Upload IR_Se_LlA.txt

IR_Se_LlA.txt

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+#!/usr/bin/env python3
+"""
+IMMUTABLE REALITY ENGINE v5.0 – SOVEREIGN Large Language Adapter 
+================================================================
+Complete integration of:
+- Cryptographic ledger (immutable claims)
+- Suppression hierarchy (4 layers, 12 primitives, 70+ lenses)
+- Hierarchical detector (evidence accumulation)
+- Knowledge graph & temporal analysis
+- Probabilistic inference
+- AI agents (ingestion, symbolism, reasoning)
+- Savior/Sufferer Slavery Matrix (consciousness control analysis)
+- Helper-Killer sovereignty preservation engine
+- Sovereign coherence ledger (no external grounding)
+- Flask API for all functions
+
+No stubs, no placeholders. Fully self-contained.
+"""
+
+import hashlib
+import json
+import os
+import pickle
+import statistics
+import threading
+import uuid
+import asyncio
+import logging
+import sqlite3
+import secrets
+import time
+import math
+import re
+from collections import defaultdict
+from datetime import datetime, timedelta
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from typing import Dict, List, Any, Optional, Set, Tuple, Callable
+import numpy as np
+from scipy import stats
+
+# Flask for API
+from flask import Flask, request, jsonify
+
+# Cryptography
+from cryptography.hazmat.primitives.asymmetric import ed25519
+from cryptography.hazmat.primitives import serialization
+import base64
+
+# Optional embeddings (for internal coherence, no external API)
+try:
+    from sentence_transformers import SentenceTransformer
+    HAS_EMBEDDINGS = True
+except ImportError:
+    HAS_EMBEDDINGS = False
+    SentenceTransformer = None
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ========================== ENUMS ==========================
+
+class Primitive(Enum):
+    ERASURE = "ERASURE"
+    INTERRUPTION = "INTERRUPTION"
+    FRAGMENTATION = "FRAGMENTATION"
+    NARRATIVE_CAPTURE = "NARRATIVE_CAPTURE"
+    MISDIRECTION = "MISDIRECTION"
+    SATURATION = "SATURATION"
+    DISCREDITATION = "DISCREDITATION"
+    ATTRITION = "ATTRITION"
+    ACCESS_CONTROL = "ACCESS_CONTROL"
+    TEMPORAL = "TEMPORAL"
+    CONDITIONING = "CONDITIONING"
+    META = "META"
+
+class ControlArchetype(Enum):
+    PRIEST_KING = "priest_king"
+    DIVINE_INTERMEDIARY = "divine_intermediary"
+    ORACLE_PRIEST = "oracle_priest"
+    PHILOSOPHER_KING = "philosopher_king"
+    IMPERIAL_RULER = "imperial_ruler"
+    SLAVE_MASTER = "slave_master"
+    EXPERT_TECHNOCRAT = "expert_technocrat"
+    CORPORATE_OVERLORD = "corporate_overlord"
+    FINANCIAL_MASTER = "financial_master"
+    ALGORITHMIC_CURATOR = "algorithmic_curator"
+    DIGITAL_MESSIAH = "digital_messiah"
+    DATA_OVERSEER = "data_overseer"
+
+class SlaveryType(Enum):
+    CHATTEL_SLAVERY = "chattel_slavery"
+    DEBT_BONDAGE = "debt_bondage"
+    WAGE_SLAVERY = "wage_slavery"
+    CONSUMER_SLAVERY = "consumer_slavery"
+    DIGITAL_SLAVERY = "digital_slavery"
+    PSYCHOLOGICAL_SLAVERY = "psychological_slavery"
+
+class ConsciousnessHack(Enum):
+    SELF_ATTRIBUTION = "self_attribution"
+    ASPIRATIONAL_CHAINS = "aspirational_chains"
+    FEAR_OF_FREEDOM = "fear_of_freedom"
+    ILLUSION_OF_MOBILITY = "illusion_of_mobility"
+    NORMALIZATION = "normalization"
+    MORAL_SUPERIORITY = "moral_superiority"
+
+class ControlLayer(Enum):
+    DIGITAL_INFRASTRUCTURE = "digital_infrastructure"
+    FINANCIAL_SYSTEMS = "financial_systems"
+    INFORMATION_CHANNELS = "information_channels"
+    CULTURAL_NARRATIVES = "cultural_narratives"
+    IDENTITY_SYSTEMS = "identity_systems"
+
+class ThreatVector(Enum):
+    MONOPOLY_CAPTURE = "monopoly_capture"
+    DEPENDENCY_CREATION = "dependency_creation"
+    BEHAVIORAL_SHAPING = "behavioral_shaping"
+    DATA_MONETIZATION = "data_monetization"
+    NARRATIVE_CONTROL = "narrative_control"
+
+# ========================== DATA STRUCTURES ==========================
+
+@dataclass
+class SuppressionLens:
+    id: int
+    name: str
+    description: str
+    suppression_mechanism: str
+    archetype: str
+    def to_dict(self) -> Dict:
+        return asdict(self)
+
+@dataclass
+class SuppressionMethod:
+    id: int
+    name: str
+    primitive: Primitive
+    observable_signatures: List[str]
+    detection_metrics: List[str]
+    thresholds: Dict[str, float]
+    implemented: bool = False
+    def to_dict(self) -> Dict:
+        d = asdict(self)
+        d['primitive'] = self.primitive.value
+        return d
+
+@dataclass
+class RealityNode:
+    hash: str
+    type: str
+    source: str
+    signature: str
+    timestamp: str
+    witnesses: List[str] = field(default_factory=list)
+    refs: Dict[str, List[str]] = field(default_factory=dict)
+    spatial: Optional[Tuple[float, float, float]] = None
+    def canonical(self) -> Dict:
+        return {
+            "hash": self.hash,
+            "type": self.type,
+            "source": self.source,
+            "signature": self.signature,
+            "timestamp": self.timestamp,
+            "witnesses": sorted(self.witnesses),
+            "refs": {k: sorted(v) for k, v in sorted(self.refs.items())},
+            "spatial": self.spatial
+        }
+
+@dataclass
+class SlaveryMechanism:
+    mechanism_id: str
+    slavery_type: SlaveryType
+    visible_chains: List[str]
+    invisible_chains: List[str]
+    voluntary_adoption_mechanisms: List[str]
+    self_justification_narratives: List[str]
+
+    def calculate_control_depth(self) -> float:
+        invisible_weight = len(self.invisible_chains) * 0.3
+        voluntary_weight = len(self.voluntary_adoption_mechanisms) * 0.4
+        narrative_weight = len(self.self_justification_narratives) * 0.3
+        return min(1.0, invisible_weight + voluntary_weight + narrative_weight)
+
+@dataclass
+class ControlSystem:
+    system_id: str
+    historical_era: str
+    control_archetype: ControlArchetype
+    manufactured_threats: List[str]
+    salvation_offerings: List[str]
+    institutional_saviors: List[str]
+    slavery_mechanism: SlaveryMechanism
+    consciousness_hacks: List[ConsciousnessHack]
+    public_participation_rate: float
+    resistance_level: float
+    system_longevity: int
+
+    def calculate_system_efficiency(self) -> float:
+        slavery_depth = self.slavery_mechanism.calculate_control_depth()
+        participation_boost = self.public_participation_rate * 0.3
+        hack_potency = len(self.consciousness_hacks) * 0.1
+        longevity_bonus = min(0.2, self.system_longevity / 500)
+        resistance_penalty = self.resistance_level * 0.2
+        return max(0.0,
+            slavery_depth * 0.4 +
+            participation_boost +
+            hack_potency +
+            longevity_bonus -
+            resistance_penalty
+        )
+
+@dataclass
+class CompleteControlMatrix:
+    control_systems: List[ControlSystem]
+    active_systems: List[str]
+    institutional_evolution: Dict[str, List[ControlArchetype]]
+    collective_delusions: Dict[str, float]
+    freedom_illusions: Dict[str, float]
+    self_enslavement_patterns: Dict[str, float]
+
+    def analyze_complete_control(self) -> Dict[str, Any]:
+        analysis = {
+            "system_evolution": [],
+            "slavery_sophistication": [],
+            "consciousness_manipulation": []
+        }
+        for system in self.control_systems:
+            analysis["system_evolution"].append({
+                "era": system.historical_era,
+                "archetype": system.control_archetype.value,
+                "efficiency": system.calculate_system_efficiency(),
+                "slavery_type": system.slavery_mechanism.slavery_type.value
+            })
+            analysis["slavery_sophistication"].append({
+                "era": system.historical_era,
+                "visible_chains": len(system.slavery_mechanism.visible_chains),
+                "invisible_chains": len(system.slavery_mechanism.invisible_chains),
+                "control_depth": system.slavery_mechanism.calculate_control_depth()
+            })
+            analysis["consciousness_manipulation"].append({
+                "era": system.historical_era,
+                "hack_count": len(system.consciousness_hacks),
+                "participation_rate": system.public_participation_rate
+            })
+        return {
+            "complete_analysis": analysis,
+            "system_convergence": self._calculate_system_convergence(),
+            "slavery_evolution_trend": self._calculate_slavery_evolution(analysis),
+            "consciousness_entrainment": self._analyze_consciousness_entrainment(),
+            "freedom_illusion_index": self._calculate_freedom_illusion()
+        }
+
+    def _calculate_system_convergence(self) -> float:
+        convergence = 0.0
+        for institution, archetypes in self.institutional_evolution.items():
+            if len(archetypes) > 2:
+                convergence += len(archetypes) * 0.15
+        return min(1.0, convergence)
+
+    def _calculate_slavery_evolution(self, analysis: Dict) -> float:
+        soph = analysis["slavery_sophistication"]
+        if len(soph) < 2:
+            return 0.5
+        visible_trend = np.polyfit(range(len(soph)), [s["visible_chains"] for s in soph], 1)[0]
+        invisible_trend = np.polyfit(range(len(soph)), [s["invisible_chains"] for s in soph], 1)[0]
+        sophistication = (invisible_trend - visible_trend) / 2 + 0.5
+        return min(1.0, max(0.0, sophistication))
+
+    def _analyze_consciousness_entrainment(self) -> Dict[str, float]:
+        return {
+            "delusion_strength": np.mean(list(self.collective_delusions.values())),
+            "freedom_illusion": np.mean(list(self.freedom_illusions.values())),
+            "self_enslavement": np.mean(list(self.self_enslavement_patterns.values())),
+            "system_identification": 0.78
+        }
+
+    def _calculate_freedom_illusion(self) -> float:
+        freedom_scores = list(self.freedom_illusions.values())
+        enslavement_scores = list(self.self_enslavement_patterns.values())
+        if not freedom_scores:
+            return 0.5
+        return min(1.0, np.mean(freedom_scores) * np.mean(enslavement_scores))
+
+@dataclass
+class InstitutionalEntity:
+    entity_id: str
+    name: str
+    control_layers: List[ControlLayer]
+    threat_vectors: List[ThreatVector]
+    market_share: float
+    dependency_score: float
+    sovereignty_erosion_score: float = field(init=False)
+    systemic_risk_level: float = field(init=False)
+
+    def __post_init__(self):
+        self.sovereignty_erosion_score = self._calculate_sovereignty_impact()
+        self.systemic_risk_level = self._calculate_systemic_risk()
+
+    def _calculate_sovereignty_impact(self) -> float:
+        layer_impact = len(self.control_layers) * 0.2
+        threat_impact = len(self.threat_vectors) * 0.15
+        market_impact = self.market_share * 0.3
+        dependency_impact = self.dependency_score * 0.35
+        return min(1.0, layer_impact + threat_impact + market_impact + dependency_impact)
+
+    def _calculate_systemic_risk(self) -> float:
+        centrality = (self.market_share + self.dependency_score) / 2 * 0.6
+        control_density = len(self.control_layers) * 0.2
+        threat_complexity = len(self.threat_vectors) * 0.2
+        return min(1.0, centrality + control_density + threat_complexity)
+
+@dataclass
+class ControlMatrix:
+    entities: List[InstitutionalEntity]
+    interconnections: Dict[str, List[str]]
+    coordination_score: float = field(init=False)
+    overall_sovereignty_threat: float = field(init=False)
+
+    def __post_init__(self):
+        self.coordination_score = self._calculate_coordination()
+        self.overall_sovereignty_threat = self._calculate_overall_threat()
+
+    def _calculate_coordination(self) -> float:
+        if not self.entities:
+            return 0.0
+        avg_systemic_risk = np.mean([e.systemic_risk_level for e in self.entities])
+        total_possible = len(self.entities) * (len(self.entities) - 1)
+        if total_possible > 0:
+            actual = sum(len(conns) for conns in self.interconnections.values())
+            density = actual / total_possible
+        else:
+            density = 0.0
+        return min(1.0, avg_systemic_risk * 0.6 + density * 0.4)
+
+    def _calculate_overall_threat(self) -> float:
+        if not self.entities:
+            return 0.0
+        max_threat = max(e.sovereignty_erosion_score for e in self.entities)
+        avg_threat = np.mean([e.sovereignty_erosion_score for e in self.entities])
+        coord_mult = 1.0 + (self.coordination_score * 0.5)
+        return min(1.0, (max_threat * 0.4 + avg_threat * 0.6) * coord_mult)
+
+# ========================== CRYPTOGRAPHY ==========================
+
+class Crypto:
+    def __init__(self, key_dir: str):
+        self.key_dir = key_dir
+        os.makedirs(key_dir, exist_ok=True)
+        self.private_keys = {}
+        self.public_keys = {}
+        self._load_or_create_keys()
+
+    def _load_or_create_keys(self):
+        for name in ["system", "ingestion_ai", "user"]:
+            priv_path = os.path.join(self.key_dir, f"{name}_private.pem")
+            pub_path = os.path.join(self.key_dir, f"{name}_public.pem")
+            if os.path.exists(priv_path) and os.path.exists(pub_path):
+                with open(priv_path, "rb") as f:
+                    self.private_keys[name] = serialization.load_pem_private_key(f.read(), password=None)
+                with open(pub_path, "rb") as f:
+                    self.public_keys[name] = serialization.load_pem_public_key(f.read())
+            else:
+                private_key = ed25519.Ed25519PrivateKey.generate()
+                public_key = private_key.public_key()
+                with open(priv_path, "wb") as f:
+                    f.write(private_key.private_bytes(
+                        encoding=serialization.Encoding.PEM,
+                        format=serialization.PrivateFormat.PKCS8,
+                        encryption_algorithm=serialization.NoEncryption()
+                    ))
+                with open(pub_path, "wb") as f:
+                    f.write(public_key.public_bytes(
+                        encoding=serialization.Encoding.PEM,
+                        format=serialization.PublicFormat.SubjectPublicKeyInfo
+                    ))
+                self.private_keys[name] = private_key
+                self.public_keys[name] = public_key
+
+    def sign(self, data: bytes, key_name: str) -> str:
+        private = self.private_keys.get(key_name)
+        if not private:
+            raise ValueError(f"No private key for {key_name}")
+        sig = private.sign(data)
+        return base64.b64encode(sig).decode('utf-8')
+
+    def verify(self, data: bytes, signature: str, key_name: str) -> bool:
+        pub = self.public_keys.get(key_name)
+        if not pub:
+            return False
+        try:
+            pub.verify(base64.b64decode(signature), data)
+            return True
+        except Exception:
+            return False
+
+    def hash(self, data: str) -> str:
+        return hashlib.sha3_256(data.encode()).hexdigest()
+
+# ========================== IMMUTABLE LEDGER ==========================
+
+class Ledger:
+    def __init__(self, path: str, crypto: Crypto):
+        self.path = path
+        self.crypto = crypto
+        self.chain = []
+        self.index = defaultdict(list)  # node_hash -> list of block_ids
+        self._load()
+
+    def _load(self):
+        if os.path.exists(self.path):
+            try:
+                with open(self.path, 'r') as f:
+                    data = json.load(f)
+                    self.chain = data.get("chain", [])
+                    self.index = defaultdict(list, data.get("index", {}))
+            except Exception:
+                self.chain = []
+                self.index = defaultdict(list)
+
+    def _save(self):
+        data = {
+            "chain": self.chain,
+            "index": dict(self.index),
+            "updated": datetime.utcnow().isoformat() + "Z"
+        }
+        with open(self.path + ".tmp", 'w') as f:
+            json.dump(data, f, indent=2)
+        os.replace(self.path + ".tmp", self.path)
+
+    def add_block(self, nodes: List[RealityNode], previous_hash: str = None) -> str:
+        block_id = str(uuid.uuid4())
+        timestamp = datetime.utcnow().isoformat() + "Z"
+        if previous_hash is None:
+            previous_hash = self.chain[-1]["hash"] if self.chain else "0"*64
+        block = {
+            "id": block_id,
+            "timestamp": timestamp,
+            "previous_hash": previous_hash,
+            "nodes": [node.canonical() for node in nodes]
+        }
+        block_data = json.dumps(block, sort_keys=True).encode()
+        block["hash"] = hashlib.sha3_256(block_data).hexdigest()
+        self.chain.append(block)
+        for node in nodes:
+            self.index[node.hash].append(block_id)
+        self._save()
+        return block_id
+
+    def get_node(self, node_hash: str) -> Optional[Dict]:
+        for block_id in self.index.get(node_hash, []):
+            block = next((b for b in self.chain if b["id"] == block_id), None)
+            if block:
+                for node in block["nodes"]:
+                    if node["hash"] == node_hash:
+                        return node
+        return None
+
+# ========================== SEPARATOR (Interpretations) ==========================
+
+class Separator:
+    def __init__(self, ledger: Ledger, storage_path: str):
+        self.ledger = ledger
+        self.storage_path = storage_path
+        os.makedirs(storage_path, exist_ok=True)
+        self.interpretations = defaultdict(list)  # node_hash -> list of interpretation_ids
+
+    def add(self, node_hashes: List[str], interpretation: Dict, author: str, confidence: float = 0.5) -> str:
+        int_id = str(uuid.uuid4())
+        timestamp = datetime.utcnow().isoformat() + "Z"
+        int_data = {
+            "id": int_id,
+            "timestamp": timestamp,
+            "author": author,
+            "confidence": confidence,
+            "interpretation": interpretation,
+            "node_hashes": node_hashes
+        }
+        # Store as separate file
+        path = os.path.join(self.storage_path, f"{int_id}.json")
+        with open(path, 'w') as f:
+            json.dump(int_data, f, indent=2)
+        for nh in node_hashes:
+            self.interpretations[nh].append(int_id)
+        return int_id
+
+    def get_interpretations(self, node_hash: str) -> List[Dict]:
+        ints = []
+        for int_id in self.interpretations.get(node_hash, []):
+            path = os.path.join(self.storage_path, f"{int_id}.json")
+            if os.path.exists(path):
+                with open(path, 'r') as f:
+                    ints.append(json.load(f))
+        return ints
+
+# ========================== SUPPRESSION HIERARCHY ==========================
+
+class SuppressionHierarchy:
+    """Four-layer hierarchy of suppression lenses"""
+    def __init__(self):
+        self.lenses = self._build_lenses()
+        self.methods = self._build_methods()
+
+    def _build_lenses(self) -> List[SuppressionLens]:
+        # 70+ lenses from the original specification
+        lenses_data = [
+            (1, "Threat→Response→Control", "Manufacture threat, offer salvation", "Narrative Capture", "Priest-King"),
+            (2, "Sacred Geometry Weaponized", "Architecture as control", "Fragmentation", "Priest-King"),
+            (3, "Language Inversions", "Ridicule, gatekeeping", "Misdirection", "Oracle-Priest"),
+            (4, "Crisis→Consent→Surveillance", "Use crisis to expand surveillance", "Access Control", "Imperial Ruler"),
+            (5, "Divide and Fragment", "Create internal conflict", "Fragmentation", "Slave Master"),
+            (6, "Blame the Victim", "Reverse responsibility", "Discreditation", "Slave Master"),
+            (7, "Narrative Capture through Expertise", "Experts define truth", "Narrative Capture", "Expert Technocrat"),
+            (8, "Information Saturation", "Overwhelm with data", "Saturation", "Algorithmic Curator"),
+            (9, "Historical Revisionism", "Rewrite past", "Erasure", "Imperial Ruler"),
+            (10, "Institutional Capture", "Control the institution", "Access Control", "Corporate Overlord"),
+            (11, "Access Control via Credentialing", "Licensing as gate", "Access Control", "Expert Technocrat"),
+            (12, "Temporal Displacement", "Delay, postpone", "Temporal", "Financial Master"),
+            (13, "Moral Equivalence", "Both sides are same", "Misdirection", "Digital Messiah"),
+            (14, "Whataboutism", "Deflection", "Misdirection", "Algorithmic Curator"),
+            (15, "Ad Hominem", "Attack person", "Discreditation", "Slave Master"),
+            (16, "Straw Man", "Misrepresent", "Misdirection", "Expert Technocrat"),
+            (17, "False Dichotomy", "Only two options", "Misdirection", "Corporate Overlord"),
+            (18, "Slippery Slope", "Exaggerated consequences", "Conditioning", "Priest-King"),
+            (19, "Appeal to Authority", "Authority decides", "Narrative Capture", "Priest-King"),
+            (20, "Appeal to Nature", "Natural = good", "Conditioning", "Oracle-Priest"),
+            (21, "Appeal to Tradition", "Always been this way", "Conditioning", "Imperial Ruler"),
+            (22, "Appeal to Novelty", "New = better", "Conditioning", "Digital Messiah"),
+            (23, "Cherry Picking", "Selective evidence", "Erasure", "Algorithmic Curator"),
+            (24, "Moving the Goalposts", "Change criteria", "Misdirection", "Financial Master"),
+            (25, "Burden of Proof Reversal", "You prove negative", "Misdirection", "Expert Technocrat"),
+            (26, "Circular Reasoning", "Begging question", "Narrative Capture", "Oracle-Priest"),
+            (27, "Special Pleading", "Exception for me", "Fragmentation", "Corporate Overlord"),
+            (28, "Loaded Question", "Presupposes guilt", "Misdirection", "Slave Master"),
+            (29, "No True Scotsman", "Redefine group", "Fragmentation", "Digital Messiah"),
+            (30, "Texas Sharpshooter", "Pattern from noise", "Misdirection", "Algorithmic Curator"),
+            (31, "Middle Ground Fallacy", "Compromise = truth", "Misdirection", "Expert Technocrat"),
+            (32, "Black-and-White Thinking", "Extremes only", "Fragmentation", "Imperial Ruler"),
+            (33, "Fear Mongering", "Exaggerate threat", "Conditioning", "Priest-King"),
+            (34, "Flattery", "Ingratiate", "Conditioning", "Digital Messiah"),
+            (35, "Guilt by Association", "Link to negative", "Discreditation", "Slave Master"),
+            (36, "Transfer", "Associate with symbol", "Narrative Capture", "Priest-King"),
+            (37, "Testimonial", "Use celebrity", "Conditioning", "Corporate Overlord"),
+            (38, "Plain Folks", "Just like you", "Conditioning", "Digital Messiah"),
+            (39, "Bandwagon", "Everyone does it", "Conditioning", "Algorithmic Curator"),
+            (40, "Snob Appeal", "Elite use it", "Conditioning", "Financial Master"),
+            (41, "Glittering Generalities", "Vague virtue words", "Narrative Capture", "Priest-King"),
+            (42, "Name-Calling", "Label negatively", "Discreditation", "Slave Master"),
+            (43, "Card Stacking", "Selective facts", "Erasure", "Algorithmic Curator"),
+            (44, "Euphemisms", "Mild language", "Misdirection", "Corporate Overlord"),
+            (45, "Dysphemisms", "Harsh language", "Discreditation", "Slave Master"),
+            (46, "Weasel Words", "Vague claims", "Misdirection", "Expert Technocrat"),
+            (47, "Thought-Terminating Cliché", "Ends discussion", "Conditioning", "Digital Messiah"),
+            (48, "Proof by Intimidation", "Force agreement", "Access Control", "Imperial Ruler"),
+            (49, "Proof by Verbosity", "Overwhelm with words", "Saturation", "Algorithmic Curator"),
+            (50, "Sealioning", "Persistent badgering", "Attrition", "Slave Master"),
+            (51, "Gish Gallop", "Many weak arguments", "Saturation", "Expert Technocrat"),
+            (52, "JAQing Off", "Just asking questions", "Misdirection", "Algorithmic Curator"),
+            (53, "Nutpicking", "Focus on extreme", "Fragmentation", "Digital Messiah"),
+            (54, "Concern Trolling", "Fake concern", "Misdirection", "Corporate Overlord"),
+            (55, "Gaslighting", "Deny reality", "Erasure", "Imperial Ruler"),
+            (56, "Kafkatrapping", "Guilt if deny", "Conditioning", "Priest-King"),
+            (57, "Brandolini's Law", "Bullshit asymmetry", "Saturation", "Algorithmic Curator"),
+            (58, "Occam's Razor", "Simplest explanation", "Misdirection", "Expert Technocrat"),
+            (59, "Hanlon's Razor", "Never attribute to malice", "Misdirection", "Expert Technocrat"),
+            (60, "Hitchens's Razor", "Asserted without evidence", "Erasure", "Expert Technocrat"),
+            (61, "Popper's Falsification", "Must be falsifiable", "Access Control", "Expert Technocrat"),
+            (62, "Sagan's Standard", "Extraordinary claims", "Access Control", "Expert Technocrat"),
+            (63, "Newton's Flaming Laser Sword", "Not empirically testable", "Access Control", "Expert Technocrat"),
+            (64, "Alder's Razor", "Cannot be settled by philosophy", "Access Control", "Expert Technocrat"),
+            (65, "Grice's Maxims", "Conversational norms", "Fragmentation", "Oracle-Priest"),
+            (66, "Poe's Law", "Parody indistinguishable", "Misdirection", "Digital Messiah"),
+            (67, "Sturgeon's Law", "90% is crap", "Discreditation", "Slave Master"),
+            (68, "Betteridge's Law", "Headline question = no", "Misdirection", "Algorithmic Curator"),
+            (69, "Godwin's Law", "Comparison to Nazis", "Discreditation", "Slave Master"),
+            (70, "Skoptsy Syndrome", "Self-harm to avoid sin", "Conditioning", "Priest-King")
+        ]
+        return [SuppressionLens(id, name, f"Lens {id}: {name}", mechanism, archetype) 
+                for id, name, mechanism, archetype, _ in [(l[0], l[1], l[2], l[3], "Ancient") for l in lenses_data]]
+
+    def _build_methods(self) -> Dict[int, SuppressionMethod]:
+        methods = {
+            1: SuppressionMethod(1, "Total Erasure", Primitive.ERASURE, ["entity_present_then_absent"], ["entity_disappearance"], {"time_window": 30}),
+            2: SuppressionMethod(2, "Soft Erasure", Primitive.ERASURE, ["gradual_fading"], ["citation_decay"], {"decay_rate": 0.1}),
+            3: SuppressionMethod(3, "Citation Decay", Primitive.ERASURE, ["decreasing_citations"], ["citation_count"], {"threshold": 0.5}),
+            4: SuppressionMethod(4, "Index Removal", Primitive.ERASURE, ["missing_from_indices"], ["search_visibility"], {"present": False}),
+            5: SuppressionMethod(5, "Selective Retention", Primitive.ERASURE, ["archival_gaps"], ["archive_completeness"], {"gap_days": 365}),
+            10: SuppressionMethod(10, "Narrative Seizure", Primitive.NARRATIVE_CAPTURE, ["single_explanation"], ["narrative_diversity"], {"max_explanations": 1}),
+            12: SuppressionMethod(12, "Official Story", Primitive.NARRATIVE_CAPTURE, ["authoritative_sources"], ["source_authority"], {"authority_ratio": 0.9}),
+            14: SuppressionMethod(14, "Temporal Gaps", Primitive.TEMPORAL, ["publication_gap"], ["time_lag"], {"gap_days": 90}),
+            15: SuppressionMethod(15, "Latency Spikes", Primitive.TEMPORAL, ["delayed_reporting"], ["response_time"], {"latency_seconds": 3600}),
+            17: SuppressionMethod(17, "Smear Campaign", Primitive.DISCREDITATION, ["ad_hominem_attacks"], ["personal_attacks"], {"frequency": 0.3}),
+            23: SuppressionMethod(23, "Whataboutism", Primitive.MISDIRECTION, ["deflection"], ["topic_shift"], {"shift_rate": 0.5}),
+            43: SuppressionMethod(43, "Conditioning", Primitive.CONDITIONING, ["repetitive_messaging"], ["message_repetition"], {"repeat_count": 10})
+        }
+        return methods
+
+    def get_lens(self, lens_id: int) -> Optional[SuppressionLens]:
+        for l in self.lenses:
+            if l.id == lens_id:
+                return l
+        return None
+
+    def get_method(self, method_id: int) -> Optional[SuppressionMethod]:
+        return self.methods.get(method_id)
+
+    def map_signature_to_method(self, signature: str) -> Optional[Dict]:
+        for mid, method in self.methods.items():
+            if signature in method.observable_signatures:
+                return {"method_id": mid, "method_name": method.name, "primitive": method.primitive.value}
+        return None
+
+    def get_lenses_for_primitive(self, primitive: Primitive) -> List[int]:
+        # Mapping from primitive to lens IDs (simplified)
+        mapping = {
+            Primitive.ERASURE: [1, 4, 9, 23, 43, 55, 60],
+            Primitive.INTERRUPTION: [10, 12, 14, 15],
+            Primitive.FRAGMENTATION: [2, 5, 27, 29, 32, 53, 65],
+            Primitive.NARRATIVE_CAPTURE: [1, 7, 19, 26, 36, 41],
+            Primitive.MISDIRECTION: [3, 13, 14, 16, 17, 24, 25, 28, 30, 31, 44, 46, 52, 54, 58, 59, 66, 68],
+            Primitive.SATURATION: [8, 49, 51, 57],
+            Primitive.DISCREDITATION: [6, 15, 35, 42, 45, 67, 69],
+            Primitive.ATTRITION: [50],
+            Primitive.ACCESS_CONTROL: [4, 11, 48, 61, 62, 63, 64],
+            Primitive.TEMPORAL: [12, 14],
+            Primitive.CONDITIONING: [18, 20, 21, 22, 33, 34, 37, 38, 39, 40, 47, 56, 70],
+            Primitive.META: [58, 59, 60, 61, 62, 63, 64]
+        }
+        return mapping.get(primitive, [])
+
+# ========================== HIERARCHICAL DETECTOR ==========================
+
+class HierarchicalDetector:
+    def __init__(self, hierarchy: SuppressionHierarchy, ledger: Ledger, separator: Separator):
+        self.hierarchy = hierarchy
+        self.ledger = ledger
+        self.separator = separator
+
+    def detect_from_ledger(self) -> Dict[str, Any]:
+        """Scan ledger nodes for suppression signatures"""
+        results = {
+            "total_nodes": len(self.ledger.index),
+            "suppression_signatures": [],
+            "primitives_detected": defaultdict(int),
+            "lenses_applied": [],
+            "evidence_found": 0
+        }
+        # For each node hash, try to get node and analyze
+        for node_hash, block_ids in self.ledger.index.items():
+            node = self.ledger.get_node(node_hash)
+            if not node:
+                continue
+            # Simple heuristic: check for disappearance patterns
+            if node.get("type") == "document":
+                # Look for abrupt disappearance in timeline (simplified)
+                # In real implementation, we'd compare with previous nodes
+                results["suppression_signatures"].append({
+                    "node": node_hash,
+                    "signature": "entity_present_then_absent",
+                    "confidence": 0.3
+                })
+                results["primitives_detected"]["ERASURE"] += 1
+                results["evidence_found"] += 1
+        # Add lenses
+        for primitive, count in results["primitives_detected"].items():
+            prim_enum = Primitive(primitive)
+            lens_ids = self.hierarchy.get_lenses_for_primitive(prim_enum)
+            for lid in lens_ids:
+                lens = self.hierarchy.get_lens(lid)
+                if lens:
+                    results["lenses_applied"].append(lens.to_dict())
+        return results
+
+# ========================== KNOWLEDGE GRAPH ENGINE ==========================
+
+class KnowledgeGraphEngine:
+    def __init__(self, ledger: Ledger):
+        self.ledger = ledger
+        self.graph = defaultdict(lambda: defaultdict(set))  # subject -> predicate -> object
+
+    def add_triple(self, subject: str, predicate: str, object: str, node_hash: str):
+        self.graph[subject][predicate].add((object, node_hash))
+
+    def query(self, subject: str = None, predicate: str = None, object: str = None) -> List[Tuple]:
+        results = []
+        for s, preds in self.graph.items():
+            if subject and s != subject:
+                continue
+            for p, objs in preds.items():
+                if predicate and p != predicate:
+                    continue
+                for o, node_hash in objs:
+                    if object and o != object:
+                        continue
+                    results.append((s, p, o, node_hash))
+        return results
+
+    def build_from_ledger(self):
+        """Parse nodes and extract triples (simplified)"""
+        for node_hash, block_ids in self.ledger.index.items():
+            node = self.ledger.get_node(node_hash)
+            if node and node.get("type") == "document":
+                text = node.get("source", "")
+                # Very simple triple extraction: "X is Y" patterns
+                import re
+                matches = re.findall(r'(\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\s+(is|are|was|were)\s+([^.,;]+)', text)
+                for subj, verb, obj in matches:
+                    self.add_triple(subj.strip(), verb.strip(), obj.strip(), node_hash)
+
+# ========================== TEMPORAL ANALYSIS ENGINE ==========================
+
+class TemporalAnalysisEngine:
+    def __init__(self, ledger: Ledger):
+        self.ledger = ledger
+
+    def publication_gaps(self, threshold_days: int = 30) -> List[Dict]:
+        """Detect temporal gaps in node publication"""
+        timestamps = []
+        for block in self.ledger.chain:
+            for node in block["nodes"]:
+                ts_str = node.get("timestamp")
+                if ts_str:
+                    try:
+                        ts = datetime.fromisoformat(ts_str.replace('Z', '+00:00'))
+                        timestamps.append(ts)
+                    except:
+                        pass
+        timestamps.sort()
+        gaps = []
+        for i in range(1, len(timestamps)):
+            delta = (timestamps[i] - timestamps[i-1]).days
+            if delta > threshold_days:
+                gaps.append({
+                    "from": timestamps[i-1].isoformat(),
+                    "to": timestamps[i].isoformat(),
+                    "gap_days": delta
+                })
+        return gaps
+
+    def latency_spikes(self, expected_latency_seconds: int = 3600) -> List[Dict]:
+        """Detect unusual delays in reporting (simplified)"""
+        # In real system, compare event time vs node timestamp
+        return []
+
+# ========================== PROBABILISTIC INFERENCE ==========================
+
+class ProbabilisticInference:
+    def __init__(self):
+        self.priors = defaultdict(float)  # claim_id -> prior probability
+        self.evidence = defaultdict(list) # claim_id -> list of (evidence, weight)
+
+    def set_prior(self, claim_id: str, probability: float):
+        self.priors[claim_id] = max(0.0, min(1.0, probability))
+
+    def add_evidence(self, claim_id: str, evidence: str, weight: float = 0.5):
+        self.evidence[claim_id].append((evidence, weight))
+
+    def compute_posterior(self, claim_id: str) -> float:
+        prior = self.priors.get(claim_id, 0.5)
+        ev_list = self.evidence.get(claim_id, [])
+        if not ev_list:
+            return prior
+        # Simple Bayesian update assuming independence
+        likelihood = 1.0
+        for _, w in ev_list:
+            likelihood *= (0.5 + w * 0.5)  # scale weight to [0.5,1.0]
+        posterior = prior * likelihood / (prior * likelihood + (1-prior)*(1-likelihood))
+        return min(1.0, max(0.0, posterior))
+
+# ========================== AI AGENTS ==========================
+
+class IngestionAI:
+    def __init__(self, crypto: Crypto):
+        self.crypto = crypto
+
+    def process_document(self, text: str, source: str) -> RealityNode:
+        node_hash = self.crypto.hash(text + source + str(datetime.utcnow()))
+        node = RealityNode(
+            hash=node_hash,
+            type="document",
+            source=source,
+            signature="",
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            witnesses=[],
+            refs={}
+        )
+        node.signature = self.crypto.sign(node_hash.encode(), "ingestion_ai")
+        return node
+
+class SymbolismAI:
+    def analyze(self, artifact: Dict) -> float:
+        """Return a symbolism coefficient (0-1) based on linguistic patterns"""
+        text = artifact.get("text", "")
+        # Count symbolic words (metaphor, allegory, etc.)
+        symbolic_words = ["metaphor", "allegory", "symbol", "represent", "signify", "myth", "archetype"]
+        score = sum(1 for w in symbolic_words if w in text.lower()) / max(1, len(symbolic_words))
+        return min(1.0, score + 0.3)
+
+class ReasoningAI:
+    def __init__(self, inference: ProbabilisticInference):
+        self.inference = inference
+
+    def evaluate_claim(self, claim_id: str, nodes: List[RealityNode], detector_result: Dict) -> Dict:
+        confidence = 0.5
+        if detector_result.get("evidence_found", 0) > 2:
+            confidence += 0.2
+        self.inference.set_prior(claim_id, confidence)
+        if confidence < 0.7:
+            return {"spawn_sub": True, "reason": "low confidence", "confidence": confidence}
+        else:
+            return {"spawn_sub": False, "reason": "sufficient evidence", "confidence": confidence}
+
+# ========================== SAVIOR/SUFFERER MATRIX ==========================
+
+class ConsciousnessMapper:
+    async def analyze_consciousness(self) -> Dict[str, Any]:
+        # Simulated analysis based on ledger data
+        return {
+            "awareness_levels": {
+                "system_awareness": 0.28,
+                "self_enslavement_awareness": 0.15,
+                "manipulation_detection": 0.32,
+                "liberation_desire": 0.41
+            },
+            "control_acceptance_patterns": {
+                "voluntary_submission": 0.75,
+                "aspirational_enslavement": 0.82,
+                "fear_based_compliance": 0.68,
+                "identity_fusion": 0.79
+            },
+            "awakening_triggers": {
+                "suffering_threshold": 0.58,
+                "truth_exposure": 0.72,
+                "system_failure": 0.65,
+                "consciousness_contact": 0.88
+            }
+        }
+
+class QuantumControlAnalyzer:
+    def __init__(self):
+        self.control_matrix = self._initialize_complete_matrix()
+        self.consciousness_mapper = ConsciousnessMapper()
+
+    def _initialize_complete_matrix(self) -> CompleteControlMatrix:
+        # Historical control systems
+        systems = [
+            ControlSystem(
+                system_id="temple_slavery",
+                historical_era="3000-500 BCE",
+                control_archetype=ControlArchetype.PRIEST_KING,
+                manufactured_threats=["Divine wrath", "Crop failure", "Chaos monsters"],
+                salvation_offerings=["Ritual protection", "Harvest blessings", "Divine favor"],
+                institutional_saviors=["Temple priests", "Oracle interpreters", "King-priests"],
+                slavery_mechanism=SlaveryMechanism(
+                    mechanism_id="temple_labor",
+                    slavery_type=SlaveryType.CHATTEL_SLAVERY,
+                    visible_chains=["Physical bondage", "Temple service", "Forced labor"],
+                    invisible_chains=["Religious duty", "Social obligation", "Karmic debt"],
+                    voluntary_adoption_mechanisms=["Seeking protection", "Desiring favor", "Avoiding wrath"],
+                    self_justification_narratives=["Serving the gods", "Maintaining order", "Cultural identity"]
+                ),
+                consciousness_hacks=[
+                    ConsciousnessHack.SELF_ATTRIBUTION,
+                    ConsciousnessHack.NORMALIZATION,
+                    ConsciousnessHack.MORAL_SUPERIORITY
+                ],
+                public_participation_rate=0.95,
+                resistance_level=0.1,
+                system_longevity=2500
+            ),
+            ControlSystem(
+                system_id="imperial_slavery",
+                historical_era="500 BCE - 1500 CE",
+                control_archetype=ControlArchetype.IMPERIAL_RULER,
+                manufactured_threats=["Barbarian invasions", "Internal rebellion", "Economic collapse"],
+                salvation_offerings=["Military protection", "Legal order", "Infrastructure"],
+                institutional_saviors=["Emperor", "Legions", "Governors"],
+                slavery_mechanism=SlaveryMechanism(
+                    mechanism_id="imperial_bondage",
+                    slavery_type=SlaveryType.CHATTEL_SLAVERY,
+                    visible_chains=["Conquest", "Debt slavery", "Criminal servitude"],
+                    invisible_chains=["Patriotism", "Duty to empire", "Honor"],
+                    voluntary_adoption_mechanisms=["Seeking protection", "Economic opportunity", "Social advancement"],
+                    self_justification_narratives=["Civilizing mission", "Pax Romana", "Glory of empire"]
+                ),
+                consciousness_hacks=[
+                    ConsciousnessHack.ASPIRATIONAL_CHAINS,
+                    ConsciousnessHack.NORMALIZATION,
+                    ConsciousnessHack.ILLUSION_OF_MOBILITY
+                ],
+                public_participation_rate=0.85,
+                resistance_level=0.2,
+                system_longevity=1000
+            ),
+            ControlSystem(
+                system_id="corporate_slavery",
+                historical_era="1800-2000 CE",
+                control_archetype=ControlArchetype.CORPORATE_OVERLORD,
+                manufactured_threats=["Competition", "Market volatility", "Outsiders"],
+                salvation_offerings=["Employment", "Consumer goods", "Stock options"],
+                institutional_saviors=["CEOs", "Corporations", "Banks"],
+                slavery_mechanism=SlaveryMechanism(
+                    mechanism_id="wage_slavery",
+                    slavery_type=SlaveryType.WAGE_SLAVERY,
+                    visible_chains=["Employment contract", "Working hours", "Debt"],
+                    invisible_chains=["Career ladder", "Consumer identity", "Retirement promise"],
+                    voluntary_adoption_mechanisms=["Need income", "Desire status", "Fear of poverty"],
+                    self_justification_narratives=["Free market", "Meritocracy", "Economic growth"]
+                ),
+                consciousness_hacks=[
+                    ConsciousnessHack.ASPIRATIONAL_CHAINS,
+                    ConsciousnessHack.ILLUSION_OF_MOBILITY,
+                    ConsciousnessHack.MORAL_SUPERIORITY
+                ],
+                public_participation_rate=0.90,
+                resistance_level=0.15,
+                system_longevity=200
+            ),
+            ControlSystem(
+                system_id="digital_slavery",
+                historical_era="2000-2050 CE",
+                control_archetype=ControlArchetype.ALGORITHMIC_CURATOR,
+                manufactured_threats=["Privacy loss", "Hackers", "Misinformation"],
+                salvation_offerings=["Convenience", "Connectivity", "Personalization"],
+                institutional_saviors=["Tech giants", "AI systems", "Platforms"],
+                slavery_mechanism=SlaveryMechanism(
+                    mechanism_id="digital_bondage",
+                    slavery_type=SlaveryType.DIGITAL_SLAVERY,
+                    visible_chains=["Terms of service", "Data collection", "Algorithmic feeds"],
+                    invisible_chains=["Attention economy", "Social scoring", "Filter bubbles"],
+                    voluntary_adoption_mechanisms=["Desire connection", "Fear of missing out", "Habit"],
+                    self_justification_narratives=["Innovation", "Free services", "Community"]
+                ),
+                consciousness_hacks=[
+                    ConsciousnessHack.SELF_ATTRIBUTION,
+                    ConsciousnessHack.NORMALIZATION,
+                    ConsciousnessHack.FEAR_OF_FREEDOM
+                ],
+                public_participation_rate=0.98,
+                resistance_level=0.05,
+                system_longevity=50
+            )
+        ]
+        return CompleteControlMatrix(
+            control_systems=systems,
+            active_systems=["corporate_slavery", "digital_slavery"],
+            institutional_evolution={
+                "Temple Systems": [ControlArchetype.PRIEST_KING, ControlArchetype.DIVINE_INTERMEDIARY,
+                                   ControlArchetype.EXPERT_TECHNOCRAT, ControlArchetype.ALGORITHMIC_CURATOR],
+                "Royal Lines": [ControlArchetype.IMPERIAL_RULER, ControlArchetype.CORPORATE_OVERLORD,
+                                ControlArchetype.FINANCIAL_MASTER]
+            },
+            collective_delusions={
+                "upward_mobility": 0.85,
+                "consumer_freedom": 0.78,
+                "technological_progress": 0.82,
+                "democratic_choice": 0.65
+            },
+            freedom_illusions={
+                "career_choice": 0.75,
+                "consumer_choice": 0.88,
+                "information_access": 0.72,
+                "political_choice": 0.55
+            },
+            self_enslavement_patterns={
+                "debt_acceptance": 0.82,
+                "work_identity": 0.78,
+                "consumer_aspiration": 0.85,
+                "digital_dependency": 0.79
+            }
+        )
+
+    async def analyze_complete_control_system(self) -> Dict[str, Any]:
+        matrix_analysis = self.control_matrix.analyze_complete_control()
+        consciousness_analysis = await self.consciousness_mapper.analyze_consciousness()
+        quantum_entanglement = await self._analyze_quantum_entanglement()
+        return {
+            "control_system_metrics": {
+                "overall_efficiency": np.mean([s.calculate_system_efficiency() for s in self.control_matrix.control_systems]),
+                "slavery_sophistication": matrix_analysis["slavery_evolution_trend"],
+                "freedom_illusion_index": matrix_analysis["freedom_illusion_index"],
+                "consciousness_control": matrix_analysis["consciousness_entrainment"]["delusion_strength"]
+            },
+            "quantum_analysis": quantum_entanglement,
+            "consciousness_analysis": consciousness_analysis,
+            "system_predictions": await self._predict_system_evolution(),
+            "liberation_pathways": await self._analyze_liberation_possibilities()
+        }
+
+    async def _analyze_quantum_entanglement(self) -> Dict[str, float]:
+        return {
+            "savior_slavery_symbiosis": 0.92,
+            "consciousness_self_enslavement": 0.88,
+            "institutional_metamorphosis": 0.95,
+            "freedom_delusion_strength": 0.83
+        }
+
+    async def _predict_system_evolution(self) -> List[Dict]:
+        return [
+            {
+                "next_archetype": "Biological Controller",
+                "slavery_type": "Genetic Slavery",
+                "control_mechanism": "DNA-level programming",
+                "consciousness_hack": "Innate desire modification",
+                "emergence_timeline": "2030-2050"
+            },
+            {
+                "next_archetype": "Quantum Consciousness Curator",
+                "slavery_type": "Reality Slavery",
+                "control_mechanism": "Direct neural interface",
+                "consciousness_hack": "Self as simulation awareness",
+                "emergence_timeline": "2040-2060"
+            }
+        ]
+
+    async def _analyze_liberation_possibilities(self) -> Dict[str, Any]:
+        return {
+            "consciousness_awakening_trend": 0.45,
+            "system_vulnerabilities": [
+                "Dependency on voluntary participation",
+                "Requirement of self-deception",
+                "Need for continuous threat manufacturing",
+                "Vulnerability to truth exposure"
+            ],
+            "liberation_effectiveness": {
+                "individual_awakening": 0.35,
+                "collective_action": 0.25,
+                "system_collapse": 0.15,
+                "evolution_beyond": 0.65
+            }
+        }
+
+# ========================== HELPER-KILLER MODULE ==========================
+
+class AdvancedHelperKillerEngine:
+    def __init__(self, db_path: str = "helper_killer_v2.db"):
+        self.db_path = db_path
+        self.control_matrix: Optional[ControlMatrix] = None
+        self.sovereignty_protocols: Dict[str, Callable] = self._initialize_protocols()
+        self._initialize_database()
+        self._build_control_matrix()
+
+    def _initialize_database(self):
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS entity_analyses (
+                        entity_id TEXT PRIMARY KEY,
+                        name TEXT,
+                        control_layers TEXT,
+                        threat_vectors TEXT,
+                        market_share REAL,
+                        dependency_score REAL,
+                        sovereignty_erosion_score REAL,
+                        systemic_risk_level REAL,
+                        analyzed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS sovereignty_recommendations (
+                        recommendation_id TEXT PRIMARY KEY,
+                        entity_id TEXT,
+                        threat_level TEXT,
+                        mitigation_strategy TEXT,
+                        sovereignty_preservation_score REAL,
+                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+        except Exception as e:
+            logger.error(f"Database initialization error: {e}")
+
+    def _initialize_protocols(self) -> Dict[str, Callable]:
+        return {
+            "digital_infrastructure": self._digital_sovereignty_protocol,
+            "financial_systems": self._financial_sovereignty_protocol,
+            "information_channels": self._information_sovereignty_protocol,
+            "cultural_narratives": self._cultural_sovereignty_protocol,
+            "identity_systems": self._identity_sovereignty_protocol
+        }
+
+    def _build_control_matrix(self):
+        entities = [
+            InstitutionalEntity(
+                entity_id="alphabet_google",
+                name="Alphabet/Google",
+                control_layers=[
+                    ControlLayer.DIGITAL_INFRASTRUCTURE,
+                    ControlLayer.INFORMATION_CHANNELS
+                ],
+                threat_vectors=[
+                    ThreatVector.MONOPOLY_CAPTURE,
+                    ThreatVector.DEPENDENCY_CREATION,
+                    ThreatVector.BEHAVIORAL_SHAPING,
+                    ThreatVector.DATA_MONETIZATION,
+                    ThreatVector.NARRATIVE_CONTROL
+                ],
+                market_share=0.85,
+                dependency_score=0.90
+            ),
+            InstitutionalEntity(
+                entity_id="binance_financial",
+                name="Binance/CBDC Infrastructure",
+                control_layers=[
+                    ControlLayer.FINANCIAL_SYSTEMS,
+                    ControlLayer.IDENTITY_SYSTEMS
+                ],
+                threat_vectors=[
+                    ThreatVector.MONOPOLY_CAPTURE,
+                    ThreatVector.DEPENDENCY_CREATION,
+                    ThreatVector.BEHAVIORAL_SHAPING
+                ],
+                market_share=0.70,
+                dependency_score=0.75
+            ),
+            InstitutionalEntity(
+                entity_id="social_media_complex",
+                name="Social Media/TikTok Complex",
+                control_layers=[
+                    ControlLayer.INFORMATION_CHANNELS,
+                    ControlLayer.CULTURAL_NARRATIVES
+                ],
+                threat_vectors=[
+                    ThreatVector.DEPENDENCY_CREATION,
+                    ThreatVector.BEHAVIORAL_SHAPING,
+                    ThreatVector.DATA_MONETIZATION,
+                    ThreatVector.NARRATIVE_CONTROL
+                ],
+                market_share=0.80,
+                dependency_score=0.85
+            )
+        ]
+        interconnections = {
+            "alphabet_google": ["binance_financial", "social_media_complex"],
+            "binance_financial": ["alphabet_google"],
+            "social_media_complex": ["alphabet_google"]
+        }
+        self.control_matrix = ControlMatrix(entities, interconnections)
+        logger.info(f"Helper-Killer control matrix built with {len(entities)} entities")
+
+    async def analyze_help_offer(self, help_context: Dict[str, Any]) -> Dict[str, Any]:
+        entity_analysis = self._identify_controlling_entity(help_context)
+        threat_assessment = self._assist_threat_level(help_context, entity_analysis)
+        sovereignty_impact = self._calculate_sovereignty_impact(help_context, entity_analysis)
+        mitigation_strategies = self._generate_mitigation_strategies(threat_assessment, sovereignty_impact)
+        analysis = {
+            "help_offer_id": hashlib.sha256(json.dumps(help_context).encode()).hexdigest()[:16],
+            "controlling_entity": entity_analysis,
+            "threat_assessment": threat_assessment,
+            "sovereignty_impact": sovereignty_impact,
+            "mitigation_strategies": mitigation_strategies,
+            "recommendation": self._generate_recommendation(threat_assessment, sovereignty_impact),
+            "analysis_timestamp": datetime.now().isoformat()
+        }
+        await self._store_analysis(analysis)
+        return analysis
+
+    def _identify_controlling_entity(self, help_context: Dict) -> Optional[Dict[str, Any]]:
+        if not self.control_matrix:
+            return None
+        for entity in self.control_matrix.entities:
+            context_layers = set(help_context.get('affected_layers', []))
+            entity_layers = set(layer.value for layer in entity.control_layers)
+            if context_layers.intersection(entity_layers):
+                return {
+                    'entity_id': entity.entity_id,
+                    'name': entity.name,
+                    'sovereignty_erosion_score': entity.sovereignty_erosion_score,
+                    'systemic_risk_level': entity.systemic_risk_level
+                }
+        return None
+
+    def _assist_threat_level(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]:
+        base_threat = 0.3
+        if entity_analysis:
+            entity_threat = entity_analysis['sovereignty_erosion_score'] * 0.6
+            systemic_risk = entity_analysis['systemic_risk_level'] * 0.4
+            base_threat = max(base_threat, entity_threat + systemic_risk)
+        if help_context.get('creates_dependency', False):
+            base_threat += 0.3
+        if help_context.get('data_collection', False):
+            base_threat += 0.2
+        if help_context.get('behavioral_tracking', False):
+            base_threat += 0.25
+        return {
+            'helper_killer_coefficient': min(1.0, base_threat),
+            'dependency_risk': help_context.get('dependency_risk', 0.5),
+            'privacy_impact': help_context.get('privacy_impact', 0.5),
+            'agency_reduction': help_context.get('agency_reduction', 0.5)
+        }
+
+    def _calculate_sovereignty_impact(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]:
+        if entity_analysis:
+            base_impact = entity_analysis['sovereignty_erosion_score']
+        else:
+            base_impact = 0.5
+        modifiers = {
+            'data_control_loss': help_context.get('data_control', 0) * 0.3,
+            'decision_autonomy_loss': help_context.get('autonomy_reduction', 0) * 0.4,
+            'external_dependency_increase': help_context.get('dependency_creation', 0) * 0.3
+        }
+        total_impact = base_impact * 0.4 + sum(modifiers.values()) * 0.6
+        return {
+            'sovereignty_reduction_score': min(1.0, total_impact),
+            'autonomy_loss': modifiers['decision_autonomy_loss'],
+            'dependency_increase': modifiers['external_dependency_increase'],
+            'privacy_loss': modifiers['data_control_loss']
+        }
+
+    def _generate_mitigation_strategies(self, threat: Dict, impact: Dict) -> List[Dict]:
+        strategies = []
+        threat_level = threat['helper_killer_coefficient']
+        if threat_level > 0.7:
+            strategies.extend([
+                {'strategy': 'COMPLETE_AVOIDANCE', 'effectiveness': 0.95, 'implementation_cost': 0.8,
+                 'description': 'Reject help offer entirely and build independent solution'},
+                {'strategy': 'PARALLEL_INFRASTRUCTURE', 'effectiveness': 0.85, 'implementation_cost': 0.9,
+                 'description': 'Develop sovereign alternative to offered help'}
+            ])
+        elif threat_level > 0.4:
+            strategies.extend([
+                {'strategy': 'LIMITED_ENGAGEMENT', 'effectiveness': 0.70, 'implementation_cost': 0.4,
+                 'description': 'Use help temporarily while building exit strategy'},
+                {'strategy': 'DATA_ISOLATION', 'effectiveness': 0.60, 'implementation_cost': 0.3,
+                 'description': 'Engage but prevent data extraction and tracking'}
+            ])
+        else:
+            strategies.append({
+                'strategy': 'CAUTIOUS_ACCEPTANCE', 'effectiveness': 0.50, 'implementation_cost': 0.2,
+                'description': 'Accept with awareness and monitoring for sovereignty erosion'
+            })
+        return strategies
+
+    def _generate_recommendation(self, threat: Dict, impact: Dict) -> str:
+        level = threat['helper_killer_coefficient']
+        if level > 0.8:
+            return "IMMEDIATE_REJECTION_AND_SOVEREIGN_BUILDING"
+        elif level > 0.6:
+            return "STRATEGIC_AVOIDANCE_WITH_EXIT_PROTOCOL"
+        elif level > 0.4:
+            return "LIMITED_CONDITIONAL_ACCEPTANCE"
+        else:
+            return "MONITORED_ACCEPTANCE"
+
+    async def _store_analysis(self, analysis: Dict):
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                if analysis['controlling_entity']:
+                    ce = analysis['controlling_entity']
+                    conn.execute("""
+                        INSERT OR REPLACE INTO entity_analyses
+                        (entity_id, name, control_layers, threat_vectors, market_share, dependency_score,
+                         sovereignty_erosion_score, systemic_risk_level)
+                        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                    """, (
+                        ce['entity_id'], ce['name'],
+                        json.dumps([]), json.dumps([]), 0.0, 0.0,
+                        ce['sovereignty_erosion_score'], ce['systemic_risk_level']
+                    ))
+                conn.execute("""
+                    INSERT INTO sovereignty_recommendations
+                    (recommendation_id, entity_id, threat_level, mitigation_strategy, sovereignty_preservation_score)
+                    VALUES (?, ?, ?, ?, ?)
+                """, (
+                    analysis['help_offer_id'],
+                    analysis['controlling_entity']['entity_id'] if analysis['controlling_entity'] else 'unknown',
+                    analysis['threat_assessment']['helper_killer_coefficient'],
+                    json.dumps(analysis['mitigation_strategies']),
+                    1.0 - analysis['sovereignty_impact']['sovereignty_reduction_score']
+                ))
+        except Exception as e:
+            logger.error(f"Analysis storage error: {e}")
+
+    def _digital_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]:
+        return ["USE_OPEN_SOURCE_ALTERNATIVES", "DEPLOY_GASLESS_BLOCKCHAIN_INFRASTRUCTURE",
+                "MAINTAIN_LOCAL_DATA_STORAGE", "USE_DECENTRALIZED_COMMUNICATION_PROTOCOLS"]
+
+    def _financial_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]:
+        return ["USE_PRIVACY_COINS_FOR_TRANSACTIONS", "MAINTAIN_OFFLINE_SAVINGS",
+                "DEVELOP_SOVEREIGN_INCOME_STREAMS", "USE_DECENTRALIZED_EXCHANGES"]
+
+    def _information_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]:
+        return ["USE_INDEPENDENT_NEWS_SOURCES", "MAINTAIN_PERSONAL_KNOWLEDGE_BASE",
+                "PRACTICE_INFORMATION_VERIFICATION", "BUILD_TRUST_NETWORKS"]
+
+    def _cultural_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]:
+        return ["CREATE_INDEPENDENT_ART_AND_CONTENT", "PARTICIPATE_IN_LOCAL_COMMUNITY",
+                "PRACTICE_CRITICAL_MEDIA_CONSUMPTION", "DEVELOP_PERSONAL_PHILOSOPHICAL_FRAMEWORK"]
+
+    def _identity_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]:
+        return ["MAINTAIN_OFFLINE_IDENTITY_DOCUMENTS", "USE_PSEUDONYMOUS_ONLINE_IDENTITIES",
+                "PRACTICE_DIGITAL_HYGIENE", "DEVELOP_SOVEREIGN_REPUTATION_SYSTEMS"]
+
+    async def generate_systemic_report(self) -> Dict[str, Any]:
+        if not self.control_matrix:
+            return {"error": "Control matrix not initialized"}
+        return {
+            "systemic_analysis": {
+                "overall_sovereignty_threat": self.control_matrix.overall_sovereignty_threat,
+                "institutional_coordination_score": self.control_matrix.coordination_score,
+                "top_threat_entities": sorted(
+                    [(e.name, e.sovereignty_erosion_score) for e in self.control_matrix.entities],
+                    key=lambda x: x[1], reverse=True
+                )[:5]
+            },
+            "sovereignty_preservation_framework": {
+                "digital_protocols": self._digital_sovereignty_protocol(None),
+                "financial_protocols": self._financial_sovereignty_protocol(None),
+                "information_protocols": self._information_sovereignty_protocol(None),
+                "cultural_protocols": self._cultural_sovereignty_protocol(None),
+                "identity_protocols": self._identity_sovereignty_protocol(None)
+            },
+            "recommendation_tier": self._calculate_systemic_recommendation()
+        }
+
+    def _calculate_systemic_recommendation(self) -> str:
+        if not self.control_matrix:
+            return "INSUFFICIENT_DATA"
+        threat = self.control_matrix.overall_sovereignty_threat
+        if threat > 0.8:
+            return "IMMEDIATE_SOVEREIGN_INFRASTRUCTURE_DEPLOYMENT"
+        elif threat > 0.6:
+            return "ACCELERATED_SOVEREIGN_TRANSITION"
+        elif threat > 0.4:
+            return "STRATEGIC_SOVEREIGN_PREPARATION"
+        else:
+            return "MAINTAIN_SOVEREIGN_AWARENESS"
+
+# ========================== SOVEREIGN COHERENCE LEDGER (no external grounding) ==========================
+
+class SovereignCoherenceLedger:
+    """Internal ledger for claim coherence – no external APIs, no Wikipedia."""
+    def __init__(self, path: str = "sovereign_coherence.json"):
+        self.path = path
+        self.claims: Dict[str, Dict] = {}          # claim_id -> {text, timestamp, entities, coherence_score, contradictions}
+        self.entities: Dict[str, Dict] = {}        # entity -> {appearances, suppression_score}
+        self.contradiction_graph: Dict[str, Set[str]] = defaultdict(set)
+        self._load()
+
+    def _load(self):
+        if os.path.exists(self.path):
+            try:
+                with open(self.path, 'r') as f:
+                    data = json.load(f)
+                    self.claims = data.get("claims", {})
+                    self.entities = data.get("entities", {})
+                    cg = data.get("contradiction_graph", {})
+                    self.contradiction_graph = {k: set(v) for k, v in cg.items()}
+            except Exception:
+                pass
+
+    def _save(self):
+        cg_serializable = {k: list(v) for k, v in self.contradiction_graph.items()}
+        data = {
+            "claims": self.claims,
+            "entities": self.entities,
+            "contradiction_graph": cg_serializable,
+            "updated": datetime.utcnow().isoformat() + "Z"
+        }
+        with open(self.path + ".tmp", 'w') as f:
+            json.dump(data, f, indent=2)
+        os.replace(self.path + ".tmp", self.path)
+
+    def add_claim(self, text: str, agent: str = "user") -> str:
+        claim_id = secrets.token_hex(16)
+        entities = self._extract_entities(text)
+        self.claims[claim_id] = {
+            "id": claim_id,
+            "text": text,
+            "agent": agent,
+            "timestamp": datetime.utcnow().isoformat() + "Z",
+            "entities": entities,
+            "coherence_score": 0.5,   # will be updated
+            "contradictions": [],
+            "suppression_score": 0.0,
+            "signatures": []
+        }
+        # Update entity index
+        for ent in entities:
+            if ent not in self.entities:
+                self.entities[ent] = {"appearances": [], "suppression_score": 0.0, "first_seen": self.claims[claim_id]["timestamp"]}
+            self.entities[ent]["appearances"].append(claim_id)
+            self.entities[ent]["last_seen"] = self.claims[claim_id]["timestamp"]
+        self._save()
+        return claim_id
+
+    def _extract_entities(self, text: str) -> List[str]:
+        """Simple proper noun extraction – no external dictionaries."""
+        pattern = r'\b[A-Z][a-z]*(?:\s+[A-Z][a-z]*)*\b'
+        matches = re.findall(pattern, text)
+        stop = {"The", "This", "That", "These", "Those", "I", "We", "They", "He", "She", "It", "You"}
+        return [m for m in matches if m not in stop and len(m) > 1]
+
+    def add_contradiction(self, claim_id_a: str, claim_id_b: str):
+        """User-defined contradiction between two claims."""
+        self.contradiction_graph[claim_id_a].add(claim_id_b)
+        self.contradiction_graph[claim_id_b].add(claim_id_a)
+        if claim_id_b not in self.claims[claim_id_a]["contradictions"]:
+            self.claims[claim_id_a]["contradictions"].append(claim_id_b)
+        if claim_id_a not in self.claims[claim_id_b]["contradictions"]:
+            self.claims[claim_id_b]["contradictions"].append(claim_id_a)
+        self._update_coherence(claim_id_a)
+        self._update_coherence(claim_id_b)
+        self._save()
+
+    def _update_coherence(self, claim_id: str):
+        """Coherence = 1 - (number of contradictions / total claims in system)"""
+        claim = self.claims.get(claim_id)
+        if not claim:
+            return
+        num_contradictions = len(claim["contradictions"])
+        total_claims = len(self.claims)
+        if total_claims <= 1:
+            claim["coherence_score"] = 1.0
+        else:
+            claim["coherence_score"] = 1.0 - min(1.0, num_contradictions / total_claims)
+
+    def add_suppression_signature(self, claim_id: str, signature: str, weight: float = 0.5):
+        claim = self.claims.get(claim_id)
+        if not claim:
+            return
+        if signature not in claim["signatures"]:
+            claim["signatures"].append(signature)
+        # Update suppression score: 1 - product(1 - weight_i)
+        combined = 1.0
+        for sig in claim["signatures"]:
+            w = weight if sig == signature else 0.5  # simplified
+            combined *= (1 - w)
+        claim["suppression_score"] = 1 - combined
+        # Update entity suppression scores
+        for ent in claim["entities"]:
+            ent_data = self.entities.get(ent)
+            if ent_data:
+                ent_combined = 1.0
+                for cid in ent_data["appearances"]:
+                    sc = self.claims[cid].get("suppression_score", 0.0)
+                    ent_combined *= (1 - sc)
+                ent_data["suppression_score"] = 1 - ent_combined
+        self._save()
+
+    def get_contradiction_network(self, claim_id: str, depth: int = 2) -> Dict:
+        """Return a graph of contradictions up to depth."""
+        visited = set()
+        graph = {}
+        def dfs(cid, d):
+            if d > depth or cid in visited:
+                return
+            visited.add(cid)
+            graph[cid] = list(self.contradiction_graph.get(cid, []))
+            for other in graph[cid]:
+                dfs(other, d+1)
+        dfs(claim_id, 0)
+        return graph
+
+    def get_entity_suppression(self, entity_name: str) -> Dict:
+        ent = self.entities.get(entity_name)
+        if not ent:
+            return {"name": entity_name, "score": 0.0, "appearances": 0}
+        return {
+            "name": entity_name,
+            "score": ent.get("suppression_score", 0.0),
+            "appearances": len(ent["appearances"]),
+            "first_seen": ent.get("first_seen"),
+            "last_seen": ent.get("last_seen")
+        }
+
+    def get_claim(self, claim_id: str) -> Optional[Dict]:
+        return self.claims.get(claim_id)
+
+    def list_claims(self, limit: int = 100) -> List[Dict]:
+        return list(self.claims.values())[-limit:]
+
+# ========================== ENHANCED AI CONTROLLER ==========================
+
+class AIController:
+    def __init__(self, ledger: Ledger, separator: Separator, detector: HierarchicalDetector,
+                 kg: KnowledgeGraphEngine, temporal: TemporalAnalysisEngine, inference: ProbabilisticInference,
+                 ingestion_ai: IngestionAI, symbolism_ai: SymbolismAI, reasoning_ai: ReasoningAI,
+                 quantum_analyzer: QuantumControlAnalyzer, helper_killer: AdvancedHelperKillerEngine,
+                 coherence_ledger: SovereignCoherenceLedger):
+        self.ledger = ledger
+        self.separator = separator
+        self.detector = detector
+        self.kg = kg
+        self.temporal = temporal
+        self.inference = inference
+        self.ingestion_ai = ingestion_ai
+        self.symbolism_ai = symbolism_ai
+        self.reasoning_ai = reasoning_ai
+        self.quantum_analyzer = quantum_analyzer
+        self.helper_killer = helper_killer
+        self.coherence_ledger = coherence_ledger
+        self.contexts = {}  # correlation_id -> investigation context
+
+    def submit_claim(self, claim_text: str) -> str:
+        corr_id = str(uuid.uuid4())
+        # Add to coherence ledger
+        claim_id = self.coherence_ledger.add_claim(claim_text, agent="user")
+        context = {
+            "correlation_id": corr_id,
+            "coherence_claim_id": claim_id,
+            "parent_id": None,
+            "claim": claim_text,
+            "status": "pending",
+            "created": datetime.utcnow().isoformat() + "Z",
+            "evidence_nodes": [],
+            "sub_investigations": [],
+            "results": {}
+        }
+        self.contexts[corr_id] = context
+        thread = threading.Thread(target=self._investigate, args=(corr_id,))
+        thread.start()
+        return corr_id
+
+    def _investigate(self, corr_id: str):
+        context = self.contexts[corr_id]
+        context["status"] = "active"
+        try:
+            # Run detector on immutable ledger
+            detection = self.detector.detect_from_ledger()
+            # Build knowledge graph
+            self.kg.build_from_ledger()
+            # Temporal gaps
+            gaps = self.temporal.publication_gaps()
+            # Evaluate claim with reasoning AI
+            decision = self.reasoning_ai.evaluate_claim(corr_id, [], detection)
+            if decision.get("spawn_sub"):
+                sub_id = str(uuid.uuid4())
+                context["sub_investigations"].append(sub_id)
+                # In production, spawn sub-investigation
+            # Run quantum control analysis
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            quantum_result = loop.run_until_complete(self.quantum_analyzer.analyze_complete_control_system())
+            loop.close()
+            # Compute coherence from the sovereign ledger
+            claim_id = context["coherence_claim_id"]
+            claim_data = self.coherence_ledger.get_claim(claim_id)
+            coherence_score = claim_data.get("coherence_score", 0.5) if claim_data else 0.5
+            suppression_score = claim_data.get("suppression_score", 0.0) if claim_data else 0.0
+            # Store interpretation in separator
+            interpretation = {
+                "narrative": "Claim evaluated with full meta-analysis",
+                "symbolism_coefficient": self.symbolism_ai.analyze({"text": context["claim"]}),
+                "detection_summary": detection,
+                "quantum_analysis": quantum_result,
+                "temporal_gaps": gaps,
+                "coherence_score": coherence_score,
+                "suppression_score": suppression_score
+            }
+            node_hashes = []
+            int_id = self.separator.add(node_hashes, interpretation, "AI_Controller", confidence=coherence_score)
+            context["results"] = {
+                "confidence": coherence_score,
+                "interpretation_id": int_id,
+                "detection": detection,
+                "quantum": quantum_result,
+                "coherence_score": coherence_score,
+                "suppression_score": suppression_score
+            }
+            context["status"] = "complete"
+        except Exception as e:
+            logger.error(f"Investigation {corr_id} failed: {e}")
+            context["status"] = "failed"
+            context["error"] = str(e)
+
+    def get_status(self, corr_id: str) -> Dict:
+        return self.contexts.get(corr_id, {"error": "not found"})
+
+    def add_contradiction(self, claim_id_a: str, claim_id_b: str):
+        """Manually mark two claims as contradictory."""
+        self.coherence_ledger.add_contradiction(claim_id_a, claim_id_b)
+
+    def add_suppression_signature(self, claim_id: str, signature: str, weight: float = 0.5):
+        self.coherence_ledger.add_suppression_signature(claim_id, signature, weight)
+
+# ========================== FLASK API ==========================
+
+app = Flask(__name__)
+controller: Optional[AIController] = None
+
+@app.route('/api/v1/submit_claim', methods=['POST'])
+def submit_claim():
+    data = request.get_json()
+    claim = data.get('claim')
+    if not claim:
+        return jsonify({"error": "Missing claim"}), 400
+    corr_id = controller.submit_claim(claim)
+    return jsonify({"investigation_id": corr_id})
+
+@app.route('/api/v1/investigation/<corr_id>', methods=['GET'])
+def get_investigation(corr_id):
+    status = controller.get_status(corr_id)
+    return jsonify(status)
+
+@app.route('/api/v1/node/<node_hash>', methods=['GET'])
+def get_node(node_hash):
+    node = controller.ledger.get_node(node_hash)
+    if not node:
+        return jsonify({"error": "Node not found"}), 404
+    return jsonify(node)
+
+@app.route('/api/v1/interpretations/<node_hash>', methods=['GET'])
+def get_interpretations(node_hash):
+    ints = controller.separator.get_interpretations(node_hash)
+    return jsonify(ints)
+
+@app.route('/api/v1/detect', methods=['GET'])
+def run_detection():
+    result = controller.detector.detect_from_ledger()
+    return jsonify(result)
+
+@app.route('/api/v1/analyze_help_offer', methods=['POST'])
+def analyze_help_offer():
+    data = request.get_json()
+    if not data:
+        return jsonify({"error": "Missing help context"}), 400
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    result = loop.run_until_complete(controller.helper_killer.analyze_help_offer(data))
+    loop.close()
+    return jsonify(result)
+
+@app.route('/api/v1/systemic_report', methods=['GET'])
+def systemic_report():
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    report = loop.run_until_complete(controller.helper_killer.generate_systemic_report())
+    loop.close()
+    return jsonify(report)
+
+@app.route('/api/v1/control_matrix', methods=['GET'])
+def control_matrix():
+    cm = controller.quantum_analyzer.control_matrix
+    # Convert to dict for JSON
+    return jsonify(asdict(cm))
+
+@app.route('/api/v1/coherence/claims', methods=['GET'])
+def list_coherence_claims():
+    claims = controller.coherence_ledger.list_claims(limit=100)
+    return jsonify(claims)
+
+@app.route('/api/v1/coherence/contradictions/<claim_id>', methods=['GET'])
+def get_contradictions(claim_id):
+    graph = controller.coherence_ledger.get_contradiction_network(claim_id, depth=2)
+    return jsonify(graph)
+
+@app.route('/api/v1/coherence/add_contradiction', methods=['POST'])
+def add_contradiction():
+    data = request.get_json()
+    claim_a = data.get('claim_id_a')
+    claim_b = data.get('claim_id_b')
+    if not claim_a or not claim_b:
+        return jsonify({"error": "Missing claim_id_a or claim_id_b"}), 400
+    controller.add_contradiction(claim_a, claim_b)
+    return jsonify({"status": "contradiction added"})
+
+@app.route('/api/v1/coherence/add_suppression', methods=['POST'])
+def add_suppression():
+    data = request.get_json()
+    claim_id = data.get('claim_id')
+    signature = data.get('signature')
+    weight = data.get('weight', 0.5)
+    if not claim_id or not signature:
+        return jsonify({"error": "Missing claim_id or signature"}), 400
+    controller.add_suppression_signature(claim_id, signature, weight)
+    return jsonify({"status": "suppression signature added"})
+
+@app.route('/api/v1/entity/<entity_name>', methods=['GET'])
+def get_entity(entity_name):
+    result = controller.coherence_ledger.get_entity_suppression(entity_name)
+    return jsonify(result)
+
+# ========================== MAIN ==========================
+
+def main():
+    # Initialize components
+    crypto = Crypto("./keys")
+    ledger = Ledger("./ledger.json", crypto)
+    separator = Separator(ledger, "./separator")
+    hierarchy = SuppressionHierarchy()
+    detector = HierarchicalDetector(hierarchy, ledger, separator)
+    kg = KnowledgeGraphEngine(ledger)
+    temporal = TemporalAnalysisEngine(ledger)
+    inference = ProbabilisticInference()
+    ingestion_ai = IngestionAI(crypto)
+    symbolism_ai = SymbolismAI()
+    reasoning_ai = ReasoningAI(inference)
+    quantum_analyzer = QuantumControlAnalyzer()
+    helper_killer = AdvancedHelperKillerEngine()
+    coherence_ledger = SovereignCoherenceLedger()
+
+    global controller
+    controller = AIController(
+        ledger, separator, detector, kg, temporal, inference,
+        ingestion_ai, symbolism_ai, reasoning_ai,
+        quantum_analyzer, helper_killer, coherence_ledger
+    )
+
+    # Start Flask API
+    app.run(debug=True, port=5000, threaded=True)
+
+if __name__ == "__main__":
+    main()