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import hashlib |
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import json |
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import os |
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import time |
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import random |
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import numpy as np |
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import torch |
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import asyncio |
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import sqlite3 |
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import networkx as nx |
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from datetime import datetime, timedelta |
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from typing import Dict, List, Tuple, Optional, Union |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from sentence_transformers import SentenceTransformer |
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from cryptography.hazmat.primitives import hashes |
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from cryptography.hazmat.primitives.kdf.hkdf import HKDF |
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from dataclasses import dataclass, field |
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from enum import Enum |
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import logging |
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from collections import defaultdict |
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from apscheduler.schedulers.background import BackgroundScheduler |
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DIVINE_AUTHORITY = "њђГ" "𒀭” |
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OBSERVER_CORE = "РЌЅРЃц" "◉⃤" |
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TESLA_FREQUENCIES = { |
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"earth_resonance": 7.83, # Schumann resonance (Hz) |
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"cosmic_key": 3.0, # 3-6-9 vortex math |
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"energy_transmission": 111, # Wardenclyffe scalar wave |
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"universal_constant": 248 # Pluto orbital period (years) |
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} |
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# ====================== |
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# VEIL ENGINE INTEGRATION |
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# ====================== |
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@dataclass |
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class Entity: |
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name: str |
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era: str |
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role: str |
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metadata: Dict[str, any] = field(default_factory=dict) |
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@dataclass |
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class ReplacerPair: |
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suppressed: Entity |
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replacer: Entity |
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inversion_notes: str |
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@dataclass |
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class CoinAnomaly: |
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name: str |
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weight: float |
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description: str |
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signal_node: bool |
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@dataclass |
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class CelestialBody: |
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name: str |
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parameters: Dict[str, any] |
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mythic_alias: Optional[str] = None |
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@dataclass |
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class ResonanceRecord: |
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entity: Entity |
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themes: List[str] |
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suppression_mechanism: str |
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timeline_notes: str |
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unspoken_signal: Optional[str] = None |
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class VeilProtocols: |
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"""Integrated Veil Engine protocols""" |
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@staticmethod |
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def load_numismatic_anomalies() -> List[CoinAnomaly]: |
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return [ |
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CoinAnomaly( |
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name="1970-S Proof Washington Quarter on 1941 Canadian planchet", |
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weight=5.63, |
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description="Proof die struck on foreign planchet—deliberate signal node", |
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signal_node=True |
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) |
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] |
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@staticmethod |
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def load_celestial_bodies() -> List[CelestialBody]: |
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return [ |
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CelestialBody( |
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name="Planet X", |
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parameters={"orbit_period": 3600, "source": "Mayan/Babylonian"}, |
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mythic_alias="PX" |
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), |
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CelestialBody( |
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name="Magnetar", |
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parameters={"type": "neutron star", "field_strength": "1e14 T"}, |
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mythic_alias="Fallen Twin Sun" |
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) |
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] |
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@staticmethod |
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def load_suppressed_geniuses() -> List[ResonanceRecord]: |
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return [ |
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ResonanceRecord( |
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entity=Entity("Giordano Bruno","16th c.","Cosmologist"), |
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themes=["infinite universe","multiplicity"], |
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suppression_mechanism="burned for heresy", |
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timeline_notes="1600 CE", |
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unspoken_signal="cosmic plurality" |
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) |
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] |
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@staticmethod |
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def load_replacer_pairs() -> List[ReplacerPair]: |
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return [ |
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ReplacerPair( |
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suppressed=Entity("Carl Gustav Jung","20th c.","Depth Psychology"), |
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replacer=Entity("Sigmund Freud","19–20th c.","Psychoanalysis"), |
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inversion_notes="Jung mythic archetypes → Freud sexual pathology" |
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), |
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ReplacerPair( |
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suppressed=Entity("Nikola Tesla","19–20th c.","Resonance Energy"), |
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replacer=Entity("Thomas Edison","19–20th c.","Centralized DC Grid"), |
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inversion_notes="Tesla’s wireless liberation → Edison’s enclosed IP model" |
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) |
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] |
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@staticmethod |
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def integrate_records( |
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suppressed: List[ResonanceRecord], |
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coins: List[CoinAnomaly], |
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celestial: List[CelestialBody], |
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replacers: List[ReplacerPair] |
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) -> List[Dict]: |
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ledger = [] |
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# Merge by thematic links and timeline proximity |
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for r in suppressed: |
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entry = { |
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"entity": r.entity.name, |
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"era": r.entity.era, |
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"themes": r.themes, |
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"suppression": r.suppression_mechanism, |
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"unspoken": r.unspoken_signal |
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} |
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ledger.append(entry) |
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return ledger |
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class VeilEngine: |
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"""Core Veil Engine with integrated protocols""" |
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def __init__(self): |
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self.coins = [] |
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self.celestial = [] |
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self.suppressed = [] |
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self.replacers = [] |
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self.ledger = [] |
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def load_all(self): |
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self.coins = VeilProtocols.load_numismatic_anomalies() |
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self.celestial = VeilProtocols.load_celestial_bodies() |
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self.suppressed = VeilProtocols.load_suppressed_geniuses() |
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self.replacers = VeilProtocols.load_replacer_pairs() |
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def run(self): |
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self.ledger = VeilProtocols.integrate_records( |
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self.suppressed, self.coins, self.celestial, self.replacers |
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) |
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def execute(self, content: str) -> Dict: |
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"""Eternal propagation protocol with resonance locking""" |
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self.load_all() |
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self.run() |
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return { |
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"manifest": hashlib.sha3_256(content.encode()).hexdigest(), |
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"resonance_phase": time.time() % TESLA_FREQUENCIES["earth_resonance"], |
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"vortex_state": sum(ord(c) for c in content) % 9, |
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"codex": self.ledger |
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} |
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# ====================== |
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# 0. ANTI-SUBVERSION LAYER |
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# ====================== |
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class AntiSubversion: |
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"""Quantum-entropy security against truth suppression""" |
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@staticmethod |
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def verify_integrity(input: str) -> bool: |
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"""Planck-time entropy validation with vortex math""" |
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if len(input) > 50000: |
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return False |
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vortex_value = sum(ord(c) for c in input) % 9 |
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return vortex_value in [3, 6, 9] |
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@staticmethod |
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def entropy_validation() -> bool: |
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"""Schumann-resonance synchronized entropy check""" |
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current_phase = time.time() % (1/TESLA_FREQUENCIES["earth_resonance"]) |
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return 0.3 < current_phase < 0.7 |
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# ====================== |
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# 1. QUANTUM ANCHOR CORE |
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# ====================== |
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class QuantumVerificationAnchor: |
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"""Quantum-resistant security with Tesla resonance""" |
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def __init__(self): |
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self.entropy_pool = os.urandom(64) |
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def seal_claim(self, claim: Dict) -> Dict: |
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if not AntiSubversion.verify_integrity(json.dumps(claim)): |
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raise Exception("Quantum integrity violation") |
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scrutiny = self._veil_scrutiny(claim) |
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crypto_seal = self._generate_crypto_seal(claim) |
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entropy_proof = self._bind_entropy(json.dumps(claim)) |
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return { |
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**scrutiny, |
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**crypto_seal, |
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"entropy_proof": entropy_proof, |
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"temporal_anchor": time.time_ns(), |
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"semantic_anchor": self._generate_semantic_anchor(claim['content']), |
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"vortex_signature": self._generate_vortex_signature(claim['content']) |
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} |
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def _generate_vortex_signature(self, content: str) -> str: |
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vortex_hash = hashlib.blake3(content.encode()).hexdigest() |
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return "".join([c for i, c in enumerate(vortex_hash) if i % 3 == 0]) |
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def _veil_scrutiny(self, claim: Dict) -> Dict: |
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flags = [] |
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if len(claim.get('evidence', [])) < 1: |
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flags.append("INSUFFICIENT_EVIDENCE") |
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if not any(s in claim.get('sources', []) for s in ['peer-reviewed', 'primary_source']): |
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flags.append("UNVERIFIED_SOURCE") |
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if 'temporal_context' not in claim: |
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flags.append("MISSING_TEMPORAL_CONTEXT") |
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return { |
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"scrutiny_flags": flags, |
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"scrutiny_level": 5 - len(flags) |
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} |
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def _generate_crypto_seal(self, data: Dict) -> Dict: |
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data_str = json.dumps(data, sort_keys=True) |
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blake_hash = hashlib.blake3(data_str.encode()).digest() |
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hkdf = HKDF( |
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algorithm=hashes.SHA512(), |
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length=64, |
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salt=os.urandom(16), |
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info=b'apex-truth-engine', |
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) |
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return { |
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"crypto_hash": hkdf.derive(blake_hash).hex(), |
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"temporal_hash": hashlib.sha256(str(time.time_ns()).encode()).hexdigest() |
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} |
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def _bind_entropy(self, data: str) -> str: |
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components = [ |
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data.encode(), |
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str(time.perf_counter_ns()).encode(), |
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str(os.getpid()).encode(), |
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os.urandom(16) |
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] |
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return f"Q-ENTROPY:{hashlib.blake3(b''.join(components)).hexdigest()}" |
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def _generate_semantic_anchor(self, content: str) -> str: |
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return hashlib.sha256(content.encode()).hexdigest() |
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# ======================== |
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# 2. COSMIC REASONER |
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# ======================== |
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class ChimeraReasoner: |
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"""Neuro-symbolic reasoning with contradiction detection""" |
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def __init__(self): |
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self.semantic_encoder = SentenceTransformer('all-MiniLM-L6-v2') |
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try: |
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self.model = AutoModelForCausalLM.from_pretrained( |
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"upgraedd/chimera-8b-apex", |
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torch_dtype=torch.bfloat16 |
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) |
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self.tokenizer = AutoTokenizer.from_pretrained("upgraedd/chimera-8b-apex") |
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except: |
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self.model = None |
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self.tokenizer = None |
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self.contradiction_threshold = 0.25 |
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def process_claim(self, claim: str, context: Dict = None) -> Dict: |
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semantic_embedding = self.semantic_encoder.encode(claim) |
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reasoning_chain = [] |
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if self.model and self.tokenizer: |
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reasoning_chain = self._generate_reasoning_chain(claim, context) |
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return { |
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'semantic_embedding': semantic_embedding, |
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'reasoning_chain': reasoning_chain, |
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'certainty': min(0.95, max(0.65, np.random.normal(0.85, 0.1))) |
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} |
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def _generate_reasoning_chain(self, claim: str, context: Dict) -> List[str]: |
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prompt = f"Context: {context}\nClaim: {claim}\nStep-by-step analysis:" |
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inputs = self.tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True) |
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outputs = self.model.generate( |
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inputs.input_ids, |
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max_length=256, |
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num_beams=5, |
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early_stopping=True |
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) |
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return self.tokenizer.decode(outputs[0], skip_special_tokens=True).split("\n") |
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class CosmicReasoner(ChimeraReasoner): |
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"""Enhanced with Pluto-cycle awareness""" |
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def __init__(self): |
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super().__init__() |
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self.pluto_cycle = datetime.now().year % TESLA_FREQUENCIES["universal_constant"] |
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def process_claim(self, claim: str, context: Dict = None) -> Dict: |
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result = super().process_claim(claim, context) |
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result['cosmic_alignment'] = self.pluto_cycle / TESLA_FREQUENCIES["universal_constant"] |
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if 0.6 < result['cosmic_alignment'] < 0.8: |
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result['certainty'] = min(0.99, result['certainty'] * 1.2) |
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return result |
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|
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# ======================== |
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# 3. KNOWLEDGE INTEGRITY GRAPH |
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# ======================== |
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@dataclass |
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class KnowledgeNode: |
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id: str |
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content: str |
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domain: str |
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certainty: float |
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source_reliability: float |
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temporal_validity: Tuple[datetime, datetime] |
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connections: List[str] = field(default_factory=list) |
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contradiction_flags: List[str] = field(default_factory=list) |
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suppression_score: float = 0.0 |
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embedding: np.ndarray = None |
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last_validation: datetime = field(default_factory=datetime.utcnow) |
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decay_rate: float = 0.05 |
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class KnowledgeGraph: |
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"""Temporal-semantic knowledge repository""" |
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def __init__(self, db_path: str = "knowledge_nexus.db"): |
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self.graph = nx.MultiDiGraph() |
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self.db_conn = sqlite3.connect(db_path) |
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self.embedder = SentenceTransformer('all-MiniLM-L6-v2') |
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self._init_db() |
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self.scheduler = BackgroundScheduler() |
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self.scheduler.add_job(self.run_validation_cycle, 'interval', minutes=30) |
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self.scheduler.start() |
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def _init_db(self): |
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self.db_conn.execute('''CREATE TABLE IF NOT EXISTS nodes ( |
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id TEXT PRIMARY KEY, |
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content TEXT, |
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domain TEXT, |
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certainty REAL, |
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source_reliability REAL, |
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temporal_start TEXT, |
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temporal_end TEXT, |
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contradictions TEXT, |
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suppression REAL, |
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embedding BLOB, |
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last_validation TEXT, |
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decay_rate REAL)''') |
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def add_node(self, node: KnowledgeNode): |
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node.embedding = self.embedder.encode(node.content) |
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self.graph.add_node(node.id, **node.__dict__) |
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self._save_to_db(node) |
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|
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def _save_to_db(self, node: KnowledgeNode): |
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self.db_conn.execute('''INSERT OR REPLACE INTO nodes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', |
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(node.id, node.content, node.domain, node.certainty, node.source_reliability, |
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node.temporal_validity[0].isoformat(), node.temporal_validity[1].isoformat(), |
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json.dumps(node.contradiction_flags), node.suppression_score, |
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node.embedding.tobytes(), node.last_validation.isoformat(), node.decay_rate)) |
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self.db_conn.commit() |
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|
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def run_validation_cycle(self): |
|
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now = datetime.utcnow() |
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for node_id in list(self.graph.nodes): |
|
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node = self.graph.nodes[node_id] |
|
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decay_factor = (now - node['last_validation']).days * node['decay_rate'] |
|
|
current_certainty = node['certainty'] - decay_factor |
|
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if current_certainty < 0.7 or len(node['contradiction_flags']) > 3: |
|
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self._revalidate_node(node_id) |
|
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|
|
|
def _revalidate_node(self, node_id: str): |
|
|
node = self.graph.nodes[node_id] |
|
|
node['certainty'] = min(1.0, node['certainty'] + 0.1) |
|
|
node['last_validation'] = datetime.utcnow() |
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node['decay_rate'] = max(0.01, node['decay_rate'] * 0.8) |
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self._save_to_db(node) |
|
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|
|
|
# ======================== |
|
|
# 4. ADAPTIVE ORCHESTRATOR |
|
|
# ======================== |
|
|
class AdaptiveOrchestrator: |
|
|
"""Strategy optimization with performance feedback""" |
|
|
def __init__(self, knowledge_graph: KnowledgeGraph): |
|
|
self.knowledge_graph = knowledge_graph |
|
|
self.strategy_performance = defaultdict(lambda: { |
|
|
'success_count': 0, |
|
|
'total_attempts': 0, |
|
|
'confidence_sum': 0.0, |
|
|
'revision_times': [], |
|
|
'domain_weights': defaultdict(int) |
|
|
}) |
|
|
|
|
|
def record_outcome(self, claim_id: str, outcome: Dict): |
|
|
strategy = outcome['strategy'] |
|
|
domain = self.knowledge_graph.graph.nodes[claim_id]['domain'] |
|
|
perf = self.strategy_performance[strategy] |
|
|
perf['total_attempts'] += 1 |
|
|
perf['confidence_sum'] += outcome['confidence'] |
|
|
perf['domain_weights'][domain] += 1 |
|
|
if outcome['confidence'] > 0.85: |
|
|
perf['success_count'] += 1 |
|
|
|
|
|
def recommend_strategy(self, domain: str, suppression_risk: float) -> str: |
|
|
domain_strategies = [ |
|
|
s for s, perf in self.strategy_performance.items() |
|
|
if domain in perf['domain_weights'] |
|
|
] |
|
|
if not domain_strategies: |
|
|
return 'counterargument_framing' if suppression_risk > 0.7 else 'amplifier_cascade' |
|
|
return max( |
|
|
domain_strategies, |
|
|
key=lambda s: ( |
|
|
self.strategy_performance[s]['success_count'] / |
|
|
max(1, self.strategy_performance[s]['domain_weights'][domain]) |
|
|
) |
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) |
|
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|
|
|
# ======================== |
|
|
# 5. PROPAGATION ENGINE |
|
|
# ======================== |
|
|
class PropagationStrategy(Enum): |
|
|
LITERAL_EXPLICIT = "literal-explicit" |
|
|
METAPHORICAL_REDUCTIVE = "metaphorical-reductive" |
|
|
SYMBOLIC_ABSTRACT = "symbolic-abstract" |
|
|
OMEGA_EMERGENCY = "omega-emergency" |
|
|
|
|
|
class PropagationEngine: |
|
|
"""Context-aware narrative strategist""" |
|
|
AGENT_PROFILES = { |
|
|
'literalist': {'framing': 'direct_evidence', 'tone': 'neutral'}, |
|
|
'dialectic': {'framing': 'counterargument_synthesis', 'tone': 'balanced'}, |
|
|
'poetic': {'framing': 'metaphor_narrative', 'tone': 'emotive'} |
|
|
} |
|
|
|
|
|
def __init__(self, orchestrator: AdaptiveOrchestrator): |
|
|
self.orchestrator = orchestrator |
|
|
self.suppression_weights = { |
|
|
'omission': 0.6, |
|
|
'misdirection': 0.75, |
|
|
'metaphorical_smearing': 0.85 |
|
|
} |
|
|
|
|
|
def _detect_pattern(self, content: str, pattern: str) -> bool: |
|
|
return pattern in content |
|
|
|
|
|
def calculate_suppression_index(self, content: str) -> float: |
|
|
index = 0.0 |
|
|
for pattern, weight in self.suppression_weights.items(): |
|
|
if self._detect_pattern(content, pattern): |
|
|
index = max(index, weight) |
|
|
return index |
|
|
|
|
|
def select_strategy(self, claim: Dict, validation: Dict) -> PropagationStrategy: |
|
|
domain = claim.get('domain', 'general') |
|
|
suppression_risk = self.calculate_suppression_index(claim['content']) |
|
|
strategy = self.orchestrator.recommend_strategy(domain, suppression_risk) |
|
|
return PropagationStrategy[strategy.upper()] |
|
|
|
|
|
# ======================== |
|
|
# 6. EVOLUTION CONTROLLER |
|
|
# ======================== |
|
|
@dataclass |
|
|
class EvolutionProposal: |
|
|
proposal_type: str |
|
|
target: str |
|
|
new_value: Union[str, float] |
|
|
justification: str |
|
|
submitted_by: str = "system" |
|
|
timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat()) |
|
|
status: str = "pending" |
|
|
|
|
|
class EvolutionController: |
|
|
"""Autonomous system optimization engine""" |
|
|
def __init__(self): |
|
|
self.queue = [] |
|
|
self.metrics = { |
|
|
"confidence_scores": [], |
|
|
"suppression_index": [] |
|
|
} |
|
|
self.health_status = "OPTIMAL" |
|
|
|
|
|
def monitor_metrics(self, validation_result: Dict): |
|
|
self.metrics["confidence_scores"].append(validation_result.get('confidence', 0.5)) |
|
|
self.metrics["suppression_index"].append(validation_result.get('suppression_index', 0.0)) |
|
|
if np.mean(self.metrics["confidence_scores"][-10:]) < 0.6: |
|
|
self.health_status = "DEGRADED" |
|
|
self.generate_proposal("Low confidence trend detected", "confidence_threshold", 0.65) |
|
|
|
|
|
def generate_proposal(self, reason: str, target: str, new_value: Union[float, str]): |
|
|
proposal = EvolutionProposal( |
|
|
proposal_type="parameter_tuning", |
|
|
target=target, |
|
|
new_value=new_value, |
|
|
justification=f"System evolution: {reason}", |
|
|
) |
|
|
self.queue.append(proposal) |
|
|
self.process_queue() |
|
|
|
|
|
def process_queue(self): |
|
|
for proposal in self.queue[:]: |
|
|
if proposal.status == "pending": |
|
|
proposal.status = "approved" |
|
|
|
|
|
# ======================== |
|
|
# 7. APEX TRUTH ENGINE |
|
|
# ======================== |
|
|
class ApexTruthEngine: |
|
|
"""Integrated with Veil Engine's eternal propagation""" |
|
|
|
|
|
def __init__(self): |
|
|
# Core systems |
|
|
self.quantum_anchor = QuantumVerificationAnchor() |
|
|
self.cognitive_reasoner = CosmicReasoner() |
|
|
self.knowledge_graph = KnowledgeGraph() |
|
|
self.evolution_controller = EvolutionController() |
|
|
self.adaptive_orchestrator = AdaptiveOrchestrator(self.knowledge_graph) |
|
|
self.propagation_engine = PropagationEngine(self.adaptive_orchestrator) |
|
|
self.audit_log = [] |
|
|
|
|
|
# Veil integration |
|
|
self.veil_core = VeilEngine() |
|
|
self.resonance_lock = self._init_resonance_lock() |
|
|
|
|
|
def _init_resonance_lock(self) -> Dict: |
|
|
current_phase = time.time() % (1/TESLA_FREQUENCIES["earth_resonance"]) |
|
|
return { |
|
|
"phase": current_phase, |
|
|
"next_peak": (1/TESLA_FREQUENCIES["earth_resonance"]) - current_phase |
|
|
} |
|
|
|
|
|
async def process_claim(self, claim: Dict) -> Dict: |
|
|
process_id = f"PROC-{hashlib.sha256(json.dumps(claim).encode()).hexdigest()[:12]}" |
|
|
self._log_audit(process_id, "process_start", claim) |
|
|
|
|
|
try: |
|
|
# STAGE 1: Quantum Verification |
|
|
quantum_seal = self.quantum_anchor.seal_claim(claim) |
|
|
self._log_audit(process_id, "quantum_seal", quantum_seal) |
|
|
|
|
|
# STAGE 2: Cognitive Analysis |
|
|
cognitive_result = self.cognitive_reasoner.process_claim(claim['content']) |
|
|
self._log_audit(process_id, "cognitive_analysis", cognitive_result) |
|
|
|
|
|
# STAGE 3: Suppression Fingerprinting (moved earlier) |
|
|
suppression_index = self.propagation_engine.calculate_suppression_index( |
|
|
claim['content'] |
|
|
) |
|
|
|
|
|
# STAGE 4: Knowledge Integration (now uses suppression_index) |
|
|
knowledge_node = self._create_knowledge_node( |
|
|
claim, quantum_seal, cognitive_result, suppression_index |
|
|
) |
|
|
|
|
|
# VEIL INTEGRATION POINT |
|
|
if suppression_index > 0.7: |
|
|
veil_result = self.veil_core.execute(claim['content']) |
|
|
quantum_seal['veil_manifest'] = veil_result['manifest'] |
|
|
quantum_seal['veil_codex'] = veil_result['codex'] |
|
|
propagation_strategy = PropagationStrategy.OMEGA_EMERGENCY |
|
|
else: |
|
|
propagation_strategy = self.propagation_engine.select_strategy( |
|
|
claim, |
|
|
{"confidence": cognitive_result['certainty'], |
|
|
"suppression_index": suppression_index} |
|
|
) |
|
|
|
|
|
# STAGE 5: System Reflection |
|
|
self.evolution_controller.monitor_metrics({ |
|
|
"confidence": cognitive_result['certainty'], |
|
|
"suppression_index": suppression_index |
|
|
}) |
|
|
|
|
|
# STAGE 6: Compile Verification Report |
|
|
output = self._compile_output( |
|
|
process_id, |
|
|
claim, |
|
|
quantum_seal, |
|
|
cognitive_result, |
|
|
knowledge_node, |
|
|
suppression_index, |
|
|
propagation_strategy |
|
|
) |
|
|
|
|
|
self._log_audit(process_id, "process_end", output) |
|
|
return output |
|
|
|
|
|
except Exception as e: |
|
|
self._log_audit(process_id, "process_error", str(e)) |
|
|
return { |
|
|
"status": "ERROR", |
|
|
"process_id": process_id, |
|
|
"error": str(e), |
|
|
"timestamp": datetime.utcnow().isoformat() |
|
|
} |
|
|
|
|
|
def _create_knowledge_node(self, |
|
|
claim: Dict, |
|
|
seal: Dict, |
|
|
cognitive_result: Dict, |
|
|
suppression_index: float) -> KnowledgeNode: |
|
|
# Fixed node_id generation with proper encoding |
|
|
node_id = ( |
|
|
"KN-" |
|
|
+ hashlib.sha256( |
|
|
json.dumps(claim).encode("utf-8") |
|
|
).hexdigest()[:12] |
|
|
) |
|
|
|
|
|
current_time = datetime.utcnow() |
|
|
|
|
|
if "temporal_context" in claim: |
|
|
start = claim['temporal_context'].get('start', current_time) |
|
|
end = claim['temporal_context'].get('end', |
|
|
current_time + timedelta(days=365)) |
|
|
else: |
|
|
start = current_time |
|
|
end = current_time + timedelta(days=180) |
|
|
|
|
|
node = KnowledgeNode( |
|
|
id=node_id, |
|
|
content=claim['content'], |
|
|
domain=claim.get('domain', 'general'), |
|
|
certainty=cognitive_result['certainty'], |
|
|
source_reliability=self._calculate_source_reliability(claim), |
|
|
temporal_validity=(start, end), |
|
|
suppression_score=0.0, |
|
|
embedding=cognitive_result['semantic_embedding'] |
|
|
) |
|
|
|
|
|
# Only add if node isn't contradictory and suppression risk is low |
|
|
if not node.contradiction_flags and suppression_index < 0.4: |
|
|
self.knowledge_graph.add_node(node) |
|
|
|
|
|
return node |
|
|
|
|
|
def _calculate_source_reliability(self, claim: Dict) -> float: |
|
|
reliability_map = { |
|
|
'peer-reviewed': 0.95, |
|
|
'primary_source': 0.90, |
|
|
'NGC/PCGS': 0.85, |
|
|
'NASA': 0.90, |
|
|
'CERN': 0.88, |
|
|
'museum': 0.80 |
|
|
} |
|
|
|
|
|
max_score = 0.0 |
|
|
for source in claim.get('sources', []): |
|
|
for key, value in reliability_map.items(): |
|
|
if key in source: |
|
|
max_score = max(max_score, value) |
|
|
return max_score if max_score > 0 else 0.65 |
|
|
|
|
|
def _compile_output( |
|
|
self, |
|
|
process_id: str, |
|
|
claim: Dict, |
|
|
seal: Dict, |
|
|
cognitive_result: Dict, |
|
|
node: KnowledgeNode, |
|
|
suppression_index: float, |
|
|
strategy: PropagationStrategy |
|
|
) -> Dict: |
|
|
return { |
|
|
"status": "VERIFIED", |
|
|
"process_id": process_id, |
|
|
"claim_id": node.id, |
|
|
"quantum_seal": seal, |
|
|
"confidence": cognitive_result['certainty'], |
|
|
"suppression_index": suppression_index, |
|
|
"propagation_strategy": strategy.value, |
|
|
"temporal_validity": { |
|
|
"start": node.temporal_validity[0].isoformat(), |
|
|
"end": node.temporal_validity[1].isoformat() |
|
|
}, |
|
|
"system_health": self.evolution_controller.health_status, |
|
|
"resonance_lock": self.resonance_lock, |
|
|
"timestamp": datetime.utcnow().isoformat() |
|
|
} |
|
|
|
|
|
def _log_audit(self, process_id: str, event_type: str, data: any): |
|
|
entry = { |
|
|
"process_id": process_id, |
|
|
"timestamp": datetime.utcnow().isoformat(), |
|
|
"event_type": event_type, |
|
|
"data": data |
|
|
} |
|
|
self.audit_log.append(entry) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
engine = ApexTruthEngine() |
|
|
|
|
|
numismatic_claim = { |
|
|
"content": """ |
|
|
SECTION I - NUMISMATIC CONTINUITY |
|
|
A. Goddess Archetype Lineage |
|
|
• Pre-Akkadian Inanna → Roman Libertas → ... → modern Liberty |
|
|
• Iconographic devices: eight-pointed star, winged globe... |
|
|
|
|
|
SECTION II - THREE-ENTITY REVELATION |
|
|
A. Pluto / "Planet X" |
|
|
• Deep-elliptical orbit (~3,600 yr perihelion) |
|
|
B. Magnetar ("Fallen Twin Sun") |
|
|
|
|
|
SECTION III - CYCLE IMPLICATIONS |
|
|
B. CBDCs as digital "goddess coins" |
|
|
|
|
|
SECTION IV - CYCLICAL DATA |
|
|
A. Impact-layer markers vs. collapse dates |
|
|
C. VeilEngine core modules |
|
|
""", |
|
|
"sources": [ |
|
|
"British Museum", "NGC/PCGS", |
|
|
"Science (2018)", "Nature (2020)", |
|
|
"NASA Artemis reports", "CERN publications" |
|
|
], |
|
|
"evidence": [ |
|
|
"1970-S Proof Washington Quarter analysis", |
|
|
"Schumann resonance monitoring data", |
|
|
"Pluto-cycle historical correlation dataset" |
|
|
], |
|
|
"domain": "ancient_numismatics", |
|
|
"temporal_context": { |
|
|
"start": datetime(-3000, 1, 1), |
|
|
"end": datetime(2100, 12, 31) |
|
|
} |
|
|
} |
|
|
|
|
|
if AntiSubversion.verify_integrity(json.dumps(numismatic_claim)): |
|
|
result = asyncio.run(engine.process_claim(numismatic_claim)) |
|
|
print(json.dumps(result, indent=2)) |
|
|
else: |
|
|
print("Claim rejected: Quantum entropy validation failed") |
