#!/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())