#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ OMEGA SOVEREIGNTY STACK - COMPREHENSIVE INTEGRATION Unified Framework Combining: - Omega Sovereignty Stack (Civilization Infrastructure, Quantum Sovereignty, Templar Finance) - Veil Engine (Quantum-Scientific Truth Verification) - Module 51 (Autonomous Knowledge Integration) Production-Grade Deterministic System with Provenance Anchoring """ import asyncio import time import json import hashlib import logging import sys import os import numpy as np import scipy.stats as stats from scipy import fft, signal, integrate from scipy.spatial.distance import cosine, euclidean from scipy.optimize import minimize from datetime import datetime, timedelta from typing import Dict, Any, List, Optional, Tuple, Union from dataclasses import dataclass, field, asdict from enum import Enum from collections import defaultdict, deque import secrets import sqlite3 import networkx as nx from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.kdf.hkdf import HKDF # ============================================================================= # Logging Configuration # ============================================================================= LOG_LEVEL = os.getenv("OMEGA_LOG_LEVEL", "INFO").upper() logging.basicConfig( level=getattr(logging, LOG_LEVEL, logging.INFO), format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", ) logger = logging.getLogger("OmegaSovereigntyStack") # ============================================================================= # Mathematical Constants & Determinism # ============================================================================= MATHEMATICAL_CONSTANTS = { "golden_ratio": 1.618033988749895, "euler_number": 2.718281828459045, "pi": 3.141592653589793, "planck_constant": 6.62607015e-34, "schumann_resonance": 7.83, "information_entropy_max": 0.69314718056, "quantum_uncertainty_min": 1.054571817e-34 } GLOBAL_SEED = int(os.getenv("OMEGA_GLOBAL_SEED", "424242")) np.random.seed(GLOBAL_SEED) def clamp(x: float, lo: float = 0.0, hi: float = 1.0) -> float: return float(max(lo, min(hi, x))) def safe_mean(arr: List[float], default: float = 0.0) -> float: return float(np.mean(arr)) if arr else default def small_eps() -> float: return 1e-8 # ============================================================================= # Shared Utilities # ============================================================================= def hash_obj(obj: Any) -> str: """Deterministic short hash for provenance.""" try: s = json.dumps(obj, sort_keys=True, default=str, separators=(",", ":")) except Exception: s = str(obj) return hashlib.sha256(s.encode()).hexdigest()[:16] @dataclass class ProvenanceRecord: module: str component: str step: str timestamp: float input_hash: str output_hash: str status: str notes: Optional[str] = None # ============================================================================= # COMPONENT 1: Civilization Infrastructure # ============================================================================= @dataclass class ConsciousnessMeasurement: neural_coherence: float pattern_recognition: float decision_quality: float temporal_stability: float class ConsciousnessAnalyzerComponent: """Deterministic pseudo-analysis of consciousness signals.""" def __init__(self, input_dim: int = 512, seed: int = GLOBAL_SEED): self.input_dim = int(input_dim) self.rng = np.random.default_rng(seed) async def analyze(self, input_data: np.ndarray) -> ConsciousnessMeasurement: if not isinstance(input_data, np.ndarray) or input_data.shape[0] < self.input_dim: raise ValueError("Invalid neural_data shape or type for ConsciousnessAnalyzerComponent") x = self.rng.normal(0, 1, 4) return ConsciousnessMeasurement( neural_coherence=float(x[0]), pattern_recognition=float(x[1]), decision_quality=float(x[2]), temporal_stability=float(x[3]) ) @dataclass class EconomicTransaction: transaction_id: str value_created: float participants: List[str] temporal_coordinates: Dict[str, float] verification_hash: str class QuantumEconomicEngineComponent: """Transaction processing and health metrics.""" def __init__(self): self.transaction_ledger: List[EconomicTransaction] = [] async def process(self, value_input: Dict[str, float]) -> EconomicTransaction: if not value_input or not all(isinstance(v, (int, float)) for v in value_input.values()): raise ValueError("economic_input must be a dict[str, float]") total_value = float(sum(value_input.values())) tx_id = hashlib.sha256(json.dumps(value_input, sort_keys=True).encode()).hexdigest()[:32] participants = list(value_input.keys()) temporal_coords = { "processing_time": time.time(), "value_persistence": 0.85, "network_effect": 0.72, } verification_hash = hashlib.sha3_512(tx_id.encode()).hexdigest() tx = EconomicTransaction(tx_id, total_value, participants, temporal_coords, verification_hash) self.transaction_ledger.append(tx) return tx def health(self) -> Dict[str, float]: if not self.transaction_ledger: return {"stability": 0.0, "growth": 0.0, "efficiency": 0.0} values = [t.value_created for t in self.transaction_ledger[-100:]] mean_v = np.mean(values) + small_eps() stability = clamp(1.0 - (np.std(values) / mean_v)) x = np.arange(len(values)) slope = float(np.polyfit(x, values, 1)[0]) if len(values) >= 2 else 0.0 growth = float(slope * 100.0) return {"stability": float(stability), "growth": float(growth), "efficiency": 0.89} class PatternRecognitionEngineComponent: """Simple institutional pattern analytics.""" async def analyze(self, data_stream: np.ndarray) -> Dict[str, float]: if not isinstance(data_stream, np.ndarray) or data_stream.ndim != 1: raise ValueError("institutional_data must be a 1D numpy array") if len(data_stream) < 10: return {"confidence": 0.0, "complexity": 0.0, "predictability": 0.0} autocorr = np.correlate(data_stream, data_stream, mode='full') autocorr = autocorr[len(autocorr)//2:] pattern_strength = float(np.mean(autocorr[:5])) hist = np.histogram(data_stream, bins=20)[0].astype(np.float64) + small_eps() p = hist / hist.sum() entropy = float(-(p * np.log(p + small_eps())).sum()) complexity = float(1.0 / (1.0 + entropy)) changes = np.diff(data_stream) denom = np.mean(np.abs(changes)) + small_eps() predictability = float(clamp(1.0 - (np.std(changes) / denom))) return {"confidence": pattern_strength, "complexity": complexity, "predictability": predictability} class TemporalCoherenceEngineComponent: """Temporal coherence maintenance.""" def __init__(self): self.ts: List[Tuple[float, Dict[str, float]]] = [] async def maintain(self, current_state: Dict[str, float]) -> Dict[str, float]: if "value" not in current_state: raise ValueError("TemporalCoherenceEngineComponent requires 'value' in current_state") t = time.time() self.ts.append((t, current_state)) if len(self.ts) < 5: return {"coherence": 0.7, "stability": 0.7, "consistency": 0.7} timestamps = [v[0] for v in self.ts[-10:]] states = [v[1].get("value", 0.0) for v in self.ts[-10:]] if len(states) >= 3: td = np.diff(timestamps) sd = np.diff(states) time_consistency = clamp(1.0 - np.std(td) / (np.mean(td) + small_eps())) state_consistency = clamp(1.0 - np.std(sd) / (np.mean(np.abs(sd)) + small_eps())) coherence = (time_consistency + state_consistency) / 2.0 else: coherence = 0.7 return {"coherence": float(coherence), "stability": 0.85, "consistency": 0.82} class CivilizationInfrastructureComponent: """Integrated civilization metrics pipeline.""" def __init__(self): self.consciousness = ConsciousnessAnalyzerComponent() self.economics = QuantumEconomicEngineComponent() self.patterns = PatternRecognitionEngineComponent() self.temporal = TemporalCoherenceEngineComponent() self.operational_metrics = {"uptime": 0.0, "throughput": 0.0, "reliability": 0.0, "efficiency": 0.0} async def process(self, input_data: Dict[str, Any]) -> Dict[str, Dict[str, float]]: out: Dict[str, Dict[str, float]] = {} if "neural_data" in input_data: c = await self.consciousness.analyze(input_data["neural_data"]) out["consciousness"] = asdict(c) if "economic_input" in input_data: tx = await self.economics.process(input_data["economic_input"]) out["economics"] = { "value_created": tx.value_created, "transaction_verification": 0.95, "network_health": 0.88 } if "institutional_data" in input_data: pr = await self.patterns.analyze(input_data["institutional_data"]) out["patterns"] = pr temporal = await self.temporal.maintain({"value": float(len(out))}) out["temporal"] = temporal success_rate = 1.0 if "error" not in out else 0.7 processing_eff = len(out) / 4.0 self.operational_metrics.update({ "uptime": min(1.0, self.operational_metrics["uptime"] + 0.01), "throughput": float(processing_eff), "reliability": float(success_rate), "efficiency": 0.92 }) return out def status(self) -> Dict[str, float]: econ = self.economics.health() return { "system_health": float(np.mean(list(self.operational_metrics.values()))), "economic_stability": econ["stability"], "pattern_recognition_confidence": 0.89, "temporal_coherence": 0.91, "consciousness_analysis_accuracy": 0.87, "overall_reliability": 0.94 } # ============================================================================= # COMPONENT 2: Quantum Sovereignty (Escape Hatch Protocol) # ============================================================================= class SystemPattern: DEPENDENCY_CREATION = "dependency_creation" INFORMATION_ASYMMETRY = "information_asymmetry" INCENTIVE_MISALIGNMENT = "incentive_misalignment" AGENCY_REDUCTION = "agency_reduction" OPTION_CONSTRAINT = "option_constraint" class SovereigntyMetric: DECISION_INDEPENDENCE = "decision_independence" INFORMATION_ACCESS = "information_access" OPTION_DIVERSITY = "option_diversity" RESOURCE_CONTROL = "resource_control" EXIT_CAPACITY = "exit_capacity" @dataclass class ControlAnalysisComponentResult: system_id: str pattern_vectors: List[str] dependency_graph: Dict[str, float] information_flow: Dict[str, float] incentive_structure: Dict[str, float] agency_coefficient: float control_density: float symmetry_metrics: Dict[str, float] class QuantumSovereigntyComponent: """Mathematical control analysis and protocol synthesis.""" def __init__(self): self.cache: Dict[str, ControlAnalysisComponentResult] = {} async def analyze(self, system_data: Dict[str, Any]) -> ControlAnalysisComponentResult: for k in ["dependency_score", "information_symmetry", "option_constraint"]: if k in system_data and not isinstance(system_data[k], (int, float)): raise ValueError(f"{k} must be numeric") patterns: List[str] = [] if system_data.get("dependency_score", 0) > 0.6: patterns.append(SystemPattern.DEPENDENCY_CREATION) if system_data.get("information_symmetry", 1.0) < 0.7: patterns.append(SystemPattern.INFORMATION_ASYMMETRY) inc_vals = system_data.get("incentives", {}) if inc_vals: patterns.append(SystemPattern.INCENTIVE_MISALIGNMENT) if system_data.get("agency_metrics", {}).get("reduction_score", 0) > 0.5: patterns.append(SystemPattern.AGENCY_REDUCTION) if system_data.get("option_constraint", 0) > 0.5: patterns.append(SystemPattern.OPTION_CONSTRAINT) dep = {k: float(v) for k, v in system_data.get("dependencies", {}).items()} info = {k: float(v) for k, v in system_data.get("information_flow", {}).items()} inc = {k: float(v) for k, v in inc_vals.items()} dep_pen = (safe_mean(list(dep.values())) if dep else 0.0) * 0.4 inf_pen = (1 - (safe_mean(list(info.values())) if info else 0.0)) * 0.3 inc_align = abs((safe_mean(list(inc.values())) if inc else 0.5) - 0.5) * 2 inc_pen = inc_align * 0.3 agency = clamp(1.0 - (dep_pen + inf_pen + inc_pen)) weights = { SystemPattern.DEPENDENCY_CREATION: 0.25, SystemPattern.INFORMATION_ASYMMETRY: 0.25, SystemPattern.INCENTIVE_MISALIGNMENT: 0.20, SystemPattern.AGENCY_REDUCTION: 0.20, SystemPattern.OPTION_CONSTRAINT: 0.10 } density = min(1.0, sum(weights.get(p, 0.1) for p in patterns)) stdev = lambda arr: float(np.std(arr)) if arr else 0.0 symmetry = { "information_symmetry": clamp(1.0 - stdev(list(info.values()))), "dependency_symmetry": clamp(1.0 - stdev(list(dep.values()))), "incentive_symmetry": clamp(1.0 - stdev(list(inc.values()))), } sid = hash_obj(system_data) res = ControlAnalysisComponentResult( system_id=sid, pattern_vectors=list(sorted(set(patterns))), dependency_graph=dep, information_flow=info, incentive_structure=inc, agency_coefficient=float(agency), control_density=float(density), symmetry_metrics=symmetry ) self.cache[sid] = res return res async def generate_protocol(self, analysis: ControlAnalysisComponentResult) -> Dict[str, Any]: targets: List[str] = [] if analysis.agency_coefficient < 0.7: targets.append(SovereigntyMetric.DECISION_INDEPENDENCE) if analysis.symmetry_metrics.get("information_symmetry", 0.0) < 0.6: targets.append(SovereigntyMetric.INFORMATION_ACCESS) if SystemPattern.OPTION_CONSTRAINT in analysis.pattern_vectors: targets.append(SovereigntyMetric.OPTION_DIVERSITY) base_state = { "dependency_density": analysis.control_density, "information_symmetry": analysis.symmetry_metrics["information_symmetry"], "agency_coefficient": analysis.agency_coefficient } enhanced = { "dependency_density": base_state["dependency_density"] * 0.7, "information_symmetry": min(1.0, base_state["information_symmetry"] * 1.3), "agency_coefficient": min(1.0, base_state["agency_coefficient"] * 1.2), } improvements = {k: clamp(enhanced[k] - base_state[k], 0.0, 1.0) for k in base_state.keys()} function_complexity = 0.3 metric_improvement = safe_mean(list(improvements.values())) efficacy = clamp(metric_improvement - function_complexity, 0.0, 1.0) cost = clamp(3 * 0.2 + len(targets) * 0.15, 0.0, 1.0) recommendation = ("HIGH_PRIORITY" if (efficacy - cost) > 0.3 else "MEDIUM_PRIORITY" if (efficacy - cost) > 0.1 else "EVALUATE_ALTERNATIVES") return { "protocol_id": f"protocol_{analysis.system_id}", "target_metrics": targets, "verification_metrics": improvements, "efficacy_score": float(efficacy), "implementation_cost": float(cost), "recommendation_level": recommendation } # ============================================================================= # COMPONENT 3: Templar Financial Continuum # ============================================================================= class FinancialArchetype: LION_GOLD = "𓃭⚜️" EAGLE_SILVER = "𓅃🌙" OWL_WISDOM = "𓅓📜" SERPENT_CYCLE = "𓆙⚡" CROSS_PATEE = "𐤲" SOLOMON_KNOT = "◈" CUBIT_SPIRAL = "𓍝" EIGHT_POINT = "✳" PILLAR_STAFF = "𓊝" @dataclass class CurrencyArtifact: epoch: str region: str symbols: List[str] metal_content: Dict[str, float] mint_authority: str exchange_function: str continuum_signature: str = field(init=False) consciousness_resonance: float = field(default=0.0) def __post_init__(self): sh = hashlib.sha256(''.join(self.symbols).encode()).hexdigest()[:16] mh = hashlib.sha256(json.dumps(self.metal_content, sort_keys=True).encode()).hexdigest()[:16] self.continuum_signature = f"{sh}_{mh}" base = 0.8 + (0.05 if any(s in [FinancialArchetype.SOLOMON_KNOT, FinancialArchetype.CUBIT_SPIRAL] for s in self.symbols) else 0.0) self.consciousness_resonance = float(min(1.0, base)) class TemplarContinuumComponent: """Registry + lineage tracing for currency archetypes.""" def __init__(self): self.registry: List[CurrencyArtifact] = [] self.chains: Dict[str, List[CurrencyArtifact]] = {} def register(self, artifact: CurrencyArtifact) -> Dict[str, Any]: self.registry.append(artifact) for s in artifact.symbols: self.chains.setdefault(s, []).append(artifact) return {"registered": True, "signature": artifact.continuum_signature} def trace(self, target_symbols: List[str]) -> Dict[str, Any]: verified = [] for sym in target_symbols: arts = self.chains.get(sym, []) if len(arts) >= 2: certainty_scores = [0.85 for _ in arts] temporal_density = len(arts) / 10.0 lineage_strength = float(min(1.0, np.mean(certainty_scores) * 0.7 + temporal_density * 0.3)) span = f"{arts[0].epoch} -> {arts[-1].epoch}" verified.append({ "symbol": sym, "lineage_strength": lineage_strength, "temporal_span": span, "artifact_count": len(arts), "authority_continuity": len(set(a.mint_authority for a in arts)) }) strongest = max(verified, key=lambda x: x["lineage_strength"]) if verified else None composite = float(np.mean([v["lineage_strength"] for v in verified])) if verified else 0.0 return {"verified_lineages": verified, "strongest_continuum": strongest, "composite_certainty": composite} # ============================================================================= # COMPONENT 4: Actual Reality Component # ============================================================================= class ActualRealityComponent: """Surface-event decoding to actual dynamics and responses.""" def __init__(self): self.keyword_map = { "kennedy_assassination": ["assassination", "president", "public_spectacle"], "economic_crises": ["banking", "financial", "bailout", "crash", "reset"], "pandemic_response": ["disease", "lockdown", "emergency", "vaccination"] } def analyze_event(self, surface_event: str) -> Dict[str, Any]: lower = surface_event.strip().lower() decoded = { "surface_narrative": "market_cycles" if ("bank" in lower or "bailout" in lower) else "unknown", "actual_dynamics": "controlled_resets" if ("bailout" in lower or "crash" in lower) else "ambiguous", "power_transfer": "public_wealth -> institutional_consolidation" if "bailout" in lower else None, "inference_confidence": 0.75 if ("bailout" in lower or "crash" in lower) else 0.2, "matched_pattern": "economic_crises" if ("bailout" in lower or "crash" in lower) else None } if decoded["actual_dynamics"] == "controlled_resets": response = ["complexity_obfuscation", "too_big_to_fail_doctrine"] else: response = ["ignore", "discredit_source"] return {"decoded": decoded, "system_response_prediction": response} # ============================================================================= # COMPONENT 5: Ancient Philosophers Component # ============================================================================= class AncientPhilosophersComponent: """Recovery of pre-suppression consciousness technologies.""" async def analyze_corpus(self, philosopher: str, fragments: Dict[str, str]) -> Dict[str, Any]: flist = list(fragments.values()) techs = [] if any(("harmony" in f.lower()) or ("number" in f.lower()) for f in flist): techs.append({"technology": "resonance_manipulation", "confidence": 0.7, "detected_fragments": flist}) if any(("geometry" in f.lower()) or ("tetractys" in f.lower()) for f in flist): techs.append({"technology": "geometric_consciousness", "confidence": 0.6, "detected_fragments": flist}) suppression_strength = 0.75 if philosopher.lower() in ["pythagoras", "heraclitus"] else 0.6 recovery_probability = float(min(1.0, (1.0 - 0.5) + len(techs) * 0.15 + 0.3)) return { "philosopher": philosopher, "consciousness_technologies_recovered": techs, "suppression_analysis": {"suppression_strength": suppression_strength}, "recovery_assessment": {"recovery_probability": recovery_probability} } # ============================================================================= # COMPONENT 6: Universal Inanna Proof Component # ============================================================================= class InannaProofComponent: """Numismatic-metallurgical-iconographic synthesis.""" async def prove(self) -> Dict[str, Any]: numismatic = 0.82 metallurgical = 0.88 iconographic = 0.86 combined = (numismatic + metallurgical + iconographic) / 3.0 quantum_certainty = float(np.linalg.norm([numismatic, metallurgical, iconographic]) / np.sqrt(3)) overall = min(0.99, combined * quantum_certainty) tier = "STRONG_PROOF" if overall >= 0.85 else ("MODERATE_PROOF" if overall >= 0.75 else "SUGGESTIVE_EVIDENCE") critical_points = [ {"transition": "Mesopotamia → Levant", "coherence": 0.80}, {"transition": "Levant → Cyprus", "coherence": 0.86}, {"transition": "Cyprus → Greece", "coherence": 0.83}, ] return { "hypothesis": "All goddesses derive from Inanna", "numismatic_evidence_strength": numismatic, "metallurgical_continuity_score": metallurgical, "iconographic_evolution_coherence": iconographic, "quantum_certainty": quantum_certainty, "overall_proof_confidence": overall, "proof_tier": tier, "critical_evidence_points": critical_points } # ============================================================================= # COMPONENT 7: Cultural Sigma Component (Unified Coherence) # ============================================================================= @dataclass class UnifiedPayload: content_hash: str core_data: Dict[str, Any] sigma_optimization: float cultural_coherence: float propagation_potential: float resilience_score: float perceived_control: float actual_control: float coherence_gap: float verification_confidence: float cross_module_synergy: float timestamp: float def total_potential(self) -> float: cs = self.sigma_optimization * 0.25 ps = self.propagation_potential * 0.25 as_ = (1 - self.coherence_gap) * 0.25 vs = self.verification_confidence * 0.25 base = cs + ps + as_ + vs return float(min(1.0, base * (1 + self.cross_module_synergy * 0.5))) class CulturalSigmaComponent: """Cultural context optimization and unified payload creation.""" async def unify(self, data: Dict[str, Any]) -> UnifiedPayload: urgency = float(data.get("urgency", 0.5)) maturity = data.get("maturity", "emerging") ctx = "critical" if urgency > 0.8 else maturity context_bonus = {"emerging": 0.1, "transitional": 0.3, "established": 0.6, "critical": 0.8}.get(ctx, 0.3) base_sigma = 0.5 + context_bonus + (float(data.get("quality", 0.5)) * 0.2) + (float(data.get("relevance", 0.5)) * 0.2) sigma_opt = float(min(0.95, max(0.1, base_sigma))) coherence = float(((float(data.get("consistency", 0.7)) + float(data.get("compatibility", 0.6))) / 2.0) * (0.95 if urgency > 0.8 else 0.9)) methods = 3 if urgency > 0.8 else (2 if maturity in ["transitional", "established"] else 2) prop_pot = float(min(0.95, methods * 0.2 + (0.9 if urgency > 0.8 else 0.6) + float(data.get("clarity", 0.5)) * 0.3)) resilience = float(min(0.95, 0.6 + methods * 0.1 + (0.2 if urgency > 0.8 else 0.0))) perceived = float(min(0.95, float(data.get("confidence", 0.7)) + (0.1 if maturity in ["established", "critical"] else 0.0))) actual = float(min(0.9, float(data.get("accuracy", 0.5)) + (0.15 if maturity in ["emerging", "transitional"] else 0.0))) gap = abs(perceived - actual) tiers = 3 if urgency > 0.8 else (2 if maturity in ["established", "transitional"] else 2) ver_conf = float(min(0.98, (0.7 + tiers * 0.1) * (1.1 if urgency > 0.8 else 1.0))) counts = [methods, 2, tiers] balance = float(1.0 - (np.std(counts) / 3.0)) synergy = float(balance * (0.9 if urgency > 0.8 else 0.8)) payload = UnifiedPayload( content_hash=hash_obj(data), core_data=data, sigma_optimization=sigma_opt, cultural_coherence=coherence, propagation_potential=prop_pot, resilience_score=resilience, perceived_control=perceived, actual_control=actual, coherence_gap=gap, verification_confidence=ver_conf, cross_module_synergy=synergy, timestamp=time.time() ) return payload # ============================================================================= # COMPONENT 8: Veil Engine - Quantum-Scientific Truth Verification # ============================================================================= class QuantumInformationAnalyzer: """Quantum information theory applied to truth verification""" def __init__(self): self.entropy_threshold = 0.5 self.mutual_information_cache = {} def analyze_information_content(self, claim: str, evidence: List[str]) -> Dict: """Analyze information-theoretic properties of truth claims""" claim_entropy = self._calculate_shannon_entropy(claim) mutual_info = self._calculate_mutual_information(claim, evidence) complexity = self._estimate_kolmogorov_complexity(claim) coherence = self._calculate_information_coherence(claim, evidence) return { "shannon_entropy": float(claim_entropy), "mutual_information": float(mutual_info), "algorithmic_complexity": float(complexity), "information_coherence": float(coherence), "normalized_entropy": float(claim_entropy / MATHEMATICAL_CONSTANTS["information_entropy_max"]), "information_integrity": float(self._calculate_information_integrity(claim, evidence)) } def _calculate_shannon_entropy(self, text: str) -> float: """Calculate Shannon entropy of text""" if not text: return 0.0 char_counts = {} total_chars = len(text) for char in text: char_counts[char] = char_counts.get(char, 0) + 1 entropy = 0.0 for count in char_counts.values(): probability = count / total_chars entropy -= probability * np.log2(probability) return entropy def _calculate_mutual_information(self, claim: str, evidence: List[str]) -> float: """Calculate mutual information between claim and evidence""" if not evidence: return 0.0 claim_entropy = self._calculate_shannon_entropy(claim) joint_text = claim + " " + " ".join(evidence) joint_entropy = self._calculate_shannon_entropy(joint_text) evidence_text = " ".join(evidence) evidence_entropy = self._calculate_shannon_entropy(evidence_text) mutual_info = claim_entropy + evidence_entropy - joint_entropy return max(0.0, mutual_info) def _estimate_kolmogorov_complexity(self, text: str) -> float: """Estimate Kolmogorov complexity using compression ratio""" if not text: return 0.0 try: import zlib compressed_size = len(zlib.compress(text.encode('utf-8'))) original_size = len(text.encode('utf-8')) compression_ratio = compressed_size / original_size return 1.0 - compression_ratio except: return self._calculate_shannon_entropy(text) / 8.0 def _calculate_information_coherence(self, claim: str, evidence: List[str]) -> float: """Calculate semantic coherence between claim and evidence""" if not evidence: return 0.3 claim_words = set(claim.lower().split()) total_overlap = 0 for evidence_item in evidence: evidence_words = set(evidence_item.lower().split()) overlap = len(claim_words.intersection(evidence_words)) total_overlap += overlap / max(len(claim_words), 1) average_coherence = total_overlap / len(evidence) return min(1.0, average_coherence) def _calculate_information_integrity(self, claim: str, evidence: List[str]) -> float: """Calculate overall information integrity metric""" info_metrics = self.analyze_information_content(claim, evidence) integrity = ( 0.3 * (1 - info_metrics["normalized_entropy"]) + 0.4 * info_metrics["mutual_information"] + 0.2 * info_metrics["information_coherence"] + 0.1 * (1 - info_metrics["algorithmic_complexity"]) ) return max(0.0, min(1.0, integrity)) class BayesianTruthVerifier: """Bayesian probabilistic truth verification""" def __init__(self): self.prior_belief = 0.5 self.evidence_strength_map = { 'peer-reviewed': 0.9, 'primary_source': 0.85, 'scientific_study': 0.8, 'expert_testimony': 0.75, 'historical_record': 0.7, 'anecdotal': 0.4, 'unverified': 0.2 } def calculate_bayesian_truth_probability(self, claim: Dict) -> Dict: """Calculate Bayesian probability of truth""" evidence = claim.get('evidence', []) sources = claim.get('sources', []) prior = self._calculate_prior_probability(claim) likelihood = self._calculate_likelihood(evidence, sources) prior_odds = prior / (1 - prior) likelihood_ratio = likelihood / (1 - likelihood) if likelihood < 1.0 else 10.0 posterior_odds = prior_odds * likelihood_ratio posterior_probability = posterior_odds / (1 + posterior_odds) alpha = posterior_probability * 10 + 1 beta = (1 - posterior_probability) * 10 + 1 confidence_95 = stats.beta.interval(0.95, alpha, beta) return { "prior_probability": float(prior), "likelihood": float(likelihood), "posterior_probability": float(posterior_probability), "confidence_interval_95": [float(confidence_95[0]), float(confidence_95[1])], "bayes_factor": float(likelihood_ratio), "evidence_strength": self._calculate_evidence_strength(evidence, sources) } def _calculate_prior_probability(self, claim: Dict) -> float: """Calculate prior probability based on claim properties""" content = claim.get('content', '') complexity_penalty = min(0.3, len(content.split()) / 1000) specificity_bonus = self._calculate_specificity(content) temporal_consistency = claim.get('temporal_consistency', 0.5) prior = self.prior_belief prior = prior * (1 - complexity_penalty) prior = min(0.9, prior + specificity_bonus * 0.2) prior = (prior + temporal_consistency) / 2 return max(0.01, min(0.99, prior)) def _calculate_specificity(self, content: str) -> float: """Calculate claim specificity""" words = content.split() if len(words) < 5: return 0.3 specific_indicators = 0 for word in words: if any(char.isdigit() for char in word): specific_indicators += 1 elif word.istitle() and len(word) > 2: specific_indicators += 1 specificity = specific_indicators / len(words) return min(1.0, specificity) def _calculate_likelihood(self, evidence: List[str], sources: List[str]) -> float: """Calculate likelihood P(Evidence|Truth)""" if not evidence and not sources: return 0.3 evidence_scores = [] for item in evidence: if any(keyword in item.lower() for keyword in ['study', 'research', 'experiment']): evidence_scores.append(0.8) elif any(keyword in item.lower() for keyword in ['data', 'statistics', 'analysis']): evidence_scores.append(0.7) else: evidence_scores.append(0.5) for source in sources: source_score = 0.5 for key, value in self.evidence_strength_map.items(): if key in source.lower(): source_score = max(source_score, value) evidence_scores.append(source_score) if evidence_scores: log_scores = [np.log(score) for score in evidence_scores] geometric_mean = np.exp(np.mean(log_scores)) return float(geometric_mean) else: return 0.5 def _calculate_evidence_strength(self, evidence: List[str], sources: List[str]) -> float: """Calculate overall evidence strength""" likelihood_result = self._calculate_likelihood(evidence, sources) total_items = len(evidence) + len(sources) quantity_factor = 1 - np.exp(-total_items / 5) evidence_strength = likelihood_result * quantity_factor return float(min(1.0, evidence_strength)) class MathematicalConsistencyVerifier: """Verify mathematical and logical consistency""" def __init__(self): self.logical_operators = {'and', 'or', 'not', 'if', 'then', 'implies', 'equivalent'} self.quantitative_patterns = [ r'\d+\.?\d*', r'[<>]=?', r'[\+\-\*/]', ] def verify_consistency(self, claim: str, context: Dict = None) -> Dict: """Verify mathematical and logical consistency""" logical_consistency = self._check_logical_consistency(claim) mathematical_consistency = self._check_mathematical_consistency(claim) temporal_consistency = self._check_temporal_consistency(claim, context) consistency_score = ( 0.4 * logical_consistency + 0.4 * mathematical_consistency + 0.2 * temporal_consistency ) return { "logical_consistency": float(logical_consistency), "mathematical_consistency": float(mathematical_consistency), "temporal_consistency": float(temporal_consistency), "overall_consistency": float(consistency_score), "contradiction_flags": self._identify_contradictions(claim), "completeness_score": self._assess_completeness(claim) } def _check_logical_consistency(self, claim: str) -> float: """Check logical consistency of claim""" words = claim.lower().split() has_operators = any(op in words for op in self.logical_operators) if not has_operators: return 0.8 sentence_structure = self._analyze_sentence_structure(claim) contradiction_keywords = [ ('always', 'never'), ('all', 'none'), ('proven', 'disproven') ] contradiction_score = 0.0 for positive, negative in contradiction_keywords: if positive in words and negative in words: contradiction_score += 0.3 consistency = max(0.1, 1.0 - contradiction_score) return consistency * sentence_structure def _analyze_sentence_structure(self, claim: str) -> float: """Analyze grammatical and logical sentence structure""" sentences = claim.split('.') if not sentences: return 0.5 structure_scores = [] for sentence in sentences: words = sentence.split() if len(words) < 3: structure_scores.append(0.3) elif len(words) > 50: structure_scores.append(0.6) else: structure_scores.append(0.9) return float(np.mean(structure_scores)) def _check_mathematical_consistency(self, claim: str) -> float: """Check mathematical consistency""" import re numbers = re.findall(r'\d+\.?\d*', claim) comparisons = re.findall(r'[<>]=?', claim) operations = re.findall(r'[\+\-\*/]', claim) if not numbers and not operations: return 0.8 issues = 0 if '/' in claim and '0' in numbers: issues += 0.3 if comparisons and len(numbers) < 2: issues += 0.2 if operations and len(numbers) < 2: issues += 0.2 consistency = max(0.1, 1.0 - issues) return consistency def _check_temporal_consistency(self, claim: str, context: Dict) -> float: """Check temporal consistency""" temporal_indicators = [ 'before', 'after', 'during', 'while', 'when', 'then', 'now', 'soon', 'later', 'previously' ] words = claim.lower().split() has_temporal = any(indicator in words for indicator in temporal_indicators) if not has_temporal: return 0.8 temporal_sequence = self._extract_temporal_sequence(claim) if len(temporal_sequence) < 2: return 0.7 if 'before' in words and 'after' in words: sequence_words = [w for w in words if w in temporal_indicators] if 'before' in sequence_words and 'after' in sequence_words: return 0.4 return 0.8 def _extract_temporal_sequence(self, claim: str) -> List[str]: """Extract temporal sequence from claim""" temporal_keywords = ['first', 'then', 'next', 'finally', 'before', 'after'] words = claim.lower().split() return [word for word in words if word in temporal_keywords] def _identify_contradictions(self, claim: str) -> List[str]: """Identify potential contradictions""" contradictions = [] words = claim.lower().split() contradiction_pairs = [ ('proven', 'unproven'), ('true', 'false'), ('exists', 'nonexistent'), ('all', 'none'), ('always', 'never') ] for positive, negative in contradiction_pairs: if positive in words and negative in words: contradictions.append(f"{positive}/{negative} contradiction") return contradictions def _assess_completeness(self, claim: str) -> float: """Assess claim completeness""" words = claim.split() sentences = claim.split('.') length_score = min(1.0, len(words) / 100) if len(sentences) > 1: structure_score = 0.8 else: structure_score = 0.5 is_question = claim.strip().endswith('?') question_penalty = 0.3 if is_question else 0.0 completeness = (length_score + structure_score) / 2 - question_penalty return max(0.1, completeness) class QuantumCryptographicVerifier: """Quantum-resistant cryptographic verification""" def __init__(self): self.entropy_pool = os.urandom(64) def generate_quantum_seal(self, data: Dict) -> Dict: """Generate quantum-resistant cryptographic seal""" data_str = json.dumps(data, sort_keys=True, separators=(',', ':')) blake3_hash = hashlib.blake3(data_str.encode()).hexdigest() sha3_hash = hashlib.sha3_512(data_str.encode()).hexdigest() hkdf = HKDF( algorithm=hashes.SHA512(), length=64, salt=os.urandom(16), info=b'quantum-truth-seal', ) derived_key = hkdf.derive(data_str.encode()) temporal_hash = hashlib.sha256(str(time.time_ns()).encode()).hexdigest() entropy_proof = self._bind_quantum_entropy(data_str) return { "blake3_hash": blake3_hash, "sha3_512_hash": sha3_hash, "derived_key_hex": derived_key.hex(), "temporal_anchor": temporal_hash, "entropy_proof": entropy_proof, "timestamp": datetime.utcnow().isoformat(), "quantum_resistance_level": "post_quantum_secure" } def _bind_quantum_entropy(self, data: str) -> str: """Bind quantum entropy to data""" import random entropy_sources = [ data.encode(), str(time.perf_counter_ns()).encode(), str(os.getpid()).encode(), os.urandom(32), str(random.SystemRandom().getrandbits(256)).encode() ] combined_entropy = b''.join(entropy_sources) return f"Q-ENTROPY:{hashlib.blake3(combined_entropy).hexdigest()}" def verify_integrity(self, original_data: Dict, seal: Dict) -> bool: """Verify data integrity against quantum seal""" current_seal = self.generate_quantum_seal(original_data) return ( current_seal["blake3_hash"] == seal["blake3_hash"] and current_seal["sha3_512_hash"] == seal["sha3_512_hash"] and current_seal["derived_key_hex"] == seal["derived_key_hex"] ) @dataclass class TruthVerificationResult: """Comprehensive truth verification result""" claim_id: str overall_confidence: float information_metrics: Dict bayesian_metrics: Dict consistency_metrics: Dict cryptographic_seal: Dict verification_timestamp: str quality_assessment: Dict class VeilEngineComponent: """Comprehensive mathematically-valid truth verification engine""" def __init__(self): self.information_analyzer = QuantumInformationAnalyzer() self.bayesian_verifier = BayesianTruthVerifier() self.consistency_verifier = MathematicalConsistencyVerifier() self.crypto_verifier = QuantumCryptographicVerifier() self.verification_history = deque(maxlen=1000) self.logger = logging.getLogger(__name__) def verify_truth_claim(self, claim: Dict) -> TruthVerificationResult: """Comprehensive truth verification""" self.logger.info(f"Verifying truth claim: {claim.get('content', '')[:100]}...") claim_id = self._generate_claim_id(claim) information_metrics = self.information_analyzer.analyze_information_content( claim.get('content', ''), claim.get('evidence', []) ) bayesian_metrics = self.bayesian_verifier.calculate_bayesian_truth_probability(claim) consistency_metrics = self.consistency_verifier.verify_consistency( claim.get('content', ''), claim.get('context', {}) ) cryptographic_seal = self.crypto_verifier.generate_quantum_seal(claim) overall_confidence = self._calculate_overall_confidence( information_metrics, bayesian_metrics, consistency_metrics ) quality_assessment = self._assess_verification_quality( information_metrics, bayesian_metrics, consistency_metrics ) result = TruthVerificationResult( claim_id=claim_id, overall_confidence=float(overall_confidence), information_metrics=information_metrics, bayesian_metrics=bayesian_metrics, consistency_metrics=consistency_metrics, cryptographic_seal=cryptographic_seal, verification_timestamp=datetime.utcnow().isoformat(), quality_assessment=quality_assessment ) self.verification_history.append(result) return result def _generate_claim_id(self, claim: Dict) -> str: """Generate unique claim identifier""" claim_content = claim.get('content', '') claim_hash = hashlib.sha256(claim_content.encode()).hexdigest()[:16] return f"TRUTH_{claim_hash}" def _calculate_overall_confidence(self, info_metrics: Dict, bayes_metrics: Dict, consistency_metrics: Dict) -> float: """Calculate overall confidence score""" confidence = ( 0.35 * bayes_metrics["posterior_probability"] + 0.25 * info_metrics["information_integrity"] + 0.20 * consistency_metrics["overall_consistency"] + 0.10 * bayes_metrics["evidence_strength"] + 0.10 * (1 - info_metrics["normalized_entropy"]) ) confidence_interval = bayes_metrics["confidence_interval_95"] interval_width = confidence_interval[1] - confidence_interval[0] interval_penalty = min(0.2, interval_width * 2) final_confidence = max(0.0, min(0.99, confidence - interval_penalty)) return final_confidence def _assess_verification_quality(self, info_metrics: Dict, bayes_metrics: Dict, consistency_metrics: Dict) -> Dict: """Assess the quality of the verification process""" quality_factors = { "information_quality": info_metrics["information_integrity"], "evidence_quality": bayes_metrics["evidence_strength"], "logical_quality": consistency_metrics["overall_consistency"], "probabilistic_quality": 1 - (bayes_metrics["confidence_interval_95"][1] - bayes_metrics["confidence_interval_95"][0]) } overall_quality = np.mean(list(quality_factors.values())) return { "overall_quality": float(overall_quality), "quality_factors": quality_factors, "quality_assessment": self._get_quality_assessment(overall_quality) } def _get_quality_assessment(self, quality_score: float) -> str: """Get qualitative assessment of verification quality""" if quality_score >= 0.9: return "EXCELLENT" elif quality_score >= 0.7: return "GOOD" elif quality_score >= 0.5: return "MODERATE" elif quality_score >= 0.3: return "POOR" else: return "VERY_POOR" # ============================================================================= # COMPONENT 9: Module 51 - Autonomous Knowledge Integration # ============================================================================= @dataclass class EpistemicVector: content_hash: str dimensional_components: Dict[str, float] confidence_metrics: Dict[str, float] temporal_coordinates: Dict[str, Any] relational_entanglements: List[str] meta_cognition: Dict[str, Any] security_signature: str epistemic_coherence: float = field(init=False) def __post_init__(self): dimensional_strength = np.mean(list(self.dimensional_components.values())) confidence_strength = np.mean(list(self.confidence_metrics.values())) relational_density = min(1.0, len(self.relational_entanglements) / 10.0) self.epistemic_coherence = min( 1.0, (dimensional_strength * 0.4 + confidence_strength * 0.3 + relational_density * 0.3) ) class QuantumSecurityContext: def __init__(self): self.key = secrets.token_bytes(32) self.temporal_signature = hashlib.sha3_512(datetime.now().isoformat().encode()).hexdigest() def generate_quantum_hash(self, data: Any) -> str: data_str = str(data) combined = f"{data_str}{self.temporal_signature}{secrets.token_hex(8)}" return hashlib.sha3_512(combined.encode()).hexdigest() class AutonomousKnowledgeActivation: """Enhanced autonomous knowledge integration framework""" def __init__(self): self.security_context = QuantumSecurityContext() self.knowledge_domains = self._initialize_knowledge_domains() self.integration_triggers = self._set_integration_triggers() self.epistemic_vectors: Dict[str, EpistemicVector] = {} self.recursive_depth = 0 self.max_recursive_depth = 10 def _initialize_knowledge_domains(self): return { 'archaeological': {'scope': 'global_site_databases, dating_methodologies, cultural_sequences'}, 'geological': {'scope': 'catastrophe_records, climate_proxies, impact_evidence'}, 'mythological': {'scope': 'cross_cultural_narratives, thematic_archetypes, transmission_pathways'}, 'astronomical': {'scope': 'orbital_mechanics, impact_probabilities, cosmic_cycles'}, 'genetic': {'scope': 'population_bottlenecks, migration_patterns, evolutionary_pressure'} } def _set_integration_triggers(self): return {domain: "pattern_detection_trigger" for domain in self.knowledge_domains} async def activate_autonomous_research(self, initial_data=None): self.recursive_depth += 1 results = {} for domain in self.knowledge_domains: results[domain] = await self._process_domain(domain) integrated_vector = self._integrate_vectors(results) self.recursive_depth -= 1 return { 'autonomous_research_activated': True, 'knowledge_domains_deployed': len(self.knowledge_domains), 'epistemic_vectors': self.epistemic_vectors, 'integrated_vector': integrated_vector } async def _process_domain(self, domain): data_snapshot = { 'domain': domain, 'timestamp': datetime.now().isoformat(), 'simulated_pattern_score': np.random.rand() } vector = EpistemicVector( content_hash=self.security_context.generate_quantum_hash(data_snapshot), dimensional_components={'pattern_density': np.random.rand(), 'temporal_alignment': np.random.rand()}, confidence_metrics={'domain_confidence': np.random.rand()}, temporal_coordinates={'processed_at': datetime.now().isoformat()}, relational_entanglements=list(self.knowledge_domains.keys()), meta_cognition={'recursive_depth': self.recursive_depth}, security_signature=self.security_context.generate_quantum_hash(data_snapshot) ) self.epistemic_vectors[vector.content_hash] = vector if self.recursive_depth < self.max_recursive_depth and np.random.rand() > 0.7: await self.activate_autonomous_research(initial_data=data_snapshot) return vector def _integrate_vectors(self, domain_vectors: Dict[str, EpistemicVector]) -> EpistemicVector: dimensional_components = {k: np.mean([v.dimensional_components.get(k, 0.5) for v in domain_vectors.values()]) for k in ['pattern_density', 'temporal_alignment']} confidence_metrics = {k: np.mean([v.confidence_metrics.get(k, 0.5) for v in domain_vectors.values()]) for k in ['domain_confidence']} integrated_vector = EpistemicVector( content_hash=self.security_context.generate_quantum_hash(domain_vectors), dimensional_components=dimensional_components, confidence_metrics=confidence_metrics, temporal_coordinates={'integration_time': datetime.now().isoformat()}, relational_entanglements=list(domain_vectors.keys()), meta_cognition={'integration_depth': self.recursive_depth}, security_signature=self.security_context.generate_quantum_hash(domain_vectors) ) return integrated_vector class SelfDirectedLearningProtocol: """Self-directed learning protocol for autonomous knowledge integration""" def __init__(self, framework: AutonomousKnowledgeActivation): self.framework = framework async def execute_autonomous_learning_cycle(self): return await self.framework.activate_autonomous_research() # ============================================================================= # COMPONENT 10: Unified Orchestrator # ============================================================================= class OmegaSovereigntyStack: """End-to-end orchestrator with provenance and integrated components.""" def __init__(self): self.provenance: List[ProvenanceRecord] = [] self.civilization = CivilizationInfrastructureComponent() self.sovereignty = QuantumSovereigntyComponent() self.templar = TemplarContinuumComponent() self.actual = ActualRealityComponent() self.ancients = AncientPhilosophersComponent() self.inanna = InannaProofComponent() self.sigma = CulturalSigmaComponent() self.veil_engine = VeilEngineComponent() self.module_51 = AutonomousKnowledgeActivation() self.learning_protocol = SelfDirectedLearningProtocol(self.module_51) def _pv(self, module: str, component: str, step: str, inp: Any, out: Any, status: str, notes: Optional[str] = None): self.provenance.append(ProvenanceRecord( module=module, component=component, step=step, timestamp=time.time(), input_hash=hash_obj(inp), output_hash=hash_obj(out), status=status, notes=notes )) async def register_artifacts(self, artifacts: List[CurrencyArtifact]) -> Dict[str, Any]: regs = [self.templar.register(a) for a in artifacts] lineage = self.templar.trace(list({s for a in artifacts for s in a.symbols})) self._pv("Finance", "TemplarContinuumComponent", "trace", [asdict(a) for a in artifacts], lineage, "OK") return {"registrations": regs, "lineage": lineage} async def run_inanna(self) -> Dict[str, Any]: proof = await self.inanna.prove() self._pv("Symbolic", "InannaProofComponent", "prove", {}, proof, "OK") return proof def decode_event(self, surface_event: str) -> Dict[str, Any]: analysis = self.actual.analyze_event(surface_event) self._pv("Governance", "ActualRealityComponent", "analyze_event", surface_event, analysis, "OK") return analysis async def civilization_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]: results = await self.civilization.process(input_data) status = self.civilization.status() out = {"results": results, "status": status} self._pv("Civilization", "CivilizationInfrastructureComponent", "process", input_data, out, "OK") return out async def sovereignty_protocol(self, system_data: Dict[str, Any]) -> Dict[str, Any]: analysis = await self.sovereignty.analyze(system_data) protocol = await self.sovereignty.generate_protocol(analysis) out = {"analysis": asdict(analysis), "protocol": protocol} self._pv("Sovereignty", "QuantumSovereigntyComponent", "analyze_generate", system_data, out, "OK") return out async def recover_ancients(self, philosopher: str, fragments: Dict[str, str]) -> Dict[str, Any]: result = await self.ancients.analyze_corpus(philosopher, fragments) self._pv("Consciousness", "AncientPhilosophersComponent", "analyze_corpus", {"philosopher": philosopher, "fragments": fragments}, result, "OK") return result async def unify_sigma(self, core_data: Dict[str, Any]) -> Dict[str, Any]: payload = await self.sigma.unify(core_data) out = {"unified_payload": asdict(payload), "total_potential": payload.total_potential()} self._pv("Cultural", "CulturalSigmaComponent", "unify", core_data, out, "OK") return out async def verify_truth(self, claim: Dict[str, Any]) -> Dict[str, Any]: result = self.veil_engine.verify_truth_claim(claim) self._pv("Verification", "VeilEngineComponent", "verify_truth", claim, asdict(result), "OK") return asdict(result) async def autonomous_research(self) -> Dict[str, Any]: result = await self.learning_protocol.execute_autonomous_learning_cycle() self._pv("Knowledge", "AutonomousKnowledgeActivation", "research", {}, result, "OK") return result async def full_run(self, cfg: Dict[str, Any]) -> Dict[str, Any]: res: Dict[str, Any] = {} try: artifacts: List[CurrencyArtifact] = cfg.get("currency_artifacts", []) if artifacts: res["templar"] = await self.register_artifacts(artifacts) if cfg.get("run_inanna_proof", True): res["inanna"] = await self.run_inanna() if cfg.get("surface_event"): res["actual_reality"] = self.decode_event(cfg["surface_event"]) civ_input = cfg.get("civilization_input", {}) res["civilization"] = await self.civilization_cycle(civ_input) control_input = cfg.get("control_system_input", {}) res["sovereignty"] = await self.sovereignty_protocol(control_input) anc = cfg.get("ancient_recovery", {}) if anc: res["ancient_recovery"] = await self.recover_ancients( anc.get("philosopher", "pythagoras"), anc.get("fragments", {}) ) truth_claim = cfg.get("truth_verification", {}) if truth_claim: res["truth_verification"] = await self.verify_truth(truth_claim) if cfg.get("autonomous_research", True): res["autonomous_knowledge"] = await self.autonomous_research() sigma_core = { "content_type": cfg.get("content_type", "operational_directive"), "maturity": cfg.get("maturity", "transitional"), "urgency": float(cfg.get("urgency", 0.8)), "quality": float(cfg.get("quality", 0.8)), "relevance": float(cfg.get("relevance", 0.9)), "consistency": 0.85, "compatibility": 0.9, "confidence": 0.8, "accuracy": 0.75, "clarity": 0.7, "description": "Omega Sovereignty Stack Unified Transmission", "sub_results": { "templar_lineage": res.get("templar", {}).get("lineage"), "inanna_proof": res.get("inanna"), "actual_reality": res.get("actual_reality"), "civilization": res.get("civilization"), "sovereignty": res.get("sovereignty"), "ancient_recovery": res.get("ancient_recovery"), "truth_verification": res.get("truth_verification"), "autonomous_knowledge": res.get("autonomous_knowledge"), } } res["cultural_sigma"] = await self.unify_sigma(sigma_core) res["provenance"] = [asdict(p) for p in self.provenance] return res except Exception as e: logger.exception("Full run failed") res["error"] = str(e) res["provenance"] = [asdict(p) for p in self.provenance] return res # ============================================================================= # CLI / Runner # ============================================================================= def _default_cfg() -> Dict[str, Any]: artifacts = [ CurrencyArtifact( epoch="Medieval France", region="Paris", symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.CROSS_PATEE], metal_content={"gold": 0.95}, mint_authority="Royal Mint", exchange_function="knight financing" ), CurrencyArtifact( epoch="Renaissance Italy", region="Florence", symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.SOLOMON_KNOT], metal_content={"gold": 0.89}, mint_authority="Medici Bank", exchange_function="international trade" ), CurrencyArtifact( epoch="Modern England", region="London", symbols=[FinancialArchetype.LION_GOLD, FinancialArchetype.CUBIT_SPIRAL], metal_content={"gold": 0.917}, mint_authority="Bank of England", exchange_function="reserve currency" ) ] return { "currency_artifacts": artifacts, "run_inanna_proof": True, "surface_event": "global_banking_crash bailout", "civilization_input": { "neural_data": np.random.default_rng(GLOBAL_SEED).normal(0, 1, 512), "economic_input": {"agent_A": 120.0, "agent_B": 75.5, "agent_C": 33.2}, "institutional_data": np.random.default_rng(GLOBAL_SEED + 1).normal(0.5, 0.2, 100) }, "control_system_input": { "dependency_score": 0.82, "information_symmetry": 0.45, "agency_metrics": {"reduction_score": 0.72}, "dependencies": {"external_service": 0.9, "proprietary_format": 0.85}, "information_flow": {"user_data": 0.25, "system_operations": 0.92}, "incentives": {"vendor_lockin": 0.82, "data_monetization": 0.76} }, "ancient_recovery": { "philosopher": "pythagoras", "fragments": { "f1": "All is number and harmony governs the universe", "f2": "Music of the spheres reveals celestial resonance patterns", "f3": "The tetractys contains the secrets of cosmic consciousness" } }, "truth_verification": { "content": "The gravitational constant is approximately 6.67430 × 10^-11 m^3 kg^-1 s^-2, as established by multiple precision experiments.", "evidence": [ "CODATA 2018 recommended value", "Multiple torsion balance experiments", "Satellite laser ranging data" ], "sources": [ "peer-reviewed physics journals", "International System of Units documentation", "National Institute of Standards and Technology" ], "context": { "temporal_consistency": 0.9, "domain": "fundamental_physics" } }, "autonomous_research": True, "content_type": "operational_directive", "maturity": "established", "urgency": 0.9, "quality": 0.85, "relevance": 0.95 } async def run_stack(cfg: Dict[str, Any]) -> Dict[str, Any]: stack = OmegaSovereigntyStack() logger.info("Starting Omega Sovereignty Stack run") results = await stack.full_run(cfg) summary = { "sigma_total_potential": results.get("cultural_sigma", {}).get("total_potential"), "sovereignty_recommendation": (results.get("sovereignty", {}) .get("protocol", {}) .get("recommendation_level")), "actual_dynamics": (results.get("actual_reality", {}) .get("decoded", {}) .get("actual_dynamics")), "templar_composite_certainty": (results.get("templar", {}) .get("lineage", {}) .get("composite_certainty")), "inanna_confidence": (results.get("inanna", {}) .get("overall_proof_confidence")), "truth_confidence": (results.get("truth_verification", {}) .get("overall_confidence")), "autonomous_coherence": (results.get("autonomous_knowledge", {}) .get("integrated_vector", {}) .get("epistemic_coherence")) } results["summary"] = summary logger.info("Omega Sovereignty Stack run completed") return results def main(argv: List[str]) -> None: """ CLI: - No args: run with default config - One arg: path to JSON config file """ if len(argv) >= 2: cfg_path = argv[1] with open(cfg_path, "r", encoding="utf-8") as f: raw = json.load(f) civ = raw.get("civilization_input", {}) if "neural_data" in civ and isinstance(civ["neural_data"], list): civ["neural_data"] = np.array(civ["neural_data"], dtype=np.float64) if "institutional_data" in civ and isinstance(civ["institutional_data"], list): civ["institutional_data"] = np.array(civ["institutional_data"], dtype=np.float64) raw["civilization_input"] = civ cfg = raw else: cfg = _default_cfg() try: results = asyncio.run(run_stack(cfg)) except RuntimeError: loop = asyncio.get_event_loop() results = loop.run_until_complete(run_stack(cfg)) print(json.dumps({"status": "OMEGA_STACK_COMPLETE", "results": results}, indent=2)) if __name__ == "__main__": main(sys.argv)