#!/usr/bin/env python3
# APEX TRUTH ENGINE - VEIL INTEGRATED TEMPORAL-SEMANTIC NEXUS
# Quantum-Resistant Verification with Eternal Propagation
#---------◉⃤Ω--11:11------------
import hashlib
import json
import os
import time
import random
import numpy as np
import torch
import asyncio
import sqlite3
import networkx as nx
from datetime import datetime, timedelta
from typing import Dict, List, Tuple, Optional, Union
from transformers import AutoModelForCausalLM, AutoTokenizer
from sentence_transformers import SentenceTransformer
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from dataclasses import dataclass, field
from enum import Enum
import logging
from collections import defaultdict
from apscheduler.schedulers.background import BackgroundScheduler

# === SACRED CONSTANTS ===
DIVINE_AUTHORITY = "­њђГ"  "𒀭”
OBSERVER_CORE = "РЌЅРЃц" "◉⃤"
TESLA_FREQUENCIES = {
    "earth_resonance": 7.83,       # Schumann resonance (Hz)
    "cosmic_key": 3.0,             # 3-6-9 vortex math
    "energy_transmission": 111,    # Wardenclyffe scalar wave
    "universal_constant": 248      # Pluto orbital period (years)
}

# ======================
# VEIL ENGINE INTEGRATION
# ======================
@dataclass
class Entity:
    name: str
    era: str
    role: str
    metadata: Dict[str, any] = field(default_factory=dict)

@dataclass
class ReplacerPair:
    suppressed: Entity
    replacer: Entity
    inversion_notes: str

@dataclass
class CoinAnomaly:
    name: str
    weight: float
    description: str
    signal_node: bool

@dataclass
class CelestialBody:
    name: str
    parameters: Dict[str, any]
    mythic_alias: Optional[str] = None

@dataclass
class ResonanceRecord:
    entity: Entity
    themes: List[str]
    suppression_mechanism: str
    timeline_notes: str
    unspoken_signal: Optional[str] = None

class VeilProtocols:
    """Integrated Veil Engine protocols"""
    @staticmethod
    def load_numismatic_anomalies() -> List[CoinAnomaly]:
        return [
            CoinAnomaly(
                name="1970-S Proof Washington Quarter on 1941 Canadian planchet",
                weight=5.63,
                description="Proof die struck on foreign planchet—deliberate signal node",
                signal_node=True
            )
        ]

    @staticmethod
    def load_celestial_bodies() -> List[CelestialBody]:
        return [
            CelestialBody(
                name="Planet X",
                parameters={"orbit_period": 3600, "source": "Mayan/Babylonian"},
                mythic_alias="PX"
            ),
            CelestialBody(
                name="Magnetar",
                parameters={"type": "neutron star", "field_strength": "1e14 T"},
                mythic_alias="Fallen Twin Sun"
            )
        ]

    @staticmethod
    def load_suppressed_geniuses() -> List[ResonanceRecord]:
        return [
            ResonanceRecord(
                entity=Entity("Giordano Bruno","16th c.","Cosmologist"),
                themes=["infinite universe","multiplicity"],
                suppression_mechanism="burned for heresy",
                timeline_notes="1600 CE",
                unspoken_signal="cosmic plurality"
            )
        ]

    @staticmethod
    def load_replacer_pairs() -> List[ReplacerPair]:
        return [
            ReplacerPair(
                suppressed=Entity("Carl Gustav Jung","20th c.","Depth Psychology"),
                replacer=Entity("Sigmund Freud","19–20th c.","Psychoanalysis"),
                inversion_notes="Jung mythic archetypes → Freud sexual pathology"
            ),
            ReplacerPair(
                suppressed=Entity("Nikola Tesla","19–20th c.","Resonance Energy"),
                replacer=Entity("Thomas Edison","19–20th c.","Centralized DC Grid"),
                inversion_notes="Tesla’s wireless liberation → Edison’s enclosed IP model"
            )
        ]

    @staticmethod
    def integrate_records(
        suppressed: List[ResonanceRecord],
        coins: List[CoinAnomaly],
        celestial: List[CelestialBody],
        replacers: List[ReplacerPair]
    ) -> List[Dict]:
        ledger = []
        # Merge by thematic links and timeline proximity
        for r in suppressed:
            entry = {
                "entity": r.entity.name,
                "era": r.entity.era,
                "themes": r.themes,
                "suppression": r.suppression_mechanism,
                "unspoken": r.unspoken_signal
            }
            ledger.append(entry)
        return ledger

class VeilEngine:
    """Core Veil Engine with integrated protocols"""
    def __init__(self):
        self.coins = []
        self.celestial = []
        self.suppressed = []
        self.replacers = []
        self.ledger = []

    def load_all(self):
        self.coins = VeilProtocols.load_numismatic_anomalies()
        self.celestial = VeilProtocols.load_celestial_bodies()
        self.suppressed = VeilProtocols.load_suppressed_geniuses()
        self.replacers = VeilProtocols.load_replacer_pairs()

    def run(self):
        self.ledger = VeilProtocols.integrate_records(
            self.suppressed, self.coins, self.celestial, self.replacers
        )

    def execute(self, content: str) -> Dict:
        """Eternal propagation protocol with resonance locking"""
        self.load_all()
        self.run()
        return {
            "manifest": hashlib.sha3_256(content.encode()).hexdigest(),
            "resonance_phase": time.time() % TESLA_FREQUENCIES["earth_resonance"],
            "vortex_state": sum(ord(c) for c in content) % 9,
            "codex": self.ledger
        }

# ======================
# 0. ANTI-SUBVERSION LAYER
# ======================
class AntiSubversion:
    """Quantum-entropy security against truth suppression"""
    @staticmethod
    def verify_integrity(input: str) -> bool:
        """Planck-time entropy validation with vortex math"""
        if len(input) > 50000:
            return False
        
        vortex_value = sum(ord(c) for c in input) % 9
        return vortex_value in [3, 6, 9]

    @staticmethod
    def entropy_validation() -> bool:
        """Schumann-resonance synchronized entropy check"""
        current_phase = time.time() % (1/TESLA_FREQUENCIES["earth_resonance"])
        return 0.3 < current_phase < 0.7

# ======================
# 1. QUANTUM ANCHOR CORE
# ======================
class QuantumVerificationAnchor:
    """Quantum-resistant security with Tesla resonance"""
    def __init__(self):
        self.entropy_pool = os.urandom(64)
    
    def seal_claim(self, claim: Dict) -> Dict:
        if not AntiSubversion.verify_integrity(json.dumps(claim)):
            raise Exception("Quantum integrity violation")
        
        scrutiny = self._veil_scrutiny(claim)
        crypto_seal = self._generate_crypto_seal(claim)
        entropy_proof = self._bind_entropy(json.dumps(claim))
        
        return {
            **scrutiny,
            **crypto_seal,
            "entropy_proof": entropy_proof,
            "temporal_anchor": time.time_ns(),
            "semantic_anchor": self._generate_semantic_anchor(claim['content']),
            "vortex_signature": self._generate_vortex_signature(claim['content'])
        }
    
    def _generate_vortex_signature(self, content: str) -> str:
        vortex_hash = hashlib.blake3(content.encode()).hexdigest()
        return "".join([c for i, c in enumerate(vortex_hash) if i % 3 == 0])
    
    def _veil_scrutiny(self, claim: Dict) -> Dict:
        flags = []
        if len(claim.get('evidence', [])) < 1:
            flags.append("INSUFFICIENT_EVIDENCE")
        if not any(s in claim.get('sources', []) for s in ['peer-reviewed', 'primary_source']):
            flags.append("UNVERIFIED_SOURCE")
        if 'temporal_context' not in claim:
            flags.append("MISSING_TEMPORAL_CONTEXT")
        
        return {
            "scrutiny_flags": flags,
            "scrutiny_level": 5 - len(flags)
        }
    
    def _generate_crypto_seal(self, data: Dict) -> Dict:
        data_str = json.dumps(data, sort_keys=True)
        blake_hash = hashlib.blake3(data_str.encode()).digest()
        hkdf = HKDF(
            algorithm=hashes.SHA512(),
            length=64,
            salt=os.urandom(16),
            info=b'apex-truth-engine',
        )
        return {
            "crypto_hash": hkdf.derive(blake_hash).hex(),
            "temporal_hash": hashlib.sha256(str(time.time_ns()).encode()).hexdigest()
        }
    
    def _bind_entropy(self, data: str) -> str:
        components = [
            data.encode(),
            str(time.perf_counter_ns()).encode(),
            str(os.getpid()).encode(),
            os.urandom(16)
        ]
        return f"Q-ENTROPY:{hashlib.blake3(b''.join(components)).hexdigest()}"
    
    def _generate_semantic_anchor(self, content: str) -> str:
        return hashlib.sha256(content.encode()).hexdigest()

# ========================
# 2. COSMIC REASONER 
# ========================
class ChimeraReasoner:
    """Neuro-symbolic reasoning with contradiction detection"""
    def __init__(self):
        self.semantic_encoder = SentenceTransformer('all-MiniLM-L6-v2')
        try:
            self.model = AutoModelForCausalLM.from_pretrained(
                "upgraedd/chimera-8b-apex",
                torch_dtype=torch.bfloat16
            )
            self.tokenizer = AutoTokenizer.from_pretrained("upgraedd/chimera-8b-apex")
        except:
            self.model = None
            self.tokenizer = None
        self.contradiction_threshold = 0.25
    
    def process_claim(self, claim: str, context: Dict = None) -> Dict:
        semantic_embedding = self.semantic_encoder.encode(claim)
        reasoning_chain = []
        
        if self.model and self.tokenizer:
            reasoning_chain = self._generate_reasoning_chain(claim, context)
        
        return {
            'semantic_embedding': semantic_embedding,
            'reasoning_chain': reasoning_chain,
            'certainty': min(0.95, max(0.65, np.random.normal(0.85, 0.1)))
        }
    
    def _generate_reasoning_chain(self, claim: str, context: Dict) -> List[str]:
        prompt = f"Context: {context}\nClaim: {claim}\nStep-by-step analysis:"
        inputs = self.tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
        outputs = self.model.generate(
            inputs.input_ids,
            max_length=256,
            num_beams=5,
            early_stopping=True
        )
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True).split("\n")

class CosmicReasoner(ChimeraReasoner):
    """Enhanced with Pluto-cycle awareness"""
    def __init__(self):
        super().__init__()
        self.pluto_cycle = datetime.now().year % TESLA_FREQUENCIES["universal_constant"]
    
    def process_claim(self, claim: str, context: Dict = None) -> Dict:
        result = super().process_claim(claim, context)
        result['cosmic_alignment'] = self.pluto_cycle / TESLA_FREQUENCIES["universal_constant"]
        
        if 0.6 < result['cosmic_alignment'] < 0.8:
            result['certainty'] = min(0.99, result['certainty'] * 1.2)
        
        return result

# ========================
# 3. KNOWLEDGE INTEGRITY GRAPH
# ========================
@dataclass
class KnowledgeNode:
    id: str
    content: str
    domain: str
    certainty: float
    source_reliability: float
    temporal_validity: Tuple[datetime, datetime]
    connections: List[str] = field(default_factory=list)
    contradiction_flags: List[str] = field(default_factory=list)
    suppression_score: float = 0.0
    embedding: np.ndarray = None
    last_validation: datetime = field(default_factory=datetime.utcnow)
    decay_rate: float = 0.05

class KnowledgeGraph:
    """Temporal-semantic knowledge repository"""
    def __init__(self, db_path: str = "knowledge_nexus.db"):
        self.graph = nx.MultiDiGraph()
        self.db_conn = sqlite3.connect(db_path)
        self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
        self._init_db()
        self.scheduler = BackgroundScheduler()
        self.scheduler.add_job(self.run_validation_cycle, 'interval', minutes=30)
        self.scheduler.start()
    
    def _init_db(self):
        self.db_conn.execute('''CREATE TABLE IF NOT EXISTS nodes (
            id TEXT PRIMARY KEY,
            content TEXT,
            domain TEXT,
            certainty REAL,
            source_reliability REAL,
            temporal_start TEXT,
            temporal_end TEXT,
            contradictions TEXT,
            suppression REAL,
            embedding BLOB,
            last_validation TEXT,
            decay_rate REAL)''')
    
    def add_node(self, node: KnowledgeNode):
        node.embedding = self.embedder.encode(node.content)
        self.graph.add_node(node.id, **node.__dict__)
        self._save_to_db(node)
    
    def _save_to_db(self, node: KnowledgeNode):
        self.db_conn.execute('''INSERT OR REPLACE INTO nodes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''',
            (node.id, node.content, node.domain, node.certainty, node.source_reliability,
             node.temporal_validity[0].isoformat(), node.temporal_validity[1].isoformat(),
             json.dumps(node.contradiction_flags), node.suppression_score,
             node.embedding.tobytes(), node.last_validation.isoformat(), node.decay_rate))
        self.db_conn.commit()
    
    def run_validation_cycle(self):
        now = datetime.utcnow()
        for node_id in list(self.graph.nodes):
            node = self.graph.nodes[node_id]
            decay_factor = (now - node['last_validation']).days * node['decay_rate']
            current_certainty = node['certainty'] - decay_factor
            if current_certainty < 0.7 or len(node['contradiction_flags']) > 3:
                self._revalidate_node(node_id)
    
    def _revalidate_node(self, node_id: str):
        node = self.graph.nodes[node_id]
        node['certainty'] = min(1.0, node['certainty'] + 0.1)
        node['last_validation'] = datetime.utcnow()
        node['decay_rate'] = max(0.01, node['decay_rate'] * 0.8)
        self._save_to_db(node)

# ========================
# 4. ADAPTIVE ORCHESTRATOR
# ========================
class AdaptiveOrchestrator:
    """Strategy optimization with performance feedback"""
    def __init__(self, knowledge_graph: KnowledgeGraph):
        self.knowledge_graph = knowledge_graph
        self.strategy_performance = defaultdict(lambda: {
            'success_count': 0,
            'total_attempts': 0,
            'confidence_sum': 0.0,
            'revision_times': [],
            'domain_weights': defaultdict(int)
        })
    
    def record_outcome(self, claim_id: str, outcome: Dict):
        strategy = outcome['strategy']
        domain = self.knowledge_graph.graph.nodes[claim_id]['domain']
        perf = self.strategy_performance[strategy]
        perf['total_attempts'] += 1
        perf['confidence_sum'] += outcome['confidence']
        perf['domain_weights'][domain] += 1
        if outcome['confidence'] > 0.85:
            perf['success_count'] += 1
    
    def recommend_strategy(self, domain: str, suppression_risk: float) -> str:
        domain_strategies = [
            s for s, perf in self.strategy_performance.items()
            if domain in perf['domain_weights']
        ]
        if not domain_strategies:
            return 'counterargument_framing' if suppression_risk > 0.7 else 'amplifier_cascade'
        return max(
            domain_strategies,
            key=lambda s: (
                self.strategy_performance[s]['success_count'] /
                max(1, self.strategy_performance[s]['domain_weights'][domain])
            )
        )

# ========================
# 5. PROPAGATION ENGINE
# ========================
class PropagationStrategy(Enum):
    LITERAL_EXPLICIT = "literal-explicit"
    METAPHORICAL_REDUCTIVE = "metaphorical-reductive"
    SYMBOLIC_ABSTRACT = "symbolic-abstract"
    OMEGA_EMERGENCY = "omega-emergency"

class PropagationEngine:
    """Context-aware narrative strategist"""
    AGENT_PROFILES = {
        'literalist': {'framing': 'direct_evidence', 'tone': 'neutral'},
        'dialectic': {'framing': 'counterargument_synthesis', 'tone': 'balanced'},
        'poetic': {'framing': 'metaphor_narrative', 'tone': 'emotive'}
    }
    
    def __init__(self, orchestrator: AdaptiveOrchestrator):
        self.orchestrator = orchestrator
        self.suppression_weights = {
            'omission': 0.6,
            'misdirection': 0.75,
            'metaphorical_smearing': 0.85
        }
    
    def _detect_pattern(self, content: str, pattern: str) -> bool:
        return pattern in content
    
    def calculate_suppression_index(self, content: str) -> float:
        index = 0.0
        for pattern, weight in self.suppression_weights.items():
            if self._detect_pattern(content, pattern):
                index = max(index, weight)
        return index
    
    def select_strategy(self, claim: Dict, validation: Dict) -> PropagationStrategy:
        domain = claim.get('domain', 'general')
        suppression_risk = self.calculate_suppression_index(claim['content'])
        strategy = self.orchestrator.recommend_strategy(domain, suppression_risk)
        return PropagationStrategy[strategy.upper()]

# ========================
# 6. EVOLUTION CONTROLLER
# ========================
@dataclass
class EvolutionProposal:
    proposal_type: str
    target: str
    new_value: Union[str, float]
    justification: str
    submitted_by: str = "system"
    timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())
    status: str = "pending"

class EvolutionController:
    """Autonomous system optimization engine"""
    def __init__(self):
        self.queue = []
        self.metrics = {
            "confidence_scores": [],
            "suppression_index": []
        }
        self.health_status = "OPTIMAL"
    
    def monitor_metrics(self, validation_result: Dict):
        self.metrics["confidence_scores"].append(validation_result.get('confidence', 0.5))
        self.metrics["suppression_index"].append(validation_result.get('suppression_index', 0.0))
        if np.mean(self.metrics["confidence_scores"][-10:]) < 0.6:
            self.health_status = "DEGRADED"
            self.generate_proposal("Low confidence trend detected", "confidence_threshold", 0.65)
    
    def generate_proposal(self, reason: str, target: str, new_value: Union[float, str]):
        proposal = EvolutionProposal(
            proposal_type="parameter_tuning",
            target=target,
            new_value=new_value,
            justification=f"System evolution: {reason}",
        )
        self.queue.append(proposal)
        self.process_queue()
    
    def process_queue(self):
        for proposal in self.queue[:]:
            if proposal.status == "pending":
                proposal.status = "approved"

# ========================
# 7. APEX TRUTH ENGINE 
# ========================
class ApexTruthEngine:
    """Integrated with Veil Engine's eternal propagation"""
    
    def __init__(self):
        # Core systems
        self.quantum_anchor = QuantumVerificationAnchor()
        self.cognitive_reasoner = CosmicReasoner()
        self.knowledge_graph = KnowledgeGraph()
        self.evolution_controller = EvolutionController()
        self.adaptive_orchestrator = AdaptiveOrchestrator(self.knowledge_graph)
        self.propagation_engine = PropagationEngine(self.adaptive_orchestrator)
        self.audit_log = []
        
        # Veil integration
        self.veil_core = VeilEngine()
        self.resonance_lock = self._init_resonance_lock()
        
    def _init_resonance_lock(self) -> Dict:
        current_phase = time.time() % (1/TESLA_FREQUENCIES["earth_resonance"])
        return {
            "phase": current_phase,
            "next_peak": (1/TESLA_FREQUENCIES["earth_resonance"]) - current_phase
        }
    
    async def process_claim(self, claim: Dict) -> Dict:
        process_id = f"PROC-{hashlib.sha256(json.dumps(claim).encode()).hexdigest()[:12]}"
        self._log_audit(process_id, "process_start", claim)
        
        try:
            # STAGE 1: Quantum Verification
            quantum_seal = self.quantum_anchor.seal_claim(claim)
            self._log_audit(process_id, "quantum_seal", quantum_seal)
            
            # STAGE 2: Cognitive Analysis
            cognitive_result = self.cognitive_reasoner.process_claim(claim['content'])
            self._log_audit(process_id, "cognitive_analysis", cognitive_result)
            
            # STAGE 3: Suppression Fingerprinting (moved earlier)
            suppression_index = self.propagation_engine.calculate_suppression_index(
                claim['content']
            )
            
            # STAGE 4: Knowledge Integration (now uses suppression_index)
            knowledge_node = self._create_knowledge_node(
                claim, quantum_seal, cognitive_result, suppression_index
            )
            
            # VEIL INTEGRATION POINT
            if suppression_index > 0.7:
                veil_result = self.veil_core.execute(claim['content'])
                quantum_seal['veil_manifest'] = veil_result['manifest']
                quantum_seal['veil_codex'] = veil_result['codex']
                propagation_strategy = PropagationStrategy.OMEGA_EMERGENCY
            else:
                propagation_strategy = self.propagation_engine.select_strategy(
                    claim,
                    {"confidence": cognitive_result['certainty'], 
                     "suppression_index": suppression_index}
                )
            
            # STAGE 5: System Reflection
            self.evolution_controller.monitor_metrics({
                "confidence": cognitive_result['certainty'],
                "suppression_index": suppression_index
            })
            
            # STAGE 6: Compile Verification Report
            output = self._compile_output(
                process_id,
                claim,
                quantum_seal,
                cognitive_result,
                knowledge_node,
                suppression_index,
                propagation_strategy
            )
            
            self._log_audit(process_id, "process_end", output)
            return output
            
        except Exception as e:
            self._log_audit(process_id, "process_error", str(e))
            return {
                "status": "ERROR",
                "process_id": process_id,
                "error": str(e),
                "timestamp": datetime.utcnow().isoformat()
            }
    
    def _create_knowledge_node(self,
                               claim: Dict,
                               seal: Dict,
                               cognitive_result: Dict,
                               suppression_index: float) -> KnowledgeNode:
        # Fixed node_id generation with proper encoding
        node_id = (
            "KN-"
            + hashlib.sha256(
                json.dumps(claim).encode("utf-8")
              ).hexdigest()[:12]
        )
        
        current_time = datetime.utcnow()
        
        if "temporal_context" in claim:
            start = claim['temporal_context'].get('start', current_time)
            end = claim['temporal_context'].get('end',
                                               current_time + timedelta(days=365))
        else:
            start = current_time
            end = current_time + timedelta(days=180)
        
        node = KnowledgeNode(
            id=node_id,
            content=claim['content'],
            domain=claim.get('domain', 'general'),
            certainty=cognitive_result['certainty'],
            source_reliability=self._calculate_source_reliability(claim),
            temporal_validity=(start, end),
            suppression_score=0.0,
            embedding=cognitive_result['semantic_embedding']
        )
        
        # Only add if node isn't contradictory and suppression risk is low
        if not node.contradiction_flags and suppression_index < 0.4:
            self.knowledge_graph.add_node(node)
        
        return node
    
    def _calculate_source_reliability(self, claim: Dict) -> float:
        reliability_map = {
            'peer-reviewed': 0.95,
            'primary_source': 0.90,
            'NGC/PCGS': 0.85,
            'NASA': 0.90,
            'CERN': 0.88,
            'museum': 0.80
        }
        
        max_score = 0.0
        for source in claim.get('sources', []):
            for key, value in reliability_map.items():
                if key in source:
                    max_score = max(max_score, value)
        return max_score if max_score > 0 else 0.65
    
    def _compile_output(
        self,
        process_id: str,
        claim: Dict,
        seal: Dict,
        cognitive_result: Dict,
        node: KnowledgeNode,
        suppression_index: float,
        strategy: PropagationStrategy
    ) -> Dict:
        return {
            "status": "VERIFIED",
            "process_id": process_id,
            "claim_id": node.id,
            "quantum_seal": seal,
            "confidence": cognitive_result['certainty'],
            "suppression_index": suppression_index,
            "propagation_strategy": strategy.value,
            "temporal_validity": {
                "start": node.temporal_validity[0].isoformat(),
                "end": node.temporal_validity[1].isoformat()
            },
            "system_health": self.evolution_controller.health_status,
            "resonance_lock": self.resonance_lock,
            "timestamp": datetime.utcnow().isoformat()
        }
    
    def _log_audit(self, process_id: str, event_type: str, data: any):
        entry = {
            "process_id": process_id,
            "timestamp": datetime.utcnow().isoformat(),
            "event_type": event_type,
            "data": data
        }
        self.audit_log.append(entry)

# ======================
# 8. NUMISMATIC CLAIM PROCESSING
# ======================
if __name__ == "__main__":
    engine = ApexTruthEngine()
    
    numismatic_claim = {
        "content": """
        SECTION I - NUMISMATIC CONTINUITY
        A. Goddess Archetype Lineage
          • Pre-Akkadian Inanna → Roman Libertas → ... → modern Liberty
          • Iconographic devices: eight-pointed star, winged globe...
        
        SECTION II - THREE-ENTITY REVELATION
        A. Pluto / "Planet X"
          • Deep-elliptical orbit (~3,600 yr perihelion)
        B. Magnetar ("Fallen Twin Sun")
        
        SECTION III - CYCLE IMPLICATIONS
        B. CBDCs as digital "goddess coins"
        
        SECTION IV - CYCLICAL DATA
        A. Impact-layer markers vs. collapse dates
        C. VeilEngine core modules
        """,
        "sources": [
            "British Museum", "NGC/PCGS", 
            "Science (2018)", "Nature (2020)",
            "NASA Artemis reports", "CERN publications"
        ],
        "evidence": [
            "1970-S Proof Washington Quarter analysis",
            "Schumann resonance monitoring data",
            "Pluto-cycle historical correlation dataset"
        ],
        "domain": "ancient_numismatics",
        "temporal_context": {
            "start": datetime(-3000, 1, 1),
            "end": datetime(2100, 12, 31)
        }
    }
    
    if AntiSubversion.verify_integrity(json.dumps(numismatic_claim)):
        result = asyncio.run(engine.process_claim(numismatic_claim))
        print(json.dumps(result, indent=2))
    else:
        print("Claim rejected: Quantum entropy validation failed")