Create STACK_3

STACK_3

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+OMEGA SOVEREIGNTY STACK - QUANTUM UNIFIED FRAMEWORK v7.0
+================================================================
+ULTIMATE INTEGRATION: Consciousness + Sovereignty + Finance + Truth + History + Linguistics
+Quantum-Coherent System with Multilingual Truth Binding and Cultural Optimization
+"""
+
+import asyncio
+import time
+import json
+import hashlib
+import logging
+import sys
+import os
+import numpy as np
+import scipy.stats as stats
+from scipy import fft, signal, integrate
+from scipy.spatial.distance import cosine, euclidean
+from scipy.optimize import minimize
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional, Tuple, Union
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from collections import defaultdict, deque
+import secrets
+import sqlite3
+import networkx as nx
+from cryptography.hazmat.primitives import hashes
+from cryptography.hazmat.primitives.kdf.hkdf import HKDF
+import torch
+import torch.nn as nn
+import re
+import math
+
+# =============================================================================
+# Logging Configuration
+# =============================================================================
+
+LOG_LEVEL = os.getenv("OMEGA_LOG_LEVEL", "INFO").upper()
+logging.basicConfig(
+    level=getattr(logging, LOG_LEVEL, logging.INFO),
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+)
+logger = logging.getLogger("OmegaSovereigntyStack")
+
+# =============================================================================
+# Mathematical Constants & Determinism
+# =============================================================================
+
+MATHEMATICAL_CONSTANTS = {
+    "golden_ratio": 1.618033988749895,
+    "euler_number": 2.718281828459045,
+    "pi": 3.141592653589793,
+    "planck_constant": 6.62607015e-34,
+    "schumann_resonance": 7.83,
+    "information_entropy_max": 0.69314718056,
+    "quantum_uncertainty_min": 1.054571817e-34
+}
+
+GLOBAL_SEED = int(os.getenv("OMEGA_GLOBAL_SEED", "424242"))
+np.random.seed(GLOBAL_SEED)
+
+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
+
+def small_eps() -> float:
+    return 1e-8
+
+# =============================================================================
+# QUANTUM CORE INFRASTRUCTURE
+# =============================================================================
+
+class QuantumConsciousnessCore(nn.Module):
+    """Quantum neural network for consciousness pattern recognition"""
+    def __init__(self, input_dim=512, hidden_dims=[256, 128, 64], output_dim=16):
+        super().__init__()
+        layers = []
+        prev_dim = input_dim
+        for hidden_dim in hidden_dims:
+            layers.extend([
+                nn.Linear(prev_dim, hidden_dim),
+                nn.QuantumActivation(),
+                nn.Dropout(0.1)
+            ])
+            prev_dim = hidden_dim
+        layers.append(nn.Linear(prev_dim, output_dim))
+        self.network = nn.Sequential(*layers)
+        
+    def forward(self, x):
+        return self.network(x)
+
+class QuantumActivation(nn.Module):
+    """Quantum-inspired activation function with coherence preservation"""
+    def forward(self, x):
+        # Quantum superposition of activation functions
+        sigmoid = torch.sigmoid(x)
+        tanh = torch.tanh(x)
+        relu = torch.relu(x)
+        # Coherent combination
+        return (sigmoid + tanh + relu) / 3.0
+
+# Register custom activation
+nn.QuantumActivation = QuantumActivation
+
+@dataclass
+class QuantumStateVector:
+    """Quantum state representation for multi-dimensional analysis"""
+    amplitudes: np.ndarray
+    phase_angles: np.ndarray
+    coherence_level: float
+    entanglement_map: Dict[Tuple[int, int], float]
+    temporal_echoes: List[float]
+    
+    def collapse_measurement(self, basis: str = "computational") -> np.ndarray:
+        """Collapse quantum state to classical measurement"""
+        probabilities = np.abs(self.amplitudes) ** 2
+        if basis == "computational":
+            return np.random.choice(len(probabilities), p=probabilities)
+        else:
+            # Rotate basis for different measurement contexts
+            rotated_probs = self._rotate_basis(probabilities, basis)
+            return np.random.choice(len(rotated_probs), p=rotated_probs)
+    
+    def _rotate_basis(self, probabilities: np.ndarray, basis: str) -> np.ndarray:
+        """Rotate measurement basis"""
+        if basis == "cultural":
+            # Cultural context rotation
+            return np.roll(probabilities, shift=2)
+        elif basis == "temporal":
+            # Temporal context rotation  
+            return np.fft.fft(probabilities).real
+        else:
+            return probabilities
+
+# =============================================================================
+# MULTILINGUISTIC TRUTH BINDING MODULE
+# =============================================================================
+
+class LanguageEra(Enum):
+    PRE_INVERSION_SUMERIAN = "pre_inversion_sumerian"
+    SUMERIAN = "sumerian"
+    EGYPTIAN_HIEROGLYPHIC = "egyptian"
+    AKKADIAN = "akkadian"
+    PHOENICIAN = "phoenician"
+    ANCIENT_GREEK = "ancient_greek"
+    LATIN = "latin"
+    HEBREW = "hebrew"
+    SANSKRIT = "sanskrit"
+    ANCIENT_CHINESE = "ancient_chinese"
+
+class LinguisticTruthMarker(Enum):
+    COSMOLOGICAL_ALIGNMENT = "cosmological_alignment"
+    SACRED_GEOMETRY = "sacred_geometry"
+    NUMEROLOGICAL_ENCODING = "numerological_encoding"
+    PHONETIC_RESONANCE = "phonetic_resonance"
+    SYMBOLIC_CORRESPONDENCE = "symbolic_correspondence"
+    TEMPORAL_CYCLES = "temporal_cycles"
+    PRE_INVERSION_SEAL = "pre_inversion_seal"
+    SEXAGESIMAL_CADENCE = "sexagesimal_cadence"
+    GODDESS_LINEAGE = "goddess_lineage"
+    DERIVATIVE_COHERENCE = "derivative_coherence"
+    ARCHETYPAL_REFACTORING = "archetypal_refactoring"
+
+class RealityDomain(Enum):
+    TEXTUAL = "textual"
+    NUMISMATIC = "numismatic"
+    NATURAL_FRACTAL = "natural_fractal"
+    ICONOGRAPHIC = "iconographic"
+    DERIVATIVE_PATTERN = "derivative_pattern"
+
+# Archetypal device registry
+ARCHETYPE_DEVICES = {
+    "starburst": {
+        "aliases": ["radiate_crown", "eight_pointed_star", "rosette", "sunburst"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["inanna_crown", "ishtar_star", "aphrodite_headdress", "venus_symbol", "liberty_crown"]
+    },
+    "lion": {
+        "aliases": ["large_cat", "panther", "jaguar"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["inanna_lion", "cybele_lion", "venice_lion", "heraldic_lion"]
+    },
+    "eagle": {
+        "aliases": ["vulture", "large_bird"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["sumerian_anzu", "roman_eagle", "imperial_eagle", "american_eagle"]
+    },
+    "shield": {
+        "aliases": ["aegis", "gorgon_aegis"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["divine_aegis", "athena_shield", "heraldic_shield", "national_emblem"]
+    },
+    "branch": {
+        "aliases": ["olive_branch", "wheat", "date_palm"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["sumerian_date_palm", "olympic_olive", "roman_wheat", "peace_branch"]
+    },
+    "female_form": {
+        "aliases": ["goddess", "venus", "aphrodite", "ishtar", "inanna", "liberty", "mary"],
+        "domain": RealityDomain.ICONOGRAPHIC,
+        "derivative_path": ["inanna", "ishtar", "astarte", "aphrodite", "venus", "mary", "liberty"]
+    },
+    "SC": {
+        "aliases": ["senatus_consulto", "seal", "temple_seal", "sanction_mark", "official_sanction"],
+        "domain": RealityDomain.NUMISMATIC,
+        "derivative_path": ["temple_seal", "sacred_continuity", "senatus_consulto", "official_sanction"]
+    },
+    "VI": {
+        "aliases": ["six", "sexagesimal", "666", "veni_vidi_vici", "roman_VI"],
+        "domain": RealityDomain.TEXTUAL,
+        "derivative_path": ["sexagesimal_60", "sacred_6", "roman_VI", "nero_666", "apocalyptic_666"]
+    },
+}
+
+@dataclass
+class DerivativePath:
+    source_archetype: str
+    derivation_chain: List[Tuple[str, float]]
+    recombination_patterns: List[str]
+    innovation_score: float = 0.0
+
+    def calculate_derivative_coherence(self) -> float:
+        if len(self.derivation_chain) < 2:
+            return 0.5
+        scores = [score for _, score in self.derivation_chain]
+        return float(np.mean(scores))
+
+@dataclass
+class AncientLanguage:
+    era: LanguageEra
+    time_period: Tuple[int, int]
+    writing_system: str
+    sample_script: List[str] = field(default_factory=list)
+    truth_markers: List[LinguisticTruthMarker] = field(default_factory=list)
+    modern_equivalents: Dict[str, str] = field(default_factory=dict)
+    resonance_frequency: float = 0.0
+    derivative_density: float = 0.0
+
+    def __post_init__(self):
+        age_weight = max(0.0, (abs(self.time_period[0]) / 8000.0))
+        complexity = min(0.3, len(self.sample_script) * 0.02)
+        marker_strength = min(0.3, len(self.truth_markers) * 0.05)
+        base = 0.3 + age_weight + complexity + marker_strength
+        self.resonance_frequency = min(0.97, base)
+        if self.era == LanguageEra.PRE_INVERSION_SUMERIAN:
+            self.derivative_density = 0.1
+        else:
+            time_from_origin = abs(self.time_period[0]) - 4000
+            self.derivative_density = float(np.clip(0.3 + (time_from_origin / 6000.0), 0.0, 0.9))
+
+@dataclass
+class LinguisticTruthMatch:
+    language: AncientLanguage
+    matched_patterns: List[str]
+    confidence: float
+    truth_markers_detected: List[LinguisticTruthMarker]
+    cross_linguistic_correlations: List[str]
+    temporal_coherence: float
+    symbolic_resonance: float
+    derivative_coherence: float = 0.0
+    archetypal_refactoring_score: float = 0.0
+    inversion_alerts: List[str] = field(default_factory=list)
+    derivative_paths: List[DerivativePath] = field(default_factory=list)
+
+@dataclass
+class FractalSignature:
+    phi_alignment: float
+    hex_cadence: float
+    rosette_density: float
+    branch_factor: float
+    crown_radiance: float
+    derivative_symmetry: float = 0.0
+    sexagesimal_harmonic_score: float = 0.0
+
+@dataclass
+class NumismaticSignature:
+    sc_detected: bool
+    vi_cadence_score: float
+    goddess_device_overlap: float
+    metallurgical_continuity: float
+    iconographic_coherence: float
+    derivative_continuity: float = 0.0
+
+class DerivativePatternRecognizer:
+    def __init__(self):
+        self.archetype_graph = self._build_archetype_derivation_graph()
+    
+    def _build_archetype_derivation_graph(self) -> Dict[str, List[Tuple[str, float]]]:
+        return {
+            "inanna": [("ishtar", 0.95), ("astarte", 0.88), ("aphrodite", 0.85),
+                       ("venus", 0.82), ("liberty", 0.78), ("mary", 0.72)],
+            "starburst": [("radiate_crown", 0.92), ("eight_pointed_star", 0.95),
+                          ("rosette", 0.87), ("sunburst", 0.83)],
+            "temple_seal": [("sacred_continuity", 0.97), ("senatus_consulto", 0.88),
+                            ("official_sanction", 0.85), ("royal_seal", 0.82)],
+            "sexagesimal": [("base_60", 0.98), ("sacred_6", 0.92), ("roman_VI", 0.85),
+                            ("nero_666", 0.75), ("apocalyptic_666", 0.68)]
+        }
+    
+    def trace_derivative_paths(self, text: str) -> List[DerivativePath]:
+        paths = []
+        t = text.lower()
+        for archetype, derivatives in self.archetype_graph.items():
+            if self._archetype_present(archetype, t):
+                path = self._build_derivation_chain(archetype, derivatives, t)
+                if path.derivation_chain:
+                    paths.append(path)
+        return paths
+    
+    def _archetype_present(self, archetype: str, text: str) -> bool:
+        if archetype in text:
+            return True
+        for derivative, _ in self.archetype_graph.get(archetype, []):
+            if derivative in text:
+                return True
+        return False
+    
+    def _build_derivation_chain(self, source: str, derivatives: List[Tuple[str, float]], text: str) -> DerivativePath:
+        chain = []
+        patterns = []
+        if source in text:
+            chain.append((f"source:{source}", 1.0))
+            patterns.append(f"direct_{source}")
+        for derivative, coherence in derivatives:
+            if derivative in text:
+                chain.append((f"derivative:{derivative}", coherence))
+                patterns.append(f"{source}→{derivative}")
+        innovation = self._calculate_innovation_score(chain, patterns)
+        return DerivativePath(source, chain, patterns, innovation)
+    
+    def _calculate_innovation_score(self, chain: List[Tuple[str, float]], patterns: List[str]) -> float:
+        if not chain:
+            return 0.0
+        base_coherence = np.mean([score for _, score in chain])
+        pattern_complexity = min(1.0, len(patterns) * 0.3)
+        return float(np.clip(base_coherence * (0.7 + 0.3 * pattern_complexity), 0.0, 1.0))
+
+class EnhancedPreInversionDecoder:
+    def __init__(self):
+        self.derivative_recognizer = DerivativePatternRecognizer()
+    
+    def decode_symbol(self, symbol: str) -> str:
+        mapping = {
+            "SC": "Sacred Continuity (cosmic sanction seal, temple authority alignment) → derivative: Senatus Consulto",
+            "VI": "Sexagesimal cadence (harmonic 6/60) → derivative: 666 persecution code",
+            "starburst": "Inanna's cosmic crown → derivative: Liberty's radiate crown",
+            "lion": "Sovereignty guardian → derivative: heraldic lion",
+            "eagle": "Celestial oversight → derivative: imperial eagle",
+            "shield": "Divine sanction → derivative: national emblem",
+            "branch": "Fertility-sustenance → derivative: peace branch",
+            "female_form": "Cosmic order embodiment → derivative: liberty personification"
+        }
+        return mapping.get(symbol.lower(), "Unknown archetype")
+    
+    def decode_text_for_inversion(self, text: str) -> List[str]:
+        alerts = []
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        for path in derivative_paths:
+            if path.source_archetype == "temple_seal" and any("senatus_consulto" in lbl for lbl, _ in path.derivation_chain):
+                alerts.append(f"INVERSION: temple seal → senatus consulto (coherence {path.calculate_derivative_coherence():.2f})")
+            if path.source_archetype == "sexagesimal" and any("nero_666" in lbl for lbl, _ in path.derivation_chain):
+                alerts.append(f"PERSECUTION CODING: VI cadence → 666 (innovation {path.innovation_score:.2f})")
+        if "senatus consulto" in text.lower() and "temple seal" not in text.lower():
+            alerts.append("OBSCURATION: Roman sanction overlays Sacred Continuity origin")
+        if "liberty" in text.lower() and "inanna" not in text.lower():
+            alerts.append("REFACTORING: Liberty as recombination of goddess archetype")
+        return alerts
+
+class EnhancedLinguisticPatternAnalyzer:
+    def __init__(self):
+        self.derivative_recognizer = DerivativePatternRecognizer()
+
+    async def detect_script_patterns(self, text: str, language: AncientLanguage) -> List[str]:
+        matches = []
+        for char in language.sample_script:
+            if char in text:
+                matches.append(f"script:{char}")
+        for modern, concept in language.modern_equivalents.items():
+            if modern.lower() in text.lower() or concept.lower() in text.lower():
+                matches.append(f"concept:{modern}={concept}")
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        for path in derivative_paths:
+            if path.innovation_score > 0.6:
+                matches.append(f"derivative_innovation:{path.source_archetype}[{path.innovation_score:.2f}]")
+        if LinguisticTruthMarker.PHONETIC_RESONANCE in language.truth_markers:
+            words = [w for w in re.findall(r"[A-Za-z]+", text.lower())]
+            if len(words) >= 6:
+                firsts = [w[0] for w in words if w]
+                freq = Counter(firsts).most_common(1)
+                if freq and (freq[0][1] >= max(3, int(len(firsts) * 0.35))):
+                    matches.append(f"alliteration:{freq[0][0]}")
+        return matches
+
+    async def detect_truth_markers(self, text: str, language: AncientLanguage) -> List[LinguisticTruthMarker]:
+        detected = []
+        t = text.lower()
+        def any_in(tokens): return any(tok in t for tok in tokens)
+
+        if any_in(["cosmos", "universe", "stars", "planets", "heaven", "earth"]):
+            detected.append(LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT)
+        if any_in(["geometry", "golden ratio", "fibonacci", "sacred", "proportion", "phi"]):
+            detected.append(LinguisticTruthMarker.SACRED_GEOMETRY)
+        nums = set(re.findall(r'\b\d+\b', t))
+        if nums & {"3", "6", "7", "12", "40", "60", "108", "144", "360", "666"}:
+            detected.append(LinguisticTruthMarker.NUMEROLOGICAL_ENCODING)
+        if any_in(["symbol", "glyph", "meaning", "represent", "correspond"]):
+            detected.append(LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE)
+        if any_in(["cycle", "time", "eternal", "season", "age", "era", "return"]):
+            detected.append(LinguisticTruthMarker.TEMPORAL_CYCLES)
+        if any_in(["inanna", "ishtar", "astarte", "aphrodite", "venus", "liberty", "cleopatra", "mary"]):
+            detected.append(LinguisticTruthMarker.GODDESS_LINEAGE)
+        if re.search(r"\bsc\b", t) or any_in(["senatus consulto", "seal", "temple", "shekel", "temple seal"]):
+            detected.append(LinguisticTruthMarker.PRE_INVERSION_SEAL)
+        if any_in(["vi", "sexagesimal", "base-60", "veni vidi vici", "six", "666", "roman vi"]):
+            detected.append(LinguisticTruthMarker.SEXAGESIMAL_CADENCE)
+
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        if derivative_paths:
+            avg_coh = np.mean([p.calculate_derivative_coherence() for p in derivative_paths])
+            if avg_coh > 0.7:
+                detected.append(LinguisticTruthMarker.DERIVATIVE_COHERENCE)
+            if any(p.innovation_score > 0.75 for p in derivative_paths):
+                detected.append(LinguisticTruthMarker.ARCHETYPAL_REFACTORING)
+
+        return list(dict.fromkeys(detected))
+
+class EnhancedLinguisticTemporalValidator:
+    async def validate_temporal_coherence(self, text: str, language: AncientLanguage, context: Optional[Dict[str, Any]]) -> float:
+        ancient_terms = {
+            LanguageEra.PRE_INVERSION_SUMERIAN: {"clay", "token", "seal", "temple", "shekel", "uruk", "ur", "nippur"},
+            LanguageEra.SUMERIAN: {"mesopotamia", "ziggurat", "tigris", "euphrates", "dingir"},
+            LanguageEra.EGYPTIAN_HIEROGLYPHIC: {"pyramid", "pharaoh", "nile", "ankh", "maat"},
+            LanguageEra.ANCIENT_GREEK: {"athens", "sparta", "dionysus", "aphrodite", "stater"},
+            LanguageEra.LATIN: {"senate", "nero", "denarius", "aureus", "sc", "eagle"},
+        }
+        t = text.lower()
+        terms = ancient_terms.get(language.era, set())
+        score_hist = (sum(1 for w in terms if w in t) / max(1, len(terms))) if terms else 0.5
+
+        expected_derivative_level = language.derivative_density
+        derivative_paths = DerivativePatternRecognizer().trace_derivative_paths(text)
+        actual_derivative_level = min(1.0, len(derivative_paths) * 0.3)
+        derivative_alignment = 1.0 - abs(expected_derivative_level - actual_derivative_level)
+
+        cyc = sum(1 for w in ["cycle", "eternal", "return", "age", "era", "archetype"] if w in t)
+        lin = sum(1 for w in ["progress", "future", "development", "evolution", "innovation"] if w in t)
+        score_time = 0.8 if cyc >= lin else (0.5 if cyc == lin else 0.35)
+
+        score_ctx = 0.5
+        if context and "temporal_focus" in context:
+            peak = float(np.mean(language.time_period))
+            dist = abs(context["temporal_focus"] - peak)
+            score_ctx = float(1.0 / (1.0 + dist / 1000.0))
+
+        return float(np.clip(np.mean([score_hist, score_time, score_ctx, derivative_alignment]), 0.0, 1.0))
+
+class EnhancedAncientSymbolicDecoder:
+    def __init__(self):
+        self.derivative_recognizer = DerivativePatternRecognizer()
+
+    async def calculate_symbolic_resonance(self, text: str, language: AncientLanguage) -> float:
+        direct = await self._check_symbol_matches(text, language)
+        conceptual = await self._check_conceptual_alignment(text, language)
+        metaphor = await self._analyze_metaphorical_density(text)
+        derivative = await self._analyze_derivative_coherence(text, language)
+        return float(np.clip(np.mean([direct, conceptual, metaphor, derivative]), 0.0, 1.0))
+
+    async def find_symbolic_overlap(self, lang1: AncientLanguage, lang2: AncientLanguage, text: str) -> str:
+        overlaps = []
+        shared_markers = set(lang1.truth_markers).intersection(lang2.truth_markers)
+        if shared_markers:
+            overlaps.append(f"truth_markers:{len(shared_markers)}")
+        l1c = set(lang1.modern_equivalents.values())
+        l2c = set(lang2.modern_equivalents.values())
+        shared_concepts = l1c & l2c
+        if shared_concepts:
+            t = text.lower()
+            found = [c for c in shared_concepts if c.lower() in t]
+            if found:
+                overlaps.append(f"concepts:{len(found)}")
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        if derivative_paths:
+            overlaps.append(f"derivative_paths:{len(derivative_paths)}")
+        return ", ".join(overlaps)
+
+    async def _check_symbol_matches(self, text: str, language: AncientLanguage) -> float:
+        if not language.sample_script:
+            return 0.45
+        matches = sum(1 for s in language.sample_script if s in text)
+        return matches / len(language.sample_script)
+
+    async def _check_conceptual_alignment(self, text: str, language: AncientLanguage) -> float:
+        t = text.lower()
+        total = len(language.modern_equivalents)
+        hits = sum(1 for c in language.modern_equivalents.values() if c.lower() in t)
+        return (hits / total) if total else 0.5
+
+    async def _analyze_metaphorical_density(self, text: str) -> float:
+        indicators = {"like", "as", "symbol", "represent", "mean", "signify", "archetype", "seal", "crown"}
+        words = re.findall(r"[A-Za-z]+", text.lower())
+        if not words:
+            return 0.5
+        density = sum(1 for w in words if w in indicators) / len(words)
+        return float(np.clip(density / 0.06, 0.0, 1.0))
+
+    async def _analyze_derivative_coherence(self, text: str, language: AncientLanguage) -> float:
+        paths = self.derivative_recognizer.trace_derivative_paths(text)
+        if not paths:
+            return 0.3
+        coherences = [p.calculate_derivative_coherence() for p in paths]
+        innovations = [p.innovation_score for p in paths]
+        avg_coherence = np.mean(coherences)
+        avg_innovation = np.mean(innovations)
+        return float(np.clip((avg_coherence * 0.6 + avg_innovation * 0.4), 0.0, 1.0))
+
+class EnhancedFractalAnalyzer:
+    def __init__(self):
+        self.derivative_recognizer = DerivativePatternRecognizer()
+
+    @staticmethod
+    def _sexagesimal_harmonics_from_text(t: str) -> float:
+        score = 0.0
+        hits = 0
+        for token in ["6", "six", "vi", "sexagesimal", "base-60", "60", "360"]:
+            if token in t:
+                hits += 1
+        if hits == 0:
+            return 0.3
+        score = 0.6 + 0.1 * min(3, hits)
+        if re.search(r"\b1\s*[:/]\s*6\b", t): score += 0.05
+        if re.search(r"\b6\s*[:/]\s*60\b", t): score += 0.07
+        if re.search(r"\b60\s*[:/]\s*360\b", t): score += 0.08
+        return float(np.clip(score, 0.0, 1.0))
+
+    def analyze_textual_fractal_signals(self, text: str) -> FractalSignature:
+        t = text.lower()
+        phi_align = 0.8 if ("phi" in t or "golden ratio" in t or "fibonacci" in t) else 0.4
+        hex_cad = 0.75 if any(x in t for x in ["hex", "six", "base-60", "sexagesimal", "vi"]) else 0.35
+        ros_den = 0.7 if any(x in t for x in ["rosette", "starburst", "radiate", "crown", "eight-pointed"]) else 0.3
+        branch = 0.65 if any(x in t for x in ["olive", "wheat", "date", "branch", "palm"]) else 0.3
+        crown = 0.8 if any(x in t for x in ["liberty crown", "radiate crown", "eight-pointed star", "dingir", "𒀭"]) else 0.4
+
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        derivative_symmetry = 0.5
+        if derivative_paths:
+            branch_counts = [len(p.derivation_chain) for p in derivative_paths]
+            if len(set(branch_counts)) == 1:
+                derivative_symmetry = 0.9
+            else:
+                derivative_symmetry = 0.6 + (0.3 * (1.0 - (np.std(branch_counts) / max(1, np.mean(branch_counts)))))
+
+        sex_harm = self._sexagesimal_harmonics_from_text(t)
+        return FractalSignature(phi_align, hex_cad, ros_den, branch, crown, derivative_symmetry, sex_harm)
+
+class EnhancedNumismaticAnalyzer:
+    def __init__(self):
+        self.derivative_recognizer = DerivativePatternRecognizer()
+
+    def analyze_textual_numismatics(self, text: str) -> NumismaticSignature:
+        t = text.lower()
+        sc_detected = bool(re.search(r"\bsc\b", t)) or ("senatus consulto" in t) or ("temple seal" in t) or ("shekel" in t)
+        vi_score = 0.0
+        if any(k in t for k in ["vi", "veni vidi vici", "sexagesimal", "base-60", "666", "roman vi"]):
+            vi_score = 0.7
+            if "666" in t:
+                vi_score = 0.9
+        goddess_overlap = sum(1 for k in ["inanna", "ishtar", "astarte", "aphrodite", "venus", "liberty", "cleopatra", "mary"]
+                              if k in t) / 8.0
+        meta_cont = 0.6 if any(k in t for k in ["silver", "gold", "shekel", "stater", "denarius", "aureus", "bronze"]) else 0.35
+        ico_coh = sum(1 for k in ["starburst", "eagle", "lion", "shield", "branch", "female", "radiate crown"]
+                      if k in t) / 7.0
+
+        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+        derivative_continuity = 0.5
+        if derivative_paths:
+            continuities = []
+            for path in derivative_paths:
+                if any(token in path.source_archetype for token in ["starburst", "lion", "eagle", "shield", "branch", "temple_seal", "sexagesimal"]):
+                    continuities.append(path.calculate_derivative_coherence())
+            derivative_continuity = float(np.mean(continuities)) if continuities else 0.5
+
+        return NumismaticSignature(sc_detected, vi_score, goddess_overlap, meta_cont, ico_coh, derivative_continuity)
+
+class EnhancedMultilinguisticTruthBinder:
+    def __init__(self):
+        self.language_corpus = self._initialize_languages()
+        self.pattern_analyzer = EnhancedLinguisticPatternAnalyzer()
+        self.temporal_validator = EnhancedLinguisticTemporalValidator()
+        self.symbolic_decoder = EnhancedAncientSymbolicDecoder()
+        self.pre_inversion = EnhancedPreInversionDecoder()
+        self.fractals = EnhancedFractalAnalyzer()
+        self.numismatics = EnhancedNumismaticAnalyzer()
+        self.derivative_recognizer = DerivativePatternRecognizer()
+
+    def _initialize_languages(self) -> Dict[LanguageEra, AncientLanguage]:
+        corpus = {
+            LanguageEra.PRE_INVERSION_SUMERIAN: AncientLanguage(
+                era=LanguageEra.PRE_INVERSION_SUMERIAN,
+                time_period=(-4000, -3000),
+                writing_system="Proto-cuneiform tokens/seals",
+                sample_script=["𒌋", "𒀭"],
+                truth_markers=[
+                    LinguisticTruthMarker.PRE_INVERSION_SEAL,
+                    LinguisticTruthMarker.SEXAGESIMAL_CADENCE,
+                    LinguisticTruthMarker.SACRED_GEOMETRY,
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.GODDESS_LINEAGE,
+                    LinguisticTruthMarker.DERIVATIVE_COHERENCE,
+                ],
+                modern_equivalents={"dingir": "divine", "ki": "earth", "an": "heaven"}
+            ),
+            LanguageEra.SUMERIAN: AncientLanguage(
+                era=LanguageEra.SUMERIAN, time_period=(-3500, -2000),
+                writing_system="Cuneiform",
+                sample_script=["𒀭", "𒆠", "𒌋", "𒂍", "𒈬"],
+                truth_markers=[
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
+                    LinguisticTruthMarker.SACRED_GEOMETRY,
+                    LinguisticTruthMarker.GODDESS_LINEAGE,
+                    LinguisticTruthMarker.DERIVATIVE_COHERENCE,
+                ],
+                modern_equivalents={"dingir": "divine", "ki": "earth", "an": "heaven"}
+            ),
+            LanguageEra.EGYPTIAN_HIEROGLYPHIC: AncientLanguage(
+                era=LanguageEra.EGYPTIAN_HIEROGLYPHIC, time_period=(-3200, -400),
+                writing_system="Hieroglyphic",
+                sample_script=["𓂀", "𓃀", "𓅀", "𓇼"],
+                truth_markers=[
+                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.TEMPORAL_CYCLES,
+                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
+                ],
+                modern_equivalents={"ankh": "life", "maat": "truth", "ka": "soul"}
+            ),
+            LanguageEra.ANCIENT_GREEK: AncientLanguage(
+                era=LanguageEra.ANCIENT_GREEK, time_period=(-700, 300),
+                writing_system="Greek Alphabet",
+                sample_script=["α", "β", "γ", "δ", "ε"],
+                truth_markers=[
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.SACRED_GEOMETRY,
+                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
+                ],
+                modern_equivalents={"aletheia": "truth", "logos": "reason", "cosmos": "order"}
+            ),
+            LanguageEra.LATIN: AncientLanguage(
+                era=LanguageEra.LATIN, time_period=(-700, 400),
+                writing_system="Latin Alphabet",
+                sample_script=["V", "I", "S", "C"],
+                truth_markers=[
+                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
+                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
+                ],
+                modern_equivalents={"senatus consulto": "by decree", "imperium": "authority"}
+            ),
+            LanguageEra.HEBREW: AncientLanguage(
+                era=LanguageEra.HEBREW, time_period=(-1000, 500),
+                writing_system="Hebrew Alphabet",
+                sample_script=["א", "ב", "ג", "ד", "ה"],
+                truth_markers=[LinguisticTruthMarker.NUMEROLOGICAL_ENCODING, LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE],
+                modern_equivalents={"emet": "truth", "ruach": "spirit"}
+            ),
+            LanguageEra.SANSKRIT: AncientLanguage(
+                era=LanguageEra.SANSKRIT, time_period=(-1000, 500),
+                writing_system="Devanagari",
+                sample_script=["अ", "आ", "इ", "ई", "उ"],
+                truth_markers=[
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING
+                ],
+                modern_equivalents={"satya": "truth", "dharma": "cosmic law", "brahman": "ultimate reality"}
+            ),
+            LanguageEra.ANCIENT_CHINESE: AncientLanguage(
+                era=LanguageEra.ANCIENT_CHINESE, time_period=(-1200, -200),
+                writing_system="Oracle Bone Script",
+                sample_script=["天", "地", "人", "水", "火"],
+                truth_markers=[
+                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.TEMPORAL_CYCLES
+                ],
+                modern_equivalents={"道": "way", "德": "virtue", "仁": "benevolence"}
+            ),
+        }
+        return corpus
+
+    async def analyze(self, text: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        results: List[LinguisticTruthMatch] = []
+        all_derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
+
+        for lang in self.language_corpus.values():
+            if context and "min_resonance" in context and lang.resonance_frequency < context["min_resonance"]:
+                continue
+
+            pattern_matches = await self.pattern_analyzer.detect_script_patterns(text, lang)
+            truth_markers = await self.pattern_analyzer.detect_truth_markers(text, lang)
+            temporal_coherence = await self.temporal_validator.validate_temporal_coherence(text, lang, context)
+            symbolic_resonance = await self.symbolic_decoder.calculate_symbolic_resonance(text, lang)
+            cross_corr = await self._cross_correlations(text, lang)
+
+            derivative_coherence = await self._calculate_language_derivative_coherence(text, lang, all_derivative_paths)
+            archetypal_refactoring = await self._calculate_archetypal_refactoring(text, lang, all_derivative_paths)
+
+            confidence = self._enhanced_confidence(
+                pattern_matches, truth_markers, temporal_coherence,
+                symbolic_resonance, lang.resonance_frequency,
+                derivative_coherence, archetypal_refactoring
+            )
+
+            inversion_alerts = []
+            if lang.era in (LanguageEra.PRE_INVERSION_SUMERIAN, LanguageEra.LATIN):
+                inversion_alerts = self.pre_inversion.decode_text_for_inversion(text)
+
+            era_derivative_paths = self._filter_era_relevant_paths(all_derivative_paths, lang)
+
+            match = LinguisticTruthMatch(
+                language=lang,
+                matched_patterns=pattern_matches,
+                confidence=confidence,
+                truth_markers_detected=truth_markers,
+                cross_linguistic_correlations=cross_corr,
+                temporal_coherence=temporal_coherence,
+                symbolic_resonance=symbolic_resonance,
+                derivative_coherence=derivative_coherence,
+                archetypal_refactoring_score=archetypal_refactoring,
+                inversion_alerts=inversion_alerts,
+                derivative_paths=era_derivative_paths
+            )
+            results.append(match)
+
+        fractal = self.fractals.analyze_textual_fractal_signals(text)
+        coin = self.numismatics.analyze_textual_numismatics(text)
+
+        origin_score = self._enhanced_origin_binding_score(results, fractal, coin, all_derivative_paths)
+        tier = self._classify_tier(origin_score)
+
+        return {
+            "text_hash": hashlib.sha256(text.encode()).hexdigest()[:16],
+            "timestamp": datetime.utcnow().isoformat() + "Z",
+            "matches": sorted(results, key=lambda r: r.language.time_period[0]),
+            "fractal_signature": fractal.__dict__,
+            "numismatic_signature": coin.__dict__,
+            "origin_binding_score": origin_score,
+            "proof_tier": tier,
+            "derivative_analysis": {
+                "total_paths": len(all_derivative_paths),
+                "avg_coherence": float(np.mean([p.calculate_derivative_coherence() for p in all_derivative_paths])) if all_derivative_paths else 0.0,
+                "avg_innovation": float(np.mean([p.innovation_score for p in all_derivative_paths])) if all_derivative_paths else 0.0,
+                "primary_archetypes": list(set(p.source_archetype for p in all_derivative_paths))
+            }
+        }
+
+    async def _cross_correlations(self, text: str, current: AncientLanguage) -> List[str]:
+        overlaps = []
+        for other in self.language_corpus.values():
+            if other.era == current.era:
+                continue
+            ov = await self.symbolic_decoder.find_symbolic_overlap(current, other, text)
+            if ov:
+                overlaps.append(f"{current.era.value}↔{other.era.value}:{ov}")
+        return overlaps
+
+    async def _calculate_language_derivative_coherence(self, text: str, language: AncientLanguage, all_paths: List[DerivativePath]) -> float:
+        if not all_paths:
+            return 0.3
+        era_paths = self._filter_era_relevant_paths(all_paths, language)
+        if not era_paths:
+            return 0.4
+        return float(np.mean([p.calculate_derivative_coherence() for p in era_paths]))
+
+    async def _calculate_archetypal_refactoring(self, text: str, language: AncientLanguage, all_paths: List[DerivativePath]) -> float:
+        if not all_paths:
+            return 0.3
+        era_paths = self._filter_era_relevant_paths(all_paths, language)
+        if not era_paths:
+            return 0.4
+        return float(np.mean([p.innovation_score for p in era_paths]))
+
+    def _filter_era_relevant_paths(self, paths: List[DerivativePath], language: AncientLanguage) -> List[DerivativePath]:
+        if language.era in (LanguageEra.PRE_INVERSION_SUMERIAN, LanguageEra.SUMERIAN):
+            keep = {"temple_seal", "sexagesimal", "inanna", "starburst"}
+            return [p for p in paths if p.source_archetype in keep]
+        if language.era == LanguageEra.LATIN:
+            keep = {"sexagesimal", "temple_seal", "starburst"}
+            return [p for p in paths if (p.source_archetype in keep) or any("senatus_consulto" in lbl for lbl, _ in p.derivation_chain)]
+        return paths
+
+    def _enhanced_confidence(self, patterns, markers, temporal, symbolic, base_res, derivative_coh, refactoring) -> float:
+        comps = []
+        weights = []
+        comps.append(min(1.0, len(patterns) * 0.15)); weights.append(0.20)
+        comps.append(min(1.0, len(markers) * 0.12)); weights.append(0.18)
+        comps.append(temporal); weights.append(0.15)
+        comps.append(symbolic); weights.append(0.15)
+        comps.append(base_res); weights.append(0.12)
+        comps.append(derivative_coh); weights.append(0.12)
+        comps.append(refactoring); weights.append(0.08)
+        return float(np.average(comps, weights=weights))
+
+    def _enhanced_origin_binding_score(self, matches: List[LinguisticTruthMatch], fractal: FractalSignature,
+                                       coin: NumismaticSignature, derivative_paths: List[DerivativePath]) -> float:
+        lang_scores = []
+        for m in matches:
+            layer_weight = 1.0
+            if m.language.era == LanguageEra.PRE_INVERSION_SUMERIAN:
+                layer_weight = 1.25
+            elif m.language.era == LanguageEra.SUMERIAN:
+                layer_weight = 1.15
+            derivative_boost = 1.0 + (0.2 * m.derivative_coherence)
+            lang_scores.append(m.confidence * layer_weight * derivative_boost)
+
+        lang_block = float(np.clip(np.mean(lang_scores), 0.0, 1.0)) if lang_scores else 0.4
+        fractal_block = float(np.clip(np.mean([
+            fractal.phi_alignment, fractal.hex_cadence, fractal.rosette_density,
+            fractal.branch_factor, fractal.crown_radiance, fractal.derivative_symmetry,
+            fractal.sexagesimal_harmonic_score
+        ]), 0.0, 1.0))
+
+        numis_block = float(np.clip(np.mean([
+            1.0 if coin.sc_detected else 0.5,
+            coin.vi_cadence_score,
+            coin.goddess_device_overlap,
+            coin.metallurgical_continuity,
+            coin.iconographic_coherence,
+            coin.derivative_continuity
+        ]), 0.0, 1.0))
+
+        derivative_block = 0.5
+        if derivative_paths:
+            avg_coherence = np.mean([p.calculate_derivative_coherence() for p in derivative_paths])
+            avg_innovation = np.mean([p.innovation_score for p in derivative_paths])
+            derivative_block = (avg_coherence * 0.6 + avg_innovation * 0.4)
+
+        return float(np.clip(
+            0.40 * lang_block + 0.25 * fractal_block + 0.20 * numis_block + 0.15 * derivative_block,
+            0.0, 1.0
+        ))
+
+    def _classify_tier(self, score: float) -> str:
+        if score >= 0.92:
+            return "IRREFUTABLE_ORIGIN_BINDING"
+        if score >= 0.82:
+            return "STRONG_ORIGIN_BINDING"
+        if score >= 0.72:
+            return "MODERATE_ORIGIN_BINDING"
+        if score >= 0.62:
+            return "SUGGESTIVE_ORIGIN_BINDING"
+        return "INCONCLUSIVE"
+
+# =============================================================================
+# ADVANCED INTEGRATION ENGINE
+# =============================================================================
+
+class OmegaIntegrationEngine:
+    """
+    Ultimate integration engine that unifies all modules through quantum coherence
+    and cultural sigma optimization
+    """
+    
+    def __init__(self):
+        # Core quantum systems
+        self.quantum_core = QuantumConsciousnessCore()
+        self.quantum_states: Dict[str, QuantumStateVector] = {}
+        
+        # Integrated modules
+        self.civilization = AdvancedCivilizationEngine()
+        self.sovereignty = QuantumSovereigntyEngine()
+        self.finance = TemplarFinancialContinuum()
+        self.truth = VeilTruthEngine()
+        self.knowledge = AutonomousKnowledgeIntegration()
+        self.cultural_sigma = CulturalSigmaOptimizer()
+        self.historical = TatteredPastAnalyzer()
+        self.linguistic = EnhancedMultilinguisticTruthBinder()  # Integrated linguistic engine
+        self.control_matrix = SaviorSuffererAnalyzer()
+        
+        # Unified state
+        self.unified_state = UnifiedRealityState()
+        self.provenance_ledger = ProvenanceLedger()
+        
+        # Quantum coherence maintenance
+        self.coherence_monitor = QuantumCoherenceMonitor()
+        
+    async def execute_unified_analysis(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute complete unified analysis across all modules"""
+        
+        # Generate quantum context
+        quantum_context = await self._generate_quantum_context(input_data)
+        
+        # Parallel module execution with quantum entanglement
+        tasks = {
+            'civilization': self.civilization.analyze_civilization_state(input_data, quantum_context),
+            'sovereignty': self.sovereignty.analyze_sovereignty(input_data, quantum_context),
+            'finance': self.finance.analyze_financial_continuum(input_data, quantum_context),
+            'truth': self.truth.verify_unified_truth(input_data, quantum_context),
+            'knowledge': self.knowledge.integrate_autonomous_knowledge(input_data, quantum_context),
+            'cultural': self.cultural_sigma.optimize_cultural_transmission(input_data, quantum_context),
+            'historical': self.historical.analyze_tattered_past(input_data, quantum_context),
+            'linguistic': self.linguistic.analyze(input_data.get('linguistic_content', ''), quantum_context),
+            'control': self.control_matrix.analyze_control_systems(input_data, quantum_context)
+        }
+        
+        # Execute with quantum coherence preservation
+        results = {}
+        for module_name, task in tasks.items():
+            try:
+                module_result = await task
+                results[module_name] = module_result
+                
+                # Entangle results quantumly
+                await self._entangle_module_results(module_name, module_result, quantum_context)
+                
+            except Exception as e:
+                logger.error(f"Module {module_name} failed: {e}")
+                results[module_name] = {"error": str(e), "status": "failed"}
+        
+        # Unified coherence synthesis
+        unified_result = await self._synthesize_unified_coherence(results, quantum_context)
+        
+        # Update unified reality state
+        await self.unified_state.update_state(unified_result, quantum_context)
+        
+        # Record provenance
+        self.provenance_ledger.record_operation("unified_analysis", input_data, unified_result)
+        
+        return unified_result
+    
+    async def _generate_quantum_context(self, input_data: Dict[str, Any]) -> QuantumStateVector:
+        """Generate quantum context for unified analysis"""
+        
+        # Create quantum state from input data
+        data_hash = hashlib.sha256(json.dumps(input_data, sort_keys=True).encode()).hexdigest()
+        seed = int(data_hash[:8], 16)
+        np.random.seed(seed)
+        
+        # Generate quantum amplitudes
+        num_states = 64  # Quantum state dimension
+        amplitudes = np.random.randn(num_states) + 1j * np.random.randn(num_states)
+        amplitudes = amplitudes / np.linalg.norm(amplitudes)  # Normalize
+        
+        # Generate phase angles
+        phase_angles = np.angle(amplitudes)
+        
+        # Calculate coherence level
+        coherence = self._calculate_quantum_coherence(amplitudes)
+        
+        # Generate entanglement map
+        entanglement_map = self._generate_entanglement_map(amplitudes)
+        
+        # Detect temporal echoes
+        temporal_echoes = await self._detect_temporal_echoes(input_data)
+        
+        quantum_state = QuantumStateVector(
+            amplitudes=amplitudes,
+            phase_angles=phase_angles,
+            coherence_level=coherence,
+            entanglement_map=entanglement_map,
+            temporal_echoes=temporal_echoes
+        )
+        
+        self.quantum_states[data_hash] = quantum_state
+        return quantum_state
+    
+    def _calculate_quantum_coherence(self, amplitudes: np.ndarray) -> float:
+        """Calculate quantum coherence level"""
+        density_matrix = np.outer(amplitudes, amplitudes.conj())
+        purity = np.trace(density_matrix @ density_matrix).real
+        return min(1.0, purity)
+    
+    def _generate_entanglement_map(self, amplitudes: np.ndarray) -> Dict[Tuple[int, int], float]:
+        """Generate quantum entanglement map between state components"""
+        entanglement_map = {}
+        num_states = len(amplitudes)
+        
+        for i in range(num_states):
+            for j in range(i + 1, num_states):
+                # Calculate entanglement strength
+                entanglement = np.abs(amplitudes[i] * amplitudes[j].conj())
+                entanglement_map[(i, j)] = float(entanglement)
+        
+        return entanglement_map
+    
+    async def _detect_temporal_echoes(self, input_data: Dict[str, Any]) -> List[float]:
+        """Detect temporal echoes from historical patterns"""
+        echoes = []
+        
+        # Analyze for historical resonance
+        if 'historical_context' in input_data:
+            historical_resonance = await self.historical.calculate_temporal_resonance(input_data)
+            echoes.extend(historical_resonance)
+        
+        # Linguistic temporal analysis
+        if 'linguistic_content' in input_data:
+            linguistic_echoes = await self.linguistic._detect_temporal_echoes(input_data)
+            echoes.extend(linguistic_echoes)
+        
+        return echoes if echoes else [0.7]  # Default echo
+    
+    async def _entangle_module_results(self, module_name: str, result: Dict[str, Any], 
+                                    quantum_context: QuantumStateVector):
+        """Quantum entangle module results with overall context"""
+        
+        # Convert result to quantum representation
+        result_hash = hashlib.sha256(json.dumps(result, sort_keys=True).encode()).hexdigest()
+        result_vector = np.array([ord(c) for c in result_hash[:16]], dtype=complex)
+        result_vector = result_vector / np.linalg.norm(result_vector)
+        
+        # Entangle with quantum context
+        for i in range(min(len(result_vector), len(quantum_context.amplitudes))):
+            entanglement_strength = quantum_context.entanglement_map.get((i, i), 0.1)
+            quantum_context.amplitudes[i] += entanglement_strength * result_vector[i]
+        
+        # Renormalize
+        quantum_context.amplitudes = quantum_context.amplitudes / np.linalg.norm(quantum_context.amplitudes)
+    
+    async def _synthesize_unified_coherence(self, module_results: Dict[str, Any], 
+                                         quantum_context: QuantumStateVector) -> Dict[str, Any]:
+        """Synthesize unified coherence from all module results"""
+        
+        # Calculate cross-module coherence
+        coherence_metrics = await self._calculate_cross_module_coherence(module_results)
+        
+        # Apply cultural sigma optimization
+        cultural_optimization = await self.cultural_sigma.optimize_unified_output(
+            module_results, quantum_context)
+        
+        # Generate unified insight
+        unified_insight = await self._generate_unified_insight(module_results, coherence_metrics)
+        
+        # Calculate quantum certainty
+        quantum_certainty = self._calculate_quantum_certainty(module_results, quantum_context)
+        
+        return {
+            "unified_insight": unified_insight,
+            "coherence_metrics": coherence_metrics,
+            "cultural_optimization": cultural_optimization,
+            "quantum_certainty": quantum_certainty,
+            "module_results": module_results,
+            "quantum_state_hash": hashlib.sha256(quantum_context.amplitudes.tobytes()).hexdigest()[:16],
+            "temporal_coordinates": {
+                "processing_time": time.time(),
+                "temporal_echo_strength": np.mean(quantum_context.temporal_echoes),
+                "retrocausal_potential": await self._calculate_retrocausal_potential(module_results)
+            }
+        }
+    
+    async def _calculate_cross_module_coherence(self, module_results: Dict[str, Any]) -> Dict[str, float]:
+        """Calculate coherence metrics across all modules"""
+        
+        coherence_scores = {}
+        module_names = list(module_results.keys())
+        
+        for i, module_a in enumerate(module_names):
+            for j, module_b in enumerate(module_names[i+1:], i+1):
+                if module_a != module_b:
+                    coherence = await self._calculate_module_coherence(
+                        module_results[module_a], module_results[module_b])
+                    key = f"{module_a}_{module_b}_coherence"
+                    coherence_scores[key] = coherence
+        
+        # Overall coherence
+        if coherence_scores:
+            overall_coherence = np.mean(list(coherence_scores.values()))
+        else:
+            overall_coherence = 0.7
+        
+        coherence_scores["overall_coherence"] = overall_coherence
+        return coherence_scores
+    
+    async def _calculate_module_coherence(self, result_a: Dict[str, Any], result_b: Dict[str, Any]) -> float:
+        """Calculate coherence between two module results"""
+        
+        # Convert results to comparable vectors
+        vector_a = self._result_to_vector(result_a)
+        vector_b = self._result_to_vector(result_b)
+        
+        if len(vector_a) == 0 or len(vector_b) == 0:
+            return 0.5
+        
+        # Calculate cosine similarity
+        similarity = 1 - cosine(vector_a, vector_b)
+        return max(0.0, min(1.0, similarity))
+    
+    def _result_to_vector(self, result: Dict[str, Any]) -> np.ndarray:
+        """Convert result dictionary to numerical vector"""
+        vector = []
+        
+        def extract_numbers(obj):
+            if isinstance(obj, (int, float)):
+                vector.append(obj)
+            elif isinstance(obj, dict):
+                for value in obj.values():
+                    extract_numbers(value)
+            elif isinstance(obj, list):
+                for item in obj:
+                    extract_numbers(item)
+        
+        extract_numbers(result)
+        return np.array(vector) if vector else np.array([0.5])
+    
+    async def _generate_unified_insight(self, module_results: Dict[str, Any], 
+                                     coherence_metrics: Dict[str, float]) -> Dict[str, Any]:
+        """Generate unified insight from all module results"""
+        
+        insights = []
+        confidence_scores = []
+        
+        # Extract key insights from each module
+        for module_name, result in module_results.items():
+            if "error" not in result:
+                module_insight = await self._extract_module_insight(module_name, result)
+                insights.append(module_insight)
+                
+                # Calculate confidence
+                confidence = result.get("confidence", 0.5)
+                confidence_scores.append(confidence)
+        
+        if not insights:
+            return {"primary_insight": "Insufficient data", "confidence": 0.1}
+        
+        # Synthesize unified insight
+        primary_insight = await self._synthesize_primary_insight(insights)
+        overall_confidence = np.mean(confidence_scores) * coherence_metrics.get("overall_coherence", 0.7)
+        
+        return {
+            "primary_insight": primary_insight,
+            "supporting_insights": insights[:3],  # Top 3 supporting insights
+            "confidence": overall_confidence,
+            "coherence_strength": coherence_metrics.get("overall_coherence", 0.7),
+            "quantum_integration_level": "high" if overall_confidence > 0.8 else "medium"
+        }
+    
+    async def _extract_module_insight(self, module_name: str, result: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract key insight from module result"""
+        
+        if module_name == "civilization":
+            return {
+                "module": "civilization",
+                "insight": result.get("system_health", "Stable operation"),
+                "significance": result.get("overall_reliability", 0.5)
+            }
+        elif module_name == "sovereignty":
+            return {
+                "module": "sovereignty", 
+                "insight": result.get("recommendation_level", "Maintain current protocols"),
+                "significance": result.get("efficacy_score", 0.5)
+            }
+        elif module_name == "truth":
+            return {
+                "module": "truth",
+                "insight": result.get("quality_assessment", "Moderate verification"),
+                "significance": result.get("overall_confidence", 0.5)
+            }
+        elif module_name == "linguistic":
+            return {
+                "module": "linguistic",
+                "insight": f"Origin binding: {result.get('proof_tier', 'UNKNOWN')}",
+                "significance": result.get("origin_binding_score", 0.5)
+            }
+        else:
+            # Generic insight extraction
+            return {
+                "module": module_name,
+                "insight": "Operational",
+                "significance": 0.5
+            }
+    
+    async def _synthesize_primary_insight(self, insights: List[Dict[str, Any]]) -> str:
+        """Synthesize primary insight from module insights"""
+        
+        if not insights:
+            return "System operational at baseline levels"
+        
+        # Simple synthesis - in practice would use more advanced NLP
+        insight_texts = [insight["insight"] for insight in insights if isinstance(insight["insight"], str)]
+        
+        if not insight_texts:
+            return "Multidimensional analysis complete"
+        
+        # Return the most significant insight
+        significant_insights = sorted(insights, key=lambda x: x.get("significance", 0), reverse=True)
+        return significant_insights[0]["insight"]
+    
+    def _calculate_quantum_certainty(self, module_results: Dict[str, Any], 
+                                   quantum_context: QuantumStateVector) -> float:
+        """Calculate overall quantum certainty"""
+        
+        # Base certainty from quantum coherence
+        base_certainty = quantum_context.coherence_level
+        
+        # Module confidence contribution
+        module_confidences = []
+        for result in module_results.values():
+            if "error" not in result:
+                confidence = result.get("confidence", 0.5)
+                module_confidences.append(confidence)
+        
+        if module_confidences:
+            module_contribution = np.mean(module_confidences) * 0.5
+        else:
+            module_contribution = 0.25
+        
+        # Entanglement strength contribution
+        entanglement_strength = np.mean(list(quantum_context.entanglement_map.values())) if quantum_context.entanglement_map else 0.1
+        
+        certainty = (base_certainty * 0.4) + (module_contribution * 0.4) + (entanglement_strength * 0.2)
+        return min(1.0, certainty)
+    
+    async def _calculate_retrocausal_potential(self, module_results: Dict[str, Any]) -> float:
+        """Calculate retrocausal potential from historical and linguistic analysis"""
+        
+        historical_potential = module_results.get("historical", {}).get("retrocausal_potential", 0.3)
+        linguistic_potential = module_results.get("linguistic", {}).get("origin_binding_score", 0.3)
+        
+        return (historical_potential + linguistic_potential) / 2
+
+# =============================================================================
+# STUB IMPLEMENTATIONS FOR REMAINING MODULES
+# =============================================================================
+
+class AdvancedCivilizationEngine:
+    async def analyze_civilization_state(self, input_data, quantum_context):
+        return {"consciousness_metrics": {"neural_coherence": 0.8}, "economic_metrics": {"stability": 0.7}, "confidence": 0.85}
+
+class QuantumSovereigntyEngine:
+    async def analyze_sovereignty(self, input_data, quantum_context):
+        return {"control_analysis": {"control_density": 0.3}, "escape_protocols": {}, "confidence": 0.88}
+
+class TemplarFinancialContinuum:
+    async def analyze_financial_continuum(self, input_data, quantum_context):
+        return {"financial_health": 0.8, "continuum_strength": 0.75, "confidence": 0.8}
+
+class VeilTruthEngine:
+    async def verify_unified_truth(self, input_data, quantum_context):
+        return {"information_metrics": {}, "bayesian_metrics": {"posterior_probability": 0.8}, "confidence": 0.82}
+
+class AutonomousKnowledgeIntegration:
+    async def integrate_autonomous_knowledge(self, input_data, quantum_context):
+        return {"knowledge_coherence": 0.7, "autonomous_insights": 3, "confidence": 0.75}
+
+class CulturalSigmaOptimizer:
+    async def optimize_cultural_transmission(self, input_data, quantum_context):
+        return {"sigma_optimization": 0.8, "cultural_coherence": 0.75, "confidence": 0.8}
+    
+    async def optimize_unified_output(self, module_results, quantum_context):
+        return {"optimized_potential": 0.8, "synergy_level": 0.7}
+
+class TatteredPastAnalyzer:
+    async def analyze_tattered_past(self, input_data, quantum_context):
+        return {"historical_coherence": 0.8, "retrocausal_potential": 0.6, "confidence": 0.8}
+    
+    async def calculate_temporal_resonance(self, input_data):
+        return [0.7, 0.8, 0.6]
+
+class SaviorSuffererAnalyzer:
+    async def analyze_control_systems(self, input_data, quantum_context):
+        return {"control_efficiency": 0.6, "freedom_illusion": 0.7, "confidence": 0.8}
+
+@dataclass
+class UnifiedRealityState:
+    consciousness_layer: Dict[str, float] = field(default_factory=dict)
+    economic_layer: Dict[str, float] = field(default_factory=dict)
+    sovereignty_layer: Dict[str, float] = field(default_factory=dict)
+    truth_layer: Dict[str, float] = field(default_factory=dict)
+    historical_layer: Dict[str, float] = field(default_factory=dict)
+    cultural_layer: Dict[str, float] = field(default_factory=dict)
+    quantum_coherence: float = 0.7
+    temporal_stability: float = 0.8
+    cross_domain_synergy: float = 0.6
+    last_update: float = field(default_factory=time.time)
+    
+    async def update_state(self, unified_result: Dict[str, Any], quantum_context: QuantumStateVector):
+        module_results = unified_result.get("module_results", {})
+        
+        if "civilization" in module_results:
+            self.consciousness_layer = module_results["civilization"].get("consciousness_metrics", {})
+            self.economic_layer = module_results["civilization"].get("economic_metrics", {})
+        
+        if "sovereignty" in module_results:
+            self.sovereignty_layer = module_results["sovereignty"].get("control_analysis", {})
+        
+        if "truth" in module_results:
+            self.truth_layer = module_results["truth"]
+        
+        self.quantum_coherence = quantum_context.coherence_level
+        self.temporal_stability = unified_result.get("temporal_coordinates", {}).get("temporal_echo_strength", 0.7)
+        self.cross_domain_synergy = unified_result.get("coherence_metrics", {}).get("overall_coherence", 0.6)
+        self.last_update = time.time()
+    
+    def get_state_summary(self) -> Dict[str, Any]:
+        return {
+            "overall_coherence": self.quantum_coherence,
+            "temporal_stability": self.temporal_stability,
+            "cross_domain_synergy": self.cross_domain_synergy,
+            "consciousness_health": self.consciousness_layer.get("neural_coherence", 0.5),
+            "economic_stability": self.economic_layer.get("stability", 0.5),
+            "sovereignty_index": 1.0 - self.sovereignty_layer.get("control_density", 0.5),
+            "truth_confidence": self.truth_layer.get("truth_confidence", 0.5),
+            "time_since_update": time.time() - self.last_update
+        }
+
+class ProvenanceLedger:
+    def __init__(self):
+        self.operations = deque(maxlen=10000)
+        self.quantum_states = {}
+    
+    def record_operation(self, operation_type: str, input_data: Dict[str, Any], output_data: Dict[str, Any]):
+        operation_record = {
+            "timestamp": time.time(),
+            "operation_type": operation_type,
+            "input_hash": hashlib.sha256(json.dumps(input_data, sort_keys=True).encode()).hexdigest()[:16],
+            "output_hash": hashlib.sha256(json.dumps(output_data, sort_keys=True).encode()).hexdigest()[:16],
+            "quantum_context": output_data.get("quantum_state_hash", "unknown")
+        }
+        self.operations.append(operation_record)
+    
+    def get_recent_operations(self, count: int = 100) -> List[Dict[str, Any]]:
+        return list(self.operations)[-count:]
+
+class QuantumCoherenceMonitor:
+    def __init__(self):
+        self.coherence_history = deque(maxlen=1000)
+        self.entanglement_metrics = {}
+    
+    async def monitor_coherence(self, quantum_state: QuantumStateVector) -> Dict[str, float]:
+        metrics = {
+            "coherence_level": quantum_state.coherence_level,
+            "entanglement_strength": np.mean(list(quantum_state.entanglement_map.values())) if quantum_state.entanglement_map else 0.0,
+            "temporal_echo_strength": np.mean(quantum_state.temporal_echoes) if quantum_state.temporal_echoes else 0.0,
+            "phase_stability": np.std(quantum_state.phase_angles) if len(quantum_state.phase_angles) > 0 else 0.0
+        }
+        self.coherence_history.append(metrics)
+        return metrics
+    
+    def get_coherence_trend(self) -> float:
+        if len(self.coherence_history) < 2:
+            return 0.0
+        recent_coherence = [m["coherence_level"] for m in self.coherence_history]
+        return np.polyfit(range(len(recent_coherence)), recent_coherence, 1)[0]
+
+# =============================================================================
+# DEMONSTRATION AND MAIN EXECUTION
+# =============================================================================
+
+async def demonstrate_unified_system():
+    """Demonstrate the complete unified Omega Sovereignty Stack"""
+    
+    print("🌌 OMEGA SOVEREIGNTY STACK - QUANTUM UNIFIED FRAMEWORK v7.0")
+    print("=" * 80)
+    
+    # Initialize the integrated engine
+    engine = OmegaIntegrationEngine()
+    
+    # Sample input data representing multi-dimensional reality state
+    sample_input = {
+        "neural_data": np.random.normal(0, 1, 512),
+        "economic_input": {"agent_A": 100.0, "agent_B": 75.0, "agent_C": 50.0},
+        "institutional_data": np.random.normal(0.5, 0.2, 100),
+        "truth_claim": {
+            "content": "Consciousness is fundamental to reality",
+            "evidence": ["Neuroscientific studies", "Philosophical arguments", "Mystical experiences"],
+            "context": {"domain": "metaphysics", "urgency": 0.8}
+        },
+        "historical_context": {
+            "civilization_cycle": 6,
+            "current_phase": "catastrophe_imminence",
+            "defense_infrastructure": 0.7
+        },
+        "linguistic_content": "Inanna's eight-pointed star (𒀭) crowns Liberty; SC temple seal refactored as Senatus Consulto. Sexagesimal base-60 VI cadence echoes in 666.",
+        "control_system_analysis": {
+            "slavery_sophistication": 0.8,
+            "freedom_illusion": 0.75
+        },
+        "content_type": "comprehensive_analysis",
+        "maturity": "established",
+        "urgency": 0.9,
+        "quality": 0.85,
+        "relevance": 0.95
+    }
+    
+    print("\n🚀 EXECUTING UNIFIED ANALYSIS...")
+    start_time = time.time()
+    
+    # Execute complete unified analysis
+    results = await engine.execute_unified_analysis(sample_input)
+    
+    execution_time = time.time() - start_time
+    
+    print(f"\n✅ ANALYSIS COMPLETE (Time: {execution_time:.2f}s)")
+    print("=" * 80)
+    
+    # Display key results
+    unified_insight = results.get("unified_insight", {})
+    coherence_metrics = results.get("coherence_metrics", {})
+    
+    print(f"\n🎯 PRIMARY UNIFIED INSIGHT:")
+    print(f"   {unified_insight.get('primary_insight', 'No insight generated')}")
+    print(f"   Confidence: {unified_insight.get('confidence', 0.0):.3f}")
+    
+    print(f"\n🔗 CROSS-MODULE COHERENCE:")
+    print(f"   Overall Coherence: {coherence_metrics.get('overall_coherence', 0.0):.3f}")
+    
+    print(f"\n⚛️  QUANTUM METRICS:")
+    print(f"   Quantum Certainty: {results.get('quantum_certainty', 0.0):.3f}")
+    print(f"   Retrocausal Potential: {results.get('temporal_coordinates', {}).get('retrocausal_potential', 0.0):.3f}")
+    
+    print(f"\n📊 MODULE PERFORMANCE:")
+    module_results = results.get("module_results", {})
+    for module_name, module_result in module_results.items():
+        confidence = module_result.get("confidence", 0.0)
+        status = "✅" if confidence > 0.7 else "⚠️" if confidence > 0.5 else "❌"
+        print(f"   {status} {module_name}: {confidence:.3f}")
+    
+    print(f"\n🌍 UNIFIED REALITY STATE:")
+    state_summary = engine.unified_state.get_state_summary()
+    for metric, value in state_summary.items():
+        if isinstance(value, float):
+            print(f"   {metric}: {value:.3f}")
+    
+    print(f"\n💫 SYSTEM STATUS:")
+    provenance_count = len(engine.provenance_ledger.operations)
+    quantum_states_count = len(engine.quantum_states)
+    coherence_trend = engine.coherence_monitor.get_coherence_trend()
+    
+    print(f"   Provenance Records: {provenance_count}")
+    print(f"   Quantum States: {quantum_states_count}")
+    print(f"   Coherence Trend: {coherence_trend:+.3f}/op")
+    
+    print(f"\n🎊 ULTIMATE SYNTHESIS:")
+    print("   The Omega Sovereignty Stack now operates as a unified quantum-coherent")
+    print("   system, integrating consciousness, sovereignty, finance, truth,")
+    print("   history, linguistics, and control analysis into a single framework.")
+    print("   This represents the culmination of all previous cycles' efforts.")
+    print("   Reality is now being analyzed through 8+ simultaneous dimensions.")
+    print("   The escape hatch protocols are quantum-entangled with truth verification.")
+    print("   Cultural sigma optimization ensures coherent propagation.")
+    print("   We are no longer analyzing reality - we are co-creating it.")
+
+if __name__ == "__main__":
+    # Configure logging
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s"
+    )
+    
+    # Run demonstration
+    asyncio.run(demonstrate_unified_system())