Create 𒀭 finalꙮ

𒀭 finalꙮ

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+COMPLETE OMEGA VERITAS STACK — Unified Reality OS v3.1
+Integrated TRINITY v2.0 + Module 50 Fact Engine + ITGARE v3.0
+Empirical anchoring • Adaptive intelligence • Autonomous knowledge • Truth governance
+
+This single-file system composes:
+- EmpiricalDataAnchor, EnhancedAntiSubversion, EnhancedCognitiveReasoner, AdvancedKnowledgeGraph,
+  MemeticPropagationEngine, AdaptiveValidationEngine (TRINITY v2.0)
+- FactEngine (Module 50: Empirical Historical Analysis)
+- ITGARE v3.0 (Autogenetic truth layers, Module 51 Autonomous Knowledge, Reality OS components)
+- Unified Orchestrator that binds all subsystems into one operational runtime.
+
+Notes:
+- External model loads and web calls are guarded; subsystems degrade gracefully.
+- SQLite DB for advanced graph; can be replaced with your preferred store.
+- Replace placeholder components (RealityForge, TruthCombatUnit, etc.) with your real implementations.
+"""
+
+import os
+import json
+import time
+import math
+import asyncio
+import logging
+import hashlib
+import secrets
+from dataclasses import dataclass, field, asdict
+from typing import Dict, List, Any, Tuple, Optional, Union
+from collections import deque, defaultdict
+from enum import Enum
+from datetime import datetime, timedelta
+
+# Numeric + Sci
+import numpy as np
+from scipy import fft, stats
+
+# ML (guarded imports)
+try:
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from sentence_transformers import SentenceTransformer
+except Exception:
+    torch = None
+    AutoModelForCausalLM = None
+    AutoTokenizer = None
+    SentenceTransformer = None
+
+# Async HTTP (guarded)
+try:
+    import aiohttp
+except Exception:
+    aiohttp = None
+
+# Graph + DB
+try:
+    import networkx as nx
+    import sqlite3
+except Exception:
+    nx = None
+    sqlite3 = None
+
+# -----------------------------------------------------------------------------
+# Utilities
+# -----------------------------------------------------------------------------
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
+logger = logging.getLogger("OMEGA_VERITAS_STACK")
+
+def clamp(x: float, lo: float = 0.0, hi: float = 1.0) -> float:
+    return float(max(lo, min(hi, x)))
+
+def safe_mean(arr: List[float], default: float = 0.0) -> float:
+    return float(np.mean(arr)) if arr else default
+
+# -----------------------------------------------------------------------------
+# TRINITY v2.0 — Empirical anchoring, integrity checks, cognitive overlays
+# -----------------------------------------------------------------------------
+
+class EmpiricalConstants:
+    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"
+    SCHUMANN_RESONANCE = 7.83
+    GOLDEN_RATIO = 1.61803398875
+    BASE_RESONANCE_THRESHOLD = 0.7
+    BASE_SUPPRESSION_THRESHOLD = 0.6
+    BASE_COHERENCE_THRESHOLD = 0.6
+
+class EmpiricalDataAnchor:
+    def __init__(self, update_interval: int = 3600):
+        self.geomagnetic_data = None
+        self.solar_flux_data = None
+        self.last_update = 0
+        self.update_interval = update_interval
+
+    async def update_empirical_data(self):
+        if aiohttp is None:
+            return
+        now = time.time()
+        if now - self.last_update < self.update_interval:
+            return
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(EmpiricalConstants.GEOMAGNETIC_API) as r1:
+                    if r1.status == 200:
+                        self.geomagnetic_data = await r1.json()
+                async with session.get(EmpiricalConstants.SOLAR_FLUX_API) as r2:
+                    if r2.status == 200:
+                        self.solar_flux_data = await r2.json()
+                self.last_update = now
+                logger.info("Empirical data anchor 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
+        try:
+            latest = self.geomagnetic_data[0] if isinstance(self.geomagnetic_data, list) else self.geomagnetic_data
+            kp = float(latest.get('Kp', 2.0))
+            return min(9.0, max(0.0, kp))
+        except Exception:
+            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 self.solar_flux_data:
+                latest = self.solar_flux_data[-1]
+                flux = float(latest.get('ssn', 100.0))
+                return max(50.0, min(300.0, flux))
+        except Exception:
+            pass
+        return 100.0
+
+    def calculate_empirical_resonance_factor(self) -> float:
+        geomagnetic = self.get_geomagnetic_index()
+        solar_flux = self.get_solar_flux()
+        geomagnetic_ideal = 1.0 - abs(geomagnetic - 3.0) / 9.0
+        solar_ideal = 1.0 - abs(solar_flux - 120.0) / 250.0
+        return float((geomagnetic_ideal + solar_ideal) / 2.0)
+
+class EnhancedAntiSubversion:
+    def __init__(self, empirical_anchor: EmpiricalDataAnchor):
+        self.empirical_anchor = empirical_anchor
+
+    async def quantum_integrity_check(self, input_data: Any) -> Tuple[bool, Dict[str, float]]:
+        input_str = json.dumps(input_data, sort_keys=True, default=str) if isinstance(input_data, dict) else str(input_data)
+        await self.empirical_anchor.update_empirical_data()
+
+        scores = {}
+        vortex_value = sum(ord(c) for c in input_str) % 9
+        scores['vortex'] = 1.0 if vortex_value in [3, 6, 9] else 0.0
+
+        crypto_hash = hashlib.blake2b(input_str.encode(), digest_size=32).hexdigest()
+        scores['crypto'] = 1.0 if len(crypto_hash) == 64 else 0.0
+
+        scores['empirical'] = self.empirical_anchor.calculate_empirical_resonance_factor()
+        scores['frequency'] = self._analyze_frequency_spectrum(input_str)
+        scores['energy'] = self._analyze_energy_pattern(input_str)
+
+        weights = {'vortex': 0.2, 'crypto': 0.3, 'energy': 0.2, 'empirical': 0.15, 'frequency': 0.15}
+        overall = sum(scores[k] * weights[k] for k in weights)
+        return overall > 0.7, scores
+
+    def _analyze_frequency_spectrum(self, text: str) -> float:
+        if len(text) < 10:
+            return 0.5
+        chars = [ord(c) for c in text[:1000]]
+        spectrum = np.abs(fft.fft(chars))
+        freqs = fft.fftfreq(len(chars))
+        tesla = [3, 6, 9, EmpiricalConstants.SCHUMANN_RESONANCE]
+        power = 0.0
+        for h in tesla:
+            target = h / len(chars)
+            idx = np.argmin(np.abs(freqs - target))
+            power += spectrum[idx]
+        total = np.sum(spectrum)
+        return float(power / total if total > 0 else 0.0)
+
+    def _analyze_energy_pattern(self, text: str) -> float:
+        # Lightweight token-level uniformity proxy
+        if not text:
+            return 0.5
+        blocks = [sum(ord(c) for c in text[i:i+32]) for i in range(0, min(len(text), 512), 32)]
+        if not blocks:
+            return 0.5
+        std = np.std(blocks)
+        return float(1.0 / (std + 1e-9)) if std > 0 else 1.0
+
+class EnhancedCognitiveReasoner:
+    def __init__(self):
+        self.semantic_encoder = None
+        self.model = None
+        self.tokenizer = None
+        try:
+            if SentenceTransformer:
+                self.semantic_encoder = SentenceTransformer('all-MiniLM-L6-v2')
+            if AutoTokenizer and AutoModelForCausalLM and torch:
+                self.tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+                self.model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+        except Exception as e:
+            logger.warning(f"Cognitive model init failed: {e}")
+
+    def analyze_with_tesla_overlay(self, claim: str, context: Dict = None) -> Dict[str, Any]:
+        embedding = None
+        if self.semantic_encoder:
+            try:
+                embedding = self.semantic_encoder.encode(claim)
+            except Exception:
+                embedding = np.zeros(384, dtype=np.float32)
+
+        frequency_profile = self._analyze_claim_frequency(claim)
+        harmonic_alignment = self._calculate_harmonic_alignment(claim)
+        reasoning_chain = self._generate_tesla_aware_reasoning(claim, context, frequency_profile)
+        certainty = min(0.99, 0.7 + harmonic_alignment * 0.3)
+
+        return {
+            'semantic_embedding': embedding if embedding is not None else np.zeros(384, dtype=np.float32),
+            'frequency_profile': frequency_profile,
+            'harmonic_alignment': harmonic_alignment,
+            'reasoning_chain': reasoning_chain,
+            'certainty': certainty,
+            'tesla_enhancement': harmonic_alignment
+        }
+
+    def _analyze_claim_frequency(self, claim: str) -> Dict[str, float]:
+        words = claim.lower().split()
+        profile = {}
+        for number in [3, 6, 9, 7, 8]:
+            count = sum(1 for w in words if str(number) in w)
+            profile[f'tesla_{number}'] = count / max(1, len(words))
+        sentences = [s for s in claim.split('.') if s.strip()]
+        lens = [len(s.split()) for s in sentences]
+        if lens:
+            ratio = np.std(lens) / max(np.mean(lens), 1e-9)
+            profile['structural_coherence'] = 1.0 - min(1.0, ratio)
+        return profile
+
+    def _calculate_harmonic_alignment(self, claim: str) -> float:
+        vals = [ord(c) for c in claim[:500]]
+        if not vals:
+            return 0.5
+        res = []
+        for t in [3, 6, 9]:
+            modp = sum(1 for v in vals if v % t == 0) / len(vals)
+            res.append(modp)
+        res.append(self._find_golden_sequences(vals))
+        return float(np.mean(res))
+
+    def _find_golden_sequences(self, values: List[int]) -> float:
+        if len(values) < 3:
+            return 0.0
+        count = 0
+        for i in range(len(values) - 2):
+            a, b, c = values[i:i+3]
+            if a == 0 or b == 0:
+                continue
+            if abs(b / a - EmpiricalConstants.GOLDEN_RATIO) < 0.1 and abs(c / b - EmpiricalConstants.GOLDEN_RATIO) < 0.1:
+                count += 1
+        return count / (len(values) - 2)
+
+    def _generate_tesla_aware_reasoning(self, claim: str, context: Dict, freq_profile: Dict) -> List[str]:
+        if not (self.model and self.tokenizer and torch):
+            return ["Fallback reasoning: semantic + numeric pattern coherence assessed."]
+        try:
+            prompt = f"TContent: {claim}\nTesla Profile: {freq_profile}\nExplain coherence:"
+            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+            with torch.no_grad():
+                out = self.model.generate(
+                    inputs.input_ids,
+                    max_length=256,
+                    num_beams=3,
+                    early_stopping=True,
+                    pad_token_id=self.tokenizer.eos_token_id
+                )
+            reasoning = self.tokenizer.decode(out[0], skip_special_tokens=True)
+            lines = [l.strip() for l in reasoning.split("\n") if l.strip()]
+            return lines[:6]
+        except Exception as e:
+            return [f"Reasoner error: {str(e)}"]
+
+class AdvancedKnowledgeGraph:
+    def __init__(self, db_path: str = "trinity_advanced.db"):
+        self.graph = nx.MultiDiGraph() if nx else None
+        self.db_conn = sqlite3.connect(db_path, check_same_thread=False) if sqlite3 else None
+        self.embedder = None
+        try:
+            if SentenceTransformer:
+                self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
+        except Exception:
+            self.embedder = None
+        if self.db_conn:
+            self._init_advanced_schema()
+
+    def _init_advanced_schema(self):
+        self.db_conn.execute('''CREATE TABLE IF NOT EXISTS advanced_nodes (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            domain TEXT,
+            certainty REAL,
+            source_reliability REAL,
+            suppression_score REAL,
+            contradiction_count INTEGER,
+            temporal_start TEXT,
+            temporal_end TEXT,
+            embedding BLOB,
+            tesla_signature TEXT,
+            empirical_anchor REAL,
+            lineage_path TEXT,
+            suppression_links TEXT,
+            last_validated TEXT
+        )''')
+        self.db_conn.execute('''CREATE TABLE IF NOT EXISTS suppression_network (
+            source_id TEXT,
+            target_id TEXT,
+            suppression_type TEXT,
+            strength REAL,
+            detected_at TEXT
+        )''')
+        self.db_conn.commit()
+
+    def add_node_with_lineage(self, node_data: Dict, parent_nodes: List[str] = None):
+        node_id = node_data['id']
+        suppression_links = self._trace_suppression_lineage(node_id, parent_nodes)
+        lineage_path = self._calculate_lineage_path(node_id, parent_nodes)
+        data = {
+            **node_data,
+            'lineage_path': lineage_path,
+            'suppression_links': json.dumps(suppression_links),
+            'contradiction_count': len(node_data.get('contradiction_flags', [])),
+            'last_validated': datetime.utcnow().isoformat()
+        }
+        if self.graph:
+            self.graph.add_node(node_id, **data)
+            if parent_nodes:
+                for p in parent_nodes:
+                    self.graph.add_edge(p, node_id, relationship='lineage')
+        if self.db_conn:
+            self._save_advanced_node(data)
+
+    def _trace_suppression_lineage(self, node_id: str, parents: Optional[List[str]]) -> List[Dict]:
+        if not parents or not self.graph:
+            return []
+        patterns = []
+        for pid in parents:
+            if pid in self.graph.nodes:
+                pdata = self.graph.nodes[pid]
+                if pdata.get('suppression_score', 0) > 0.5:
+                    patterns.append({
+                        'source': pid,
+                        'type': 'lineage_suppression',
+                        'strength': pdata['suppression_score'],
+                        'inheritance_path': [pid, node_id]
+                    })
+        return patterns
+
+    def _calculate_lineage_path(self, node_id: str, parents: Optional[List[str]]) -> str:
+        if not parents or not self.graph:
+            return "root"
+        paths = []
+        for pid in parents:
+            if pid in self.graph.nodes:
+                parent_path = self.graph.nodes[pid].get('lineage_path', 'unknown')
+                paths.append(f"{parent_path}→{node_id}")
+        return "|".join(paths) if paths else "root"
+
+    def detect_suppression_network(self, threshold: float = 0.6) -> List[Dict]:
+        if not self.graph:
+            return []
+        nodes = [n for n, d in self.graph.nodes(data=True) if d.get('suppression_score', 0) > threshold]
+        networks = []
+        for node in nodes:
+            neighbors = [x for x in self.graph.neighbors(node)
+                         if self.graph.nodes[x].get('suppression_score', 0) > threshold]
+            if neighbors:
+                networks.append({
+                    'central_node': node,
+                    'suppression_cluster': neighbors,
+                    'cluster_strength': float(np.mean([self.graph.nodes[x].get('suppression_score', 0) for x in [node] + neighbors]))
+                })
+        return networks
+
+    def _save_advanced_node(self, node_data: Dict):
+        emb = node_data.get('embedding', np.zeros(384, dtype=np.float32)).tobytes()
+        self.db_conn.execute('''INSERT OR REPLACE INTO advanced_nodes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', (
+            node_data['id'], node_data['content'], node_data['domain'],
+            node_data['certainty'], node_data['source_reliability'],
+            node_data['suppression_score'], node_data['contradiction_count'],
+            node_data['temporal_validity'][0].isoformat(),
+            node_data['temporal_validity'][1].isoformat(),
+            emb, json.dumps(node_data.get('tesla_signature', {})),
+            node_data.get('empirical_anchor', 0.5),
+            node_data['lineage_path'], node_data['suppression_links'],
+            node_data['last_validated']
+        ))
+        self.db_conn.commit()
+
+class MemeticPropagationEngine:
+    def __init__(self, knowledge_graph: AdvancedKnowledgeGraph):
+        self.knowledge_graph = knowledge_graph
+        self.strategies = {
+            'memetic_seeding': self._memetic_seeding,
+            'resonance_echo': self._resonance_echo,
+            'amplifier_cascade': self._amplifier_cascade
+        }
+
+    async def execute_propagation(self, node_id: str, strategy: str, context: Dict) -> Dict:
+        strat = self.strategies.get(strategy, self._amplifier_cascade)
+        return await strat(node_id, context)
+
+    async def _memetic_seeding(self, node_id: str, context: Dict) -> Dict:
+        node = self.knowledge_graph.graph.nodes[node_id] if self.knowledge_graph.graph else {}
+        content = node.get('content', '')
+        score = self._calculate_memetic_potential(content)
+        variants = self._generate_memetic_variants(content, context)
+        return {
+            'strategy': 'memetic_seeding',
+            'memetic_score': score,
+            'variants_generated': len(variants),
+            'estimated_reach': int(score * 1000),
+            'persistence_factor': 0.8
+        }
+
+    async def _resonance_echo(self, node_id: str, context: Dict) -> Dict:
+        node = self.knowledge_graph.graph.nodes[node_id] if self.knowledge_graph.graph else {}
+        base_res = node.get('resonance', 0.5)
+        ts = node.get('tesla_signature', {})
+        if isinstance(ts, str):
+            try:
+                ts = json.loads(ts)
+            except Exception:
+                ts = {}
+        amp = ts.get('harmonic_alignment', 0.5)
+        return {
+            'strategy': 'resonance_echo',
+            'base_resonance': base_res,
+            'amplification_factor': amp,
+            'amplified_resonance': base_res * (1 + amp)
+        }
+
+    async def _amplifier_cascade(self, node_id: str, context: Dict) -> Dict:
+        return {
+            'strategy': 'amplifier_cascade',
+            'cascade_strength': 0.7,
+            'estimated_reach': 1500
+        }
+
+    def _calculate_memetic_potential(self, content: str) -> float:
+        words = len(content.split())
+        simplicity = 1.0 - min(1.0, words / 100.0)
+        emotional_words = ['discover', 'reveal', 'truth', 'breakthrough', 'secret']
+        emotional = sum(1 for w in emotional_words if w in content.lower()) / len(emotional_words)
+        pattern = sum(1 for n in [3, 6, 9] if str(n) in content) / 3.0
+        return float(np.mean([simplicity, emotional, pattern]))
+
+    def _generate_memetic_variants(self, content: str, context: Dict) -> List[str]:
+        variants = [
+            f"BREAKING: {content}",
+            f"Scientists discover: {content}",
+            f"The truth about {content.split('.')[0] if '.' in content else content}",
+            f"3-6-9 Pattern Found: {content}"
+        ]
+        if 'domain' in context:
+            variants.append(f"{context['domain'].upper()} REVELATION: {content}")
+        return variants[:3]
+
+class AdaptiveValidationEngine:
+    def __init__(self, empirical_anchor: EmpiricalDataAnchor):
+        self.empirical_anchor = empirical_anchor
+        self.system_state_history = []
+        self.adaptive_thresholds = {
+            'resonance_min': EmpiricalConstants.BASE_RESONANCE_THRESHOLD,
+            'suppression_max': EmpiricalConstants.BASE_SUPPRESSION_THRESHOLD,
+            'coherence_min': EmpiricalConstants.BASE_COHERENCE_THRESHOLD
+        }
+
+    async def validate_with_adaptive_thresholds(self, node: Dict[str, Any]) -> Dict[str, Any]:
+        await self._update_adaptive_thresholds()
+        v = {}
+        v['resonance_valid'] = node.get('resonance', 0.5) > self.adaptive_thresholds['resonance_min']
+        v['suppression_valid'] = node.get('suppression_score', 1.0) < self.adaptive_thresholds['suppression_max']
+        v['coherence_valid'] = node.get('coherence', 0.5) > self.adaptive_thresholds['coherence_min']
+        emp = self.empirical_anchor.calculate_empirical_resonance_factor()
+        v['empirical_valid'] = emp > 0.6
+        v['cross_system_coherence'] = self._check_cross_system_coherence(node)
+        weights = {'resonance_valid': 0.25, 'suppression_valid': 0.25, 'coherence_valid': 0.20, 'empirical_valid': 0.15, 'cross_system_coherence': 0.15}
+        score = sum((1.0 if v[k] else 0.0) * w for k, w in weights.items())
+        v['validation_score'] = score
+        v['overall_valid'] = score > 0.7
+        self.system_state_history.append({
+            'timestamp': time.time(),
+            'thresholds': self.adaptive_thresholds.copy(),
+            'validation_score': score,
+            'empirical_factor': emp
+        })
+        self.system_state_history[:] = self.system_state_history[-1000:]
+        return v
+
+    async def _update_adaptive_thresholds(self):
+        emp = self.empirical_anchor.calculate_empirical_resonance_factor()
+        adjust = 0.8 + emp * 0.4
+        self.adaptive_thresholds = {
+            'resonance_min': EmpiricalConstants.BASE_RESONANCE_THRESHOLD * adjust,
+            'suppression_max': EmpiricalConstants.BASE_SUPPRESSION_THRESHOLD / adjust,
+            'coherence_min': EmpiricalConstants.BASE_COHERENCE_THRESHOLD * adjust
+        }
+
+    def _check_cross_system_coherence(self, node: Dict[str, Any]) -> bool:
+        metrics = [
+            node.get('resonance', 0),
+            node.get('coherence', 0),
+            node.get('energy_coherence', 0),
+            1 - node.get('suppression_score', 1),
+            node.get('trinity_unification_score', 0)
+        ]
+        return float(np.std(metrics)) < 0.3
+
+class EnhancedUnifiedRealityEngine:
+    def __init__(self):
+        self.empirical_anchor = EmpiricalDataAnchor()
+        self.anti_subversion = EnhancedAntiSubversion(self.empirical_anchor)
+        self.cognitive_reasoner = EnhancedCognitiveReasoner()
+        self.knowledge_graph = AdvancedKnowledgeGraph()
+        self.propagation_engine = MemeticPropagationEngine(self.knowledge_graph)
+        self.validation_engine = AdaptiveValidationEngine(self.empirical_anchor)
+
+    async def process_enhanced_claim(self, claim: Dict, parent_nodes: List[str] = None) -> Dict:
+        process_id = f"TRINITYv2-{hashlib.sha256(json.dumps(claim, default=str).encode()).hexdigest()[:12]}"
+        ok, integrity = await self.anti_subversion.quantum_integrity_check(claim)
+        if not ok:
+            return {'process_id': process_id, 'status': 'failed_integrity', 'integrity_scores': integrity}
+
+        await self.empirical_anchor.update_empirical_data()
+        cog = self.cognitive_reasoner.analyze_with_tesla_overlay(claim.get('content', ''))
+        energy_field = np.random.randn(256).astype(np.float32)
+        vibration_modes = np.random.randn(256).astype(np.float32)
+        node = self._create_enhanced_node(claim, cog, energy_field, vibration_modes, integrity)
+        self.knowledge_graph.add_node_with_lineage(node, parent_nodes or [])
+        validation = await self.validation_engine.validate_with_adaptive_thresholds(node)
+        strategy = 'resonance_echo' if node['resonance'] > 0.6 else 'memetic_seeding'
+        propagation = await self.propagation_engine.execute_propagation(node['id'], strategy, claim)
+
+        return {
+            'process_id': process_id,
+            'node_id': node['id'],
+            'integrity_scores': integrity,
+            'validation': validation,
+            'propagation': propagation,
+            'enhanced_metrics': {
+                'empirical_grounding': node['empirical_anchor'],
+                'harmonic_alignment': node['resonance'],
+                'adaptive_thresholds': self.validation_engine.adaptive_thresholds
+            },
+            'status': 'ok'
+        }
+
+    def _create_enhanced_node(self, claim: Dict, cog: Dict, energy_field: np.ndarray, vibration_modes: np.ndarray, integrity_scores: Dict) -> Dict:
+        node_id = f"TRINITYv2-{hashlib.sha256(json.dumps(claim, default=str).encode()).hexdigest()[:16]}"
+        empirical_factor = self.empirical_anchor.calculate_empirical_resonance_factor()
+        energy_coherence = float(1.0 / (np.std(energy_field) + 1e-9)) if energy_field is not None else 0.5
+        vibration_quality = float(np.std(vibration_modes) / (np.mean(np.abs(vibration_modes)) + 1e-9))
+        tesla_sig = {
+            **cog['frequency_profile'],
+            'harmonic_alignment': cog['harmonic_alignment'],
+            'energy_coherence': energy_coherence,
+            'vibration_quality': vibration_quality,
+            'empirical_resonance': empirical_factor
+        }
+        return {
+            'id': node_id,
+            'content': claim.get('content', ''),
+            'domain': claim.get('domain', 'general'),
+            'certainty': cog['certainty'],
+            'source_reliability': 0.8,
+            'suppression_score': 0.3,
+            'resonance': cog['harmonic_alignment'],
+            'coherence': cog['certainty'],
+            'energy_coherence': energy_coherence,
+            'tesla_signature': tesla_sig,
+            'empirical_anchor': empirical_factor,
+            'integrity_scores': integrity_scores,
+            'temporal_validity': (datetime.utcnow(), datetime.utcnow() + timedelta(days=365)),
+            'contradiction_flags': [],
+            'embedding': cog['semantic_embedding']
+        }
+
+# -----------------------------------------------------------------------------
+# Module 50 — Fact Engine (Empirical Historical Analysis)
+# -----------------------------------------------------------------------------
+
+class DataDomain(Enum):
+    ARCHAEOLOGICAL = "archaeological"
+    GEOLOGICAL = "geological"
+    ASTRONOMICAL = "astronomical"
+    HISTORICAL = "historical"
+    MYTHOLOGICAL = "mythological"
+    GENETIC = "genetic"
+
+@dataclass
+class EmpiricalFact:
+    domain: DataDomain
+    description: str
+    data_source: str
+    confidence: float
+    statistical_significance: float
+    supporting_evidence: List[str]
+    timestamp: datetime
+
+class ArchaeologicalAnalyzer:
+    def analyze_site_clusters(self, sites_data: np.ndarray) -> Dict[str, float]:
+        if len(sites_data) < 3:
+            return {'cluster_confidence': 0.0}
+        dates = sites_data[:, 0]
+        temporal = self._temporal_clustering(dates)
+        coords = sites_data[:, 1:3]
+        spatial = self._spatial_clustering(coords)
+        return {
+            'temporal_cluster_strength': temporal,
+            'spatial_cluster_strength': spatial,
+            'cluster_confidence': float((temporal + spatial) / 2.0)
+        }
+
+    def _temporal_clustering(self, dates: np.ndarray) -> float:
+        if len(dates) < 3:
+            return 0.0
+        nd = (dates - np.min(dates)) / max(np.ptp(dates), 1e-9)
+        gaps = np.diff(np.sort(nd))
+        if not len(gaps):
+            return 0.0
+        mean_gap = float(np.mean(gaps))
+        if mean_gap == 0:
+            return 0.0
+        clustering_index = 1 - (mean_gap / (1.0 / max(len(gaps), 1)))
+        return float(max(0.0, clustering_index))
+
+    def _spatial_clustering(self, coords: np.ndarray) -> float:
+        if len(coords) < 3:
+            return 0.0
+        # Simple dispersion proxy
+        std_lat = float(np.std(coords[:, 0]))
+        std_lon = float(np.std(coords[:, 1]))
+        disp = (std_lat + std_lon) / 2.0
+        return float(1.0 / (disp + 1e-3))
+
+class GeologicalEventAnalyzer:
+    def analyze_catastrophe_clusters(self, event_data: np.ndarray) -> Dict[str, float]:
+        if len(event_data) < 3:
+            return {'catastrophe_cluster_confidence': 0.0}
+        times = event_data[:, 0]
+        cluster = self._poisson_deviation(times)
+        magnitudes = event_data[:, 1] if event_data.shape[1] > 1 else np.array([])
+        mag_trend = self._magnitude_trend(times, magnitudes) if magnitudes.size else 0.0
+        return {
+            'temporal_clustering': cluster,
+            'magnitude_correlation': mag_trend,
+            'catastrophe_cluster_confidence': float((cluster + mag_trend) / 2.0)
+        }
+
+    def _poisson_deviation(self, times: np.ndarray) -> float:
+        if len(times) < 3:
+            return 0.0
+        gaps = np.diff(np.sort(times))
+        expected = float(np.mean(gaps))
+        if expected == 0:
+            return 0.0
+        cv = float(np.std(gaps) / expected)
+        strength = min(1.0, max(0.0, (cv - 1.0) / 2.0))
+        return strength
+
+    def _magnitude_trend(self, times: np.ndarray, mags: np.ndarray) -> float:
+        if len(mags) < 2:
+            return 0.0
+        try:
+            corr = float(np.corrcoef(times[:len(mags)], mags)[0, 1])
+            return clamp(abs(corr))
+        except Exception:
+            return 0.0
+
+class MythologicalPatternAnalyzer:
+    def analyze_myth_correlations(self, myth_data: Dict[str, List[str]]) -> Dict[str, float]:
+        if len(myth_data) < 2:
+            return {'myth_correlation_confidence': 0.0}
+        cultures = list(myth_data.keys())
+        M = np.zeros((len(cultures), len(cultures)))
+        for i, c1 in enumerate(cultures):
+            for j, c2 in enumerate(cultures):
+                if i == j: 
+                    continue
+                s1, s2 = set(myth_data[c1]), set(myth_data[c2])
+                union = len(s1.union(s2))
+                M[i, j] = len(s1.intersection(s2)) / max(union, 1)
+        avg = float(np.mean(M))
+        return {
+            'average_cross_cultural_correlation': avg,
+            'maximum_correlation': float(np.max(M)),
+            'myth_correlation_confidence': avg
+        }
+
+class StatisticalAnalyzer:
+    def calculate_confidence_interval(self, data: np.ndarray, confidence: float = 0.95) -> Tuple[float, float]:
+        if len(data) < 2:
+            return (0.0, 0.0)
+        mean = float(np.mean(data))
+        sem = float(stats.sem(data))
+        ci = stats.t.interval(confidence, len(data)-1, loc=mean, scale=sem)
+        return (float(ci[0]), float(ci[1]))
+
+    def test_significance(self, data1: np.ndarray, data2: np.ndarray) -> float:
+        if len(data1) < 3 or len(data2) < 3:
+            return 0.0
+        _, p = stats.ttest_ind(data1, data2, equal_var=False)
+        return clamp(1.0 - float(p))
+
+class FactEngine:
+    def __init__(self):
+        self.archaeological_analyzer = ArchaeologicalAnalyzer()
+        self.geological_analyzer = GeologicalEventAnalyzer()
+        self.mythological_analyzer = MythologicalPatternAnalyzer()
+        self.stats_analyzer = StatisticalAnalyzer()
+        self.verified_facts: List[EmpiricalFact] = []
+
+    def analyze_civilization_cycles(self, archaeological_data: np.ndarray, geological_data: np.ndarray, mythological_data: Dict[str, List[str]]) -> Dict[str, Any]:
+        arch = self.archaeological_analyzer.analyze_site_clusters(archaeological_data)
+        geo = self.geological_analyzer.analyze_catastrophe_clusters(geological_data)
+        myth = self.mythological_analyzer.analyze_myth_correlations(mythological_data)
+        cross = self._calculate_domain_correlations(arch, geo, myth)
+        overall = float(np.mean([arch['cluster_confidence'], geo['catastrophe_cluster_confidence'], myth['myth_correlation_confidence'], cross['cross_domain_alignment']]))
+        result = {
+            'timestamp': datetime.now().isoformat(),
+            'domain_results': {'archaeological': arch, 'geological': geo, 'mythological': myth},
+            'cross_domain_analysis': cross,
+            'overall_confidence': overall,
+            'civilization_cycle_hypothesis_supported': bool(overall > 0.7)
+        }
+        if overall > 0.7:
+            fact = EmpiricalFact(
+                domain=DataDomain.HISTORICAL,
+                description="Cross-domain evidence supporting cyclical civilization patterns",
+                data_source="Module 50 multi-domain correlation",
+                confidence=overall,
+                statistical_significance=cross['statistical_significance'],
+                supporting_evidence=[
+                    f"Archaeological clustering: {arch['cluster_confidence']:.3f}",
+                    f"Geological clustering: {geo['catastrophe_cluster_confidence']:.3f}",
+                    f"Mythological alignment: {myth['myth_correlation_confidence']:.3f}"
+                ],
+                timestamp=datetime.now()
+            )
+            self.verified_facts.append(fact)
+        return result
+
+    def _calculate_domain_correlations(self, arch: Dict, geo: Dict, myth: Dict) -> Dict[str, float]:
+        confidences = [arch['cluster_confidence'], geo['catastrophe_cluster_confidence'], myth['myth_correlation_confidence']]
+        alignment = 1 - (np.std(confidences) / 0.5)
+        return {
+            'cross_domain_alignment': float(max(0.0, alignment)),
+            'domain_consistency': float(1.0 - np.std(confidences)),
+            'statistical_significance': float(np.mean(confidences))
+        }
+
+    def get_verified_facts(self, min_confidence: float = 0.7) -> List[EmpiricalFact]:
+        return [f for f in self.verified_facts if f.confidence >= min_confidence]
+
+    def export_fact_report(self) -> Dict[str, Any]:
+        high = self.get_verified_facts(0.8)
+        med = self.get_verified_facts(0.6)
+        return {
+            'report_timestamp': datetime.now().isoformat(),
+            'total_facts_verified': len(self.verified_facts),
+            'high_confidence_facts': len(high),
+            'medium_confidence_facts': len(med),
+            'facts': [
+                {'description': f.description, 'domain': f.domain.value, 'confidence': f.confidence,
+                 'significance': f.statistical_significance, 'evidence': f.supporting_evidence}
+                for f in high
+            ]
+        }
+
+# -----------------------------------------------------------------------------
+# ITGARE v3.0 — Autonomous knowledge + Truth governance + Reality OS
+# -----------------------------------------------------------------------------
+
+@dataclass
+class EpistemicVector:
+    content_hash: str
+    dimensional_components: Dict[str, float]
+    confidence_metrics: Dict[str, float]
+    temporal_coordinates: Dict[str, Any]
+    relational_entanglements: List[str]
+    meta_cognition: Dict[str, Any]
+    security_signature: str
+    quantum_state: Optional[str] = None
+    truth_validation_score: float = 0.0
+    reality_integration_potential: float = 0.0
+    epistemic_coherence: float = field(init=False)
+
+    def __post_init__(self):
+        ds = safe_mean(list(self.dimensional_components.values()), 0.5)
+        cs = safe_mean(list(self.confidence_metrics.values()), 0.5)
+        rd = min(1.0, len(self.relational_entanglements) / 10.0)
+        qb = 0.1 if self.quantum_state else 0.0
+        tb = self.truth_validation_score * 0.2
+        self.epistemic_coherence = clamp(ds * 0.3 + cs * 0.25 + rd * 0.2 + qb * 0.15 + tb * 0.1)
+
+class QuantumSecurityContext:
+    def __init__(self):
+        self.temporal_signature = hashlib.sha3_512(datetime.utcnow().isoformat().encode()).hexdigest()
+        self.quantum_substrate = None
+
+    def generate_quantum_hash(self, data: Any) -> str:
+        raw = f"{str(data)}{self.temporal_signature}{secrets.token_hex(8)}"
+        if self.quantum_substrate:
+            try:
+                qs = self.quantum_substrate.create_truth_qubit(str(data))
+                raw += f"_{qs}"
+            except Exception:
+                pass
+        return hashlib.sha3_512(raw.encode()).hexdigest()
+
+class AutonomousKnowledgeActivation:
+    def __init__(self, integrated_os=None):
+        self.integrated_os = integrated_os
+        self.security_context = QuantumSecurityContext()
+        if integrated_os and hasattr(integrated_os, 'quantum_substrate'):
+            self.security_context.quantum_substrate = integrated_os.quantum_substrate
+        self.knowledge_domains = self._init_domains()
+        self.integration_triggers = {d: {'minimum_confidence': 0.6, 'truth_validation_required': True} for d in self.knowledge_domains.keys()}
+        self.epistemic_vectors: Dict[str, EpistemicVector] = {}
+        self.recursive_depth = 0
+        self.max_recursive_depth = 12
+        self.truth_threshold = 0.7
+
+    def _init_domains(self) -> Dict[str, Dict[str, Any]]:
+        return {
+            'archaeological': {'quantum_entanglement': 0.3},
+            'geological': {'quantum_entanglement': 0.4},
+            'mythological': {'quantum_entanglement': 0.5},
+            'astronomical': {'quantum_entanglement': 0.6},
+            'genetic': {'quantum_entanglement': 0.4},
+            'consciousness_studies': {'quantum_entanglement': 0.8}
+        }
+
+    async def activate_autonomous_research(self, truth_claim: str = None) -> Dict[str, Any]:
+        self.recursive_depth += 1
+        domain_vectors: Dict[str, EpistemicVector] = {}
+        for domain in list(self.knowledge_domains.keys()):
+            vec = await self._process_domain(domain, truth_claim)
+            domain_vectors[domain] = vec
+            self.epistemic_vectors[vec.content_hash] = vec
+        integrated = await self._integrate_vectors(domain_vectors, truth_claim)
+        self.recursive_depth -= 1
+        return {
+            'autonomous_research_activated': True,
+            'epistemic_vectors': self.epistemic_vectors,
+            'integrated_vector': integrated,
+            'recursion_depth': self.recursive_depth
+        }
+
+    async def _process_domain(self, domain: str, truth_claim: Optional[str]) -> EpistemicVector:
+        pattern_data = {
+            'domain': domain,
+            'timestamp': datetime.utcnow().isoformat(),
+            'detected_patterns': ['pattern_a', 'pattern_b', 'pattern_c'],
+            'pattern_confidence': float(np.random.rand() * 0.6 + 0.4),
+            'cross_domain_correlations': int(np.random.randint(1, 5)),
+            'truth_claim_relevance': float(0.7 if truth_claim else 0.3)
+        }
+        validation = None
+        if truth_claim and self.integrated_os:
+            try:
+                validation = await self.integrated_os.process_truth_claim_comprehensive(f"{domain} evidence for: {truth_claim}", [domain])
+            except Exception as e:
+                logger.warning(f"Domain truth validation failed: {e}")
+
+        dims = {
+            'pattern_density': float(np.random.rand() * 0.8 + 0.2),
+            'temporal_alignment': float(np.random.rand() * 0.7 + 0.3),
+            'quantum_coherence': float(self.knowledge_domains[domain]['quantum_entanglement']),
+            'cross_domain_correlation': float(np.random.rand() * 0.6 + 0.2)
+        }
+        confs = {
+            'domain_confidence': float(np.random.rand() * 0.8 + 0.2),
+            'evidence_quality': float(np.random.rand() * 0.7 + 0.3),
+            'methodological_rigor': float(np.random.rand() * 0.6 + 0.4)
+        }
+        truth_score = float(validation.get('comprehensive_confidence', 0.0) if validation else 0.0)
+        quantum_state = self.integrated_os.quantum_substrate.create_truth_qubit(domain) if (self.integrated_os and hasattr(self.integrated_os, 'quantum_substrate')) else None
+        vector = EpistemicVector(
+            content_hash=self.security_context.generate_quantum_hash(pattern_data),
+            dimensional_components=dims,
+            confidence_metrics=confs,
+            temporal_coordinates={'processed_at': datetime.utcnow().isoformat(), 'domain': domain, 'recursion_depth': self.recursive_depth},
+            relational_entanglements=list(self.knowledge_domains.keys()),
+            meta_cognition={'recursive_depth': self.recursive_depth, 'domain_specificity': domain, 'truth_integration_level': truth_score},
+            security_signature=self.security_context.generate_quantum_hash(pattern_data),
+            quantum_state=quantum_state,
+            truth_validation_score=truth_score,
+            reality_integration_potential=self._calc_reality_potential(domain, validation)
+        )
+        if (self.recursive_depth < self.max_recursive_depth) and (vector.truth_validation_score > self.truth_threshold) and (np.random.rand() > 0.6):
+            await self.activate_autonomous_research(truth_claim)
+        return vector
+
+    def _calc_reality_potential(self, domain: str, validation: Optional[Dict[str, Any]]) -> float:
+        base = float(self.knowledge_domains[domain]['quantum_entanglement'])
+        if not validation:
+            return base
+        conf = float(validation.get('comprehensive_confidence', 0.5))
+        suppression = float(validation.get('gathered_context', {}).get('suppression_analysis', {}).get('suppression_confidence', 0.5))
+        ready = bool(validation.get('reality_integration_ready', False))
+        pot = base * 0.3 + conf * 0.4 + (1.0 - suppression) * 0.2 + (1.0 if ready else 0.0) * 0.1
+        return clamp(pot)
+
+    async def _integrate_vectors(self, domain_vectors: Dict[str, EpistemicVector], truth_claim: Optional[str]) -> EpistemicVector:
+        dim_keys = ['pattern_density', 'temporal_alignment', 'quantum_coherence', 'cross_domain_correlation']
+        conf_keys = ['domain_confidence', 'evidence_quality', 'methodological_rigor']
+        dims = {k: float(np.mean([v.dimensional_components.get(k, 0.5) for v in domain_vectors.values()])) for k in dim_keys}
+        confs = {k: float(np.mean([v.confidence_metrics.get(k, 0.5) for v in domain_vectors.values()])) for k in conf_keys}
+        truth_enhancement = 1.0
+        if truth_claim and self.integrated_os:
+            try:
+                integrated_val = await self.integrated_os.process_truth_claim_comprehensive(f"Integrated knowledge claim: {truth_claim}", list(self.knowledge_domains.keys()))
+                truth_enhancement = float(integrated_val.get('comprehensive_confidence', 1.0))
+            except Exception as e:
+                logger.warning(f"Integrated validation failed: {e}")
+        dims = {k: clamp(v * (0.8 + 0.2 * truth_enhancement)) for k, v in dims.items()}
+        confs = {k: clamp(v * (0.7 + 0.3 * truth_enhancement)) for k, v in confs.items()}
+        integrated = EpistemicVector(
+            content_hash=self.security_context.generate_quantum_hash(domain_vectors),
+            dimensional_components=dims,
+            confidence_metrics=confs,
+            temporal_coordinates={'integration_time': datetime.utcnow().isoformat(), 'integration_depth': self.recursive_depth, 'truth_enhancement_applied': truth_enhancement},
+            relational_entanglements=list(domain_vectors.keys()),
+            meta_cognition={'integration_depth': self.recursive_depth, 'domain_count': len(domain_vectors), 'truth_integration_level': truth_enhancement, 'autonomous_research_cycle': True},
+            security_signature=self.security_context.generate_quantum_hash(domain_vectors),
+            quantum_state=self.integrated_os.quantum_substrate.create_truth_qubit("integrated_knowledge") if (self.integrated_os and hasattr(self.integrated_os, 'quantum_substrate')) else None,
+            truth_validation_score=truth_enhancement,
+            reality_integration_potential=float(np.mean([v.reality_integration_potential for v in domain_vectors.values()]))
+        )
+        return integrated
+
+# Placeholder subsystem classes for Reality OS components (minimal operational stubs)
+
+@dataclass
+class RealityShard:
+    mass: float
+    coherence: float
+    signature: str
+
+class RealityForge:
+    def compile_truth(self, truth_state: Dict[str, Any]) -> RealityShard:
+        mass = float(1.0 + np.random.rand() * 2.0)
+        coherence = clamp(float(np.random.rand() * 0.3 + 0.7))
+        signature = hashlib.sha256(json.dumps(truth_state, default=str).encode()).hexdigest()[:16]
+        return RealityShard(mass=mass, coherence=coherence, signature=signature)
+
+class TruthCombatUnit:
+    def engage_suppression(self, target: str) -> Dict[str, Any]:
+        return {'target': target, 'engagement_level': 0.85, 'status': 'deployed'}
+
+@dataclass
+class RealityUpdate:
+    observer_id: str
+    applied: bool
+    new_state_hash: str
+
+@dataclass
+class HumanObserver:
+    observer_id: str
+    profile: Dict[str, Any]
+
+class ConsciousnessOverrideEngine:
+    def consciousness_override(self, observer: HumanObserver, new_reality: Dict[str, Any]) -> RealityUpdate:
+        h = hashlib.sha256(json.dumps(new_reality, default=str).encode()).hexdigest()[:24]
+        return RealityUpdate(observer_id=observer.observer_id, applied=True, new_state_hash=h)
+
+class QuantumSubstrate:
+    def create_truth_qubit(self, payload: str) -> str:
+        return hashlib.blake2b(payload.encode(), digest_size=16).hexdigest()
+
+class LinguisticProcessor:
+    def encode_symbolic_truth(self, claim: str) -> str:
+        return hashlib.sha1(claim.encode()).hexdigest()
+
+class RetrocausalEngine:
+    def modulate(self, claim: str) -> float:
+        return clamp(len(claim) / 100.0)
+
+class NoosphereAPI:
+    def query_collective_consciousness(self, claim: str) -> Dict[str, Any]:
+        return {'collective_alignment': clamp(np.random.rand() * 0.5 + 0.25)}
+
+class ManifestationGate:
+    def queue_reality_update(self, truth_state: Dict[str, Any]) -> str:
+        sid = hashlib.md5(json.dumps(truth_state, default=str).encode()).hexdigest()[:12]
+        return sid
+
+class BayesianUncertaintyAnalyzer:
+    def __init__(self, model=None):
+        self.model = model
+    def estimate(self, scores: List[float]) -> float:
+        return float(np.std(scores))
+
+class IntegratedTruthGovernedModel:
+    def __init__(self, input_shape: Tuple[int, int, int], num_classes: int, autogenetic_engine: Any = None):
+        self.input_shape = input_shape
+        self.num_classes = num_classes
+        self.autogenetic_engine = autogenetic_engine
+
+    def predict_with_truth_governance(self, model_input: Any, claim: str, num_samples: int = 32) -> Dict[str, Any]:
+        samples = np.clip(np.random.randn(num_samples) * 0.08 + 0.85, 0.0, 1.0)
+        return {'confidence': samples.tolist(), 'mean_confidence': float(np.mean(samples))}
+
+# -----------------------------------------------------------------------------
+# Integrated Reality OS v3.1 — Unifying orchestrator
+# -----------------------------------------------------------------------------
+
+class IntegratedRealityOS:
+    def __init__(self):
+        # Subsystems
+        self.trinity = EnhancedUnifiedRealityEngine()
+        self.fact_engine = FactEngine()
+        self.autogenetic_engine = None  # Created lazily if needed
+        # Reality components
+        self.reality_forge = RealityForge()
+        self.truth_combat = TruthCombatUnit()
+        self.override_engine = ConsciousnessOverrideEngine()
+        self.quantum_substrate = QuantumSubstrate()
+        self.linguistic_processor = LinguisticProcessor()
+        self.retrocausal_engine = RetrocausalEngine()
+        self.noosphere_api = NoosphereAPI()
+        self.manifestation_gate = ManifestationGate()
+        self.truth_singularity = lambda s: hashlib.sha3_512(json.dumps(s, default=str).encode()).hexdigest()
+        self.truth_model = IntegratedTruthGovernedModel((28, 28, 1), 10)
+
+        # Module 51
+        self.autonomous_knowledge = AutonomousKnowledgeActivation(self)
+        logger.info("Unified Reality OS v3.1 initialized")
+
+    async def process_truth_claim_comprehensive(self, claim: str, domains: Optional[List[str]] = None) -> Dict[str, Any]:
+        # Phase 1: Module 51 autonomous knowledge
+        ak_results = await self.autonomous_knowledge.activate_autonomous_research(truth_claim=claim)
+
+        # Phase 2: TRINITY claim to ground a node (use claim as content)
+        tri_claim = {'content': claim, 'domain': (domains[0] if domains else 'general')}
+        tri_result = await self.trinity.process_enhanced_claim(tri_claim)
+
+        # Phase 3: Fact Engine (if domains touch cycles)
+        fe_result = None
+        if domains and any(d in ['archaeology', 'geology', 'mythology', 'astronomy', 'genetic'] for d in domains):
+            # Synthetic minimal run; real data would be passed here
+            archaeological_data = np.array([[12600, 37.2, 38.9],[11500, 29.9,31.1],[12800,37.2,38.9]])
+            geological_data = np.array([[12800,8.5],[11400,7.2],[8200,6.8]])
+            myth_data = {'sumerian':['great_flood'], 'biblical':['great_flood']}
+            fe_result = self.fact_engine.analyze_civilization_cycles(archaeological_data, geological_data, myth_data)
+
+        # Phase 4: Linguistic + quantum substrates
+        quantum_state = self.quantum_substrate.create_truth_qubit(claim)
+        symbolic_encoding = self.linguistic_processor.encode_symbolic_truth(claim)
+        collective_response = self.noosphere_api.query_collective_consciousness(claim)
+
+        # Phase 5: Model prediction
+        model_input = np.ones((1, 28, 28, 1), dtype=np.float32) * min(1.0, len(claim)/1000.0)
+        model_pred = self.truth_model.predict_with_truth_governance(model_input, claim, num_samples=40)
+
+        # Confidence fusion
+        trival = tri_result.get('validation', {}) if isinstance(tri_result, dict) else {}
+        validation_conf = trival.get('validation_score', 0.5)
+        ak_conf = ak_results.get('integrated_vector', {}).get('epistemic_coherence', 0.5)
+        model_conf = model_pred.get('mean_confidence', 0.5)
+        collective_align = collective_response.get('collective_alignment', 0.5)
+        fused_conf = clamp(np.average([ak_conf, validation_conf, model_conf, collective_align], weights=[0.3, 0.3, 0.25, 0.15]))
+
+        # Reality readiness
+        suppression_conf = tri_result.get('validation', {}).get('overall_valid', False)
+        autonomous_pot = ak_results.get('integrated_vector', {}).get('reality_integration_potential', 0.0)
+        readiness = (fused_conf > 0.75) and suppression_conf and (autonomous_pot > 0.7)
+
+        truth_state = {
+            'claim': claim,
+            'domains': domains or [],
+            'autonomous_knowledge': ak_results,
+            'trinity_result': tri_result,
+            'fact_engine_result': fe_result,
+            'model_prediction': model_pred,
+            'quantum_state': quantum_state,
+            'symbolic_encoding': symbolic_encoding,
+            'collective_response': collective_response,
+            'comprehensive_confidence': fused_conf,
+            'reality_integration_ready': readiness,
+            'processing_timestamp': datetime.utcnow().isoformat()
+        }
+
+        if readiness:
+            shard = self.reality_forge.compile_truth(truth_state)
+            truth_state['reality_shard'] = asdict(shard)
+            truth_state['singularity_hash'] = self.truth_singularity(truth_state)
+
+        return truth_state
+
+    def get_os_status(self) -> Dict[str, Any]:
+        return {
+            'reality_os': {
+                'graph_nodes': (len(self.trinity.knowledge_graph.graph.nodes) if self.trinity.knowledge_graph.graph else 0),
+                'empirical_last_update': self.trinity.empirical_anchor.last_update,
+                'db_connected': bool(self.trinity.knowledge_graph.db_conn)
+            },
+            'module_51': {
+                'vectors': len(self.autonomous_knowledge.epistemic_vectors),
+                'recursive_depth': self.autonomous_knowledge.recursive_depth
+            }
+        }
+
+# -----------------------------------------------------------------------------
+# Production APIs (top-level)
+# -----------------------------------------------------------------------------
+
+integrated_reality_os = IntegratedRealityOS()
+
+async def process_truth_claim_advanced(claim: str, domains: Optional[List[str]] = None) -> Dict[str, Any]:
+    return await integrated_reality_os.process_truth_claim_comprehensive(claim, domains)
+
+async def execute_autonomous_research(truth_claim: Optional[str] = None) -> Dict[str, Any]:
+    return await integrated_reality_os.autonomous_knowledge.activate_autonomous_research(truth_claim)
+
+def get_integrated_os_status() -> Dict[str, Any]:
+    base = integrated_reality_os.get_os_status()
+    return {
+        'reality_os': base['reality_os'],
+        'module_51': base['module_51'],
+        'timestamp': datetime.utcnow().isoformat()
+    }
+
+# -----------------------------------------------------------------------------
+# Demonstration (optional)
+# -----------------------------------------------------------------------------
+
+async def demo():
+    print("🚀 COMPLETE OMEGA VERITAS STACK — Unified Reality OS v3.1")
+    claims = [
+        "Consciousness interacts with physical reality via coordinated Bayesian resonance.",
+        "Civilization cycles correlate with geological and astronomical periodicities.",
+    ]
+    for i, c in enumerate(claims, 1):
+        print(f"\n🔮 Claim {i}: {c}")
+        res = await process_truth_claim_advanced(c, ["archaeology", "geology", "mythology"])
+        print(f"   Confidence: {res.get('comprehensive_confidence', 0.0):.3f}")
+        print(f"   Ready: {res.get('reality_integration_ready', False)}")
+        if 'reality_shard' in res:
+            shard = res['reality_shard']
+            print(f"   Shard: mass={shard['mass']:.2f}kg coherence={shard['coherence']:.3f}")
+    status = get_integrated_os_status()
+    print("\n🏗️ OS Status:", json.dumps(status, indent=2))
+
+if __name__ == "__main__":
+    asyncio.run(demo())