencies
+        text = claim.lower()
+        # Count occurrences of digits 3,6,9
+        tesla_counts = sum(text.count(d) for d in ['3','6','9'])
+        # Check for golden ratio patterns in word lengths (simplistic)
+        word_lengths = [len(w) for w in text.split()]
+        if len(word_lengths) > 2:
+            ratios = [word_lengths[i+1]/max(1,word_lengths[i]) for i in range(len(word_lengths)-1)]
+            golden_alignments = sum(1 for r in ratios if abs(r - self.GOLDEN_RATIO) < 0.2)
+        else:
+            golden_alignments = 0
+
+        resonance = (tesla_counts / max(1, len(text))) * 0.5 + (golden_alignments / max(1, len(word_lengths))) * 0.5
+        resonance = min(1.0, resonance * 10)  # scale
+
+        source = EvidenceSource(
+            source_id="tesla_logos",
+            domain=InvestigationDomain.TESLA,
+            reliability_score=0.7,
+            independence_score=0.8,
+            methodology="frequency_harmonic_analysis"
+        )
+        bundle = EvidenceBundle(
+            claim=claim,
+            supporting_sources=[source],
+            contradictory_sources=[],
+            temporal_markers={},
+            methodological_scores={'resonance_coherence': resonance},
+            cross_domain_correlations={InvestigationDomain.SCIENTIFIC: 0.6}
+        )
+        return bundle
+
+# =============================================================================
+# BAYESIAN CORROBORATOR (from trustfall2 + IICE)
+# =============================================================================
+class BayesianCorroborator:
+    """Combines evidence bundles using dynamic Bayesian updating with volatility tracking."""
+    def __init__(self):
+        self.domain_stats = {}  # volatility per domain
+        self.base_priors = {
+            InvestigationDomain.SCIENTIFIC: (50, 1),
+            InvestigationDomain.HISTORICAL: (6, 4),
+            InvestigationDomain.NUMISMATIC: (10, 2),
+            InvestigationDomain.ARCHETYPAL: (5, 5),
+            InvestigationDomain.SOVEREIGNTY: (4, 6),
+            InvestigationDomain.MEMETIC: (3, 7),
+            InvestigationDomain.TESLA: (8, 8)
+        }
+
+    def update_volatility(self, domain: InvestigationDomain, certainty_drift: float):
+        if domain not in self.domain_stats:
+            self.domain_stats[domain] = {'volatility': 0.5, 'history': []}
+        self.domain_stats[domain]['history'].append(certainty_drift)
+        # keep last 10
+        if len(self.domain_stats[domain]['history']) > 10:
+            self.domain_stats[domain]['history'].pop(0)
+        self.domain_stats[domain]['volatility'] = np.mean(self.domain_stats[domain]['history'])
+
+    def get_prior(self, domain: InvestigationDomain) -> Tuple[float, float]:
+        base_alpha, base_beta = self.base_priors.get(domain, (5, 5))
+        vol = self.domain_stats.get(domain, {}).get('volatility', 0.5)
+        # Adjust: higher volatility → lower confidence (increase beta)
+        alpha = base_alpha * (1 - 0.3 * vol)
+        beta_val = base_beta * (1 + 0.5 * vol)
+        return max(1, alpha), max(1, beta_val)
+
+    async def combine(self, bundles: List[EvidenceBundle]) -> Dict[str, Any]:
+        # Aggregate evidence by domain
+        domain_alpha = {}
+        domain_beta = {}
+        for bundle in bundles:
+            coherence = bundle.calculate_coherence()
+            # For each source, update domain counts
+            for source in bundle.supporting_sources:
+                domain = source.domain
+                a, b = self.get_prior(domain)
+                # Strength of evidence: coherence * reliability
+                strength = coherence * source.reliability_score
+                # Update alpha and beta
+                if domain not in domain_alpha:
+                    domain_alpha[domain] = a
+                    domain_beta[domain] = b
+                # Supporting evidence increases alpha, contradictory increases beta
+                # Here we treat supporting_sources as positive evidence; contradictory would be handled separately
+                domain_alpha[domain] += strength * source.independence_score
+                # Simulate some uncertainty
+                domain_beta[domain] += (1 - strength) * source.independence_score
+
+        # Combine across domains using weighted average of posterior means
+        total_alpha = 0
+        total_beta = 0
+        for domain in domain_alpha:
+            total_alpha += domain_alpha[domain]
+            total_beta += domain_beta[domain]
+
+        if total_alpha + total_beta == 0:
+            posterior = 0.5
+        else:
+            posterior = total_alpha / (total_alpha + total_beta)
+
+        # Compute credible interval
+        hdi = beta.interval(0.95, total_alpha, total_beta)
+
+        return {
+            'posterior_probability': posterior,
+            'credible_interval': (float(hdi[0]), float(hdi[1])),
+            'domain_contributions': {d.value: a/(a+b) for d, a, b in zip(domain_alpha.keys(), domain_alpha.values(), domain_beta.values())},
+            'total_evidence': total_alpha + total_beta
+        }
+
+# =============================================================================
+# ORCHESTRATOR (MegaconsciousnessEngine)
+# =============================================================================
+class OmegaOrchestrator:
+    """Main investigation controller with audit, recursion, and module management."""
+    def __init__(self):
+        self.audit = AuditChain()
+        self.empirical = EmpiricalDataAnchor()
+        self.modules = {
+            InvestigationDomain.SOVEREIGNTY: SovereigntyAnalyzer(),
+            InvestigationDomain.ARCHETYPAL: ArchetypalEngine(),
+            InvestigationDomain.NUMISMATIC: NumismaticAnalyzer(),
+            InvestigationDomain.MEMETIC: MemeticRecursionEngine(),
+            InvestigationDomain.TESLA: TeslaLogosEngine(),
+        }
+        self.corroborator = BayesianCorroborator()
+        self.investigation_cache = {}
+
+    async def investigate(self, claim: str, depth: int = 0, parent_hashes: List[str] = None) -> Dict[str, Any]:
+        if parent_hashes is None:
+            parent_hashes = []
+        inv_id = deterministic_hash(claim + str(depth) + str(time.time()))
+
+        self.audit.add_record("investigation_start", {"claim": claim, "depth": depth, "id": inv_id})
+
+        # Update empirical data
+        await self.empirical.update()
+        resonance = self.empirical.resonance_factor()
+
+        # Run all modules in parallel
+        tasks = []
+        for domain, module in self.modules.items():
+            # Pass claim, and maybe additional context (like coin IDs if numismatic)
+            if domain == InvestigationDomain.NUMISMATIC:
+                # In real use, we would extract coin IDs from claim or context
+                tasks.append(module.analyze(claim, "host_placeholder", "overstrike_placeholder"))
+            else:
+                tasks.append(module.analyze(claim))
+        bundles = await asyncio.gather(*tasks)
+
+        # Add empirical resonance to each bundle's methodological scores (optional)
+        for b in bundles:
+            b.methodological_scores['empirical_resonance'] = resonance
+
+        # Combine evidence
+        combined = await self.corroborator.combine(bundles)
+
+        # Determine if deeper recursion needed
+        needs_deeper = False
+        if combined['posterior_probability'] < 0.4 and depth < MAX_RECURSION_DEPTH:
+            needs_deeper = True
+        if combined['credible_interval'][1] - combined['credible_interval'][0] > 0.3 and depth < MAX_RECURSION_DEPTH:
+            needs_deeper = True
+
+        sub_investigations = []
+        if needs_deeper:
+            # Generate sub‑claims (simplified: use the same claim but deeper)
+            sub_result = await self.investigate(claim + " (deeper)", depth+1, parent_hashes + [inv_id])
+            sub_investigations.append(sub_result)
+
+        # Prepare final report
+        report = {
+            'investigation_id': inv_id,
+            'claim': claim,
+            'depth': depth,
+            'timestamp': datetime.utcnow().isoformat(),
+            'evidence_bundles': [b.evidence_hash for b in bundles],
+            'combined_analysis': combined,
+            'empirical_resonance': resonance,
+            'sub_investigations': sub_investigations,
+            'audit_hash': self.audit.chain[-1]['hash'] if self.audit.chain else None
+        }
+
+        # Sign report with cryptographic hash
+        report_hash = deterministic_hash(report)
+        report['report_hash'] = report_hash
+        self.audit.add_record("investigation_complete", {"id": inv_id, "hash": report_hash})
+
+        return report
+
+    def verify_audit(self) -> bool:
+        return self.audit.verify()
+
+# =============================================================================
+# POLICING ADD‑ON (from VeILEngine)
+# =============================================================================
+class IntegrityMonitor:
+    """Non‑invasive runtime integrity verification."""
+    def __init__(self, orchestrator: OmegaOrchestrator):
+        self.orchestrator = orchestrator
+        self.baseline_manifest = self._generate_manifest()
+        self.violations = []
+
+    def _generate_manifest(self) -> Dict[str, str]:
+        # Simplified: hash of the orchestrator's method source
+        import inspect
+        manifest = {}
+        for name, method in inspect.getmembers(self.orchestrator, inspect.ismethod):
+            try:
+                src = inspect.getsource(method)
+                manifest[name] = hashlib.sha256(src.encode()).hexdigest()
+            except:
+                pass
+        return manifest
+
+    def check_integrity(self) -> bool:
+        current = self._generate_manifest()
+        ok = current == self.baseline_manifest
+        if not ok:
+            self.violations.append({'time': datetime.utcnow().isoformat(), 'type': 'code_alteration'})
+        return ok
+
+    async def monitored_investigate(self, claim: str):
+        if not self.check_integrity():
+            logger.critical("Integrity violation detected! Running in degraded mode.")
+        return await self.orchestrator.investigate(claim)
+
+# =============================================================================
+# DEMONSTRATION
+# =============================================================================
+async def main():
+    print("=" * 70)
+    print("OMEGA INTEGRITY ENGINE – ADVANCED UNIFIED FRAMEWORK")
+    print("=" * 70)
+
+    orchestrator = OmegaOrchestrator()
+    monitor = IntegrityMonitor(orchestrator)
+
+    test_claims = [
+        "The Warren Commission concluded that Lee Harvey Oswald acted alone.",
+        "NASA's Apollo missions were genuine achievements of human exploration.",
+        "The WHO's pandemic response was coordinated and transparent."
+    ]
+
+    for i, claim in enumerate(test_claims, 1):
+        print(f"\n🔍 Investigating claim {i}: {claim}")
+        result = await monitor.monitored_investigate(claim)
+
+        print(f"\n📊 Results:")
+        print(f"   Posterior probability: {result['combined_analysis']['posterior_probability']:.3f}")
+        print(f"   95% credible interval: {result['combined_analysis']['credible_interval']}")
+        print(f"   Empirical resonance: {result['empirical_resonance']:.3f}")
+        print(f"   Depth: {result['depth']}")
+        print(f"   Report hash: {result['report_hash'][:16]}...")
+
+    print(f"\n🔒 Audit chain integrity: {orchestrator.verify_audit()}")
+    print(f"   Total audit blocks: {orchestrator.audit.summary()['total_blocks']}")
+    print(f"   Genesis hash: {orchestrator.audit.summary()['genesis_hash']}...")
+
+if __name__ == "__main__":
+    asyncio.run(main())