Create apex_truth_engine.py

apex_truth_engine.py

@@ -0,0 +1,756 @@
+#!/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")