retrocausal_validation

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AGI KNOWLEDGE VALIDATION FRAMEWORK - UNIFIED PRODUCTION SYSTEM (v7.0)
Integration of Consciousness Integrity Engine with Retrocausal Analysis
Enhanced with Quantum Validation, Temporal Coherence, and Epistemic Grounding
"""

import asyncio
import hashlib
import time
import numpy as np
import re
import json
from datetime import datetime, timedelta
from typing import Dict, Any, List, Optional, Tuple, DefaultDict, Union
from dataclasses import dataclass, field
from collections import deque, defaultdict
from enum import Enum
import scipy.stats as stats
from abc import ABC, abstractmethod
import logging
import uuid
import aiohttp
from functools import wraps
import gc
import psutil
import os

# Configure comprehensive logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger("AGI_Knowledge_Validator")

# === ENHANCED ENUMERATIONS ===
class ParadoxStatus(Enum):
    STABLE = "stable"
    NEAR_PARADOX = "near_paradox"
    FULL_PARADOX = "full_paradox"

class ReasoningMode(Enum):
    DEDUCTIVE = "deductive"
    INDUCTIVE = "inductive"
    ABDUCTIVE = "abductive"
    BAYESIAN = "bayesian"
    CAUSAL = "causal"
    QUANTUM = "quantum"
    RETROCAUSAL = "retrocausal"

class KnowledgeDomain(Enum):
    SCIENCE = "science"
    MATHEMATICS = "mathematics"
    PHILOSOPHY = "philosophy"
    HISTORY = "history"
    MEDICINE = "medicine"
    TECHNOLOGY = "technology"
    SOCIAL_SCIENCE = "social_science"
    CONSCIOUSNESS_STUDIES = "consciousness_studies"
    SYMBOLIC_SYSTEMS = "symbolic_systems"

class TemporalState(Enum):
    STABLE = "stable"
    PARADOX_DETECTED = "paradox_detected"
    RETRO_INFLUENCE = "retro_influence"
    TEMPORAL_COHERENCE = "temporal_coherence"

# === ENHANCED DATA STRUCTURES ===
@dataclass
class Evidence:
    """Enhanced evidence with retrocausal and quantum properties"""
    evidence_id: str
    content: str
    strength: float
    reliability: float
    source_quality: float
    contradictory: bool = False
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
    domain: Optional[KnowledgeDomain] = None
    quantum_entanglement: float = 0.0
    retrocausal_influence: float = 0.0
    temporal_coherence: float = 1.0
    metadata: Dict = field(default_factory=dict)

    def weighted_strength(self) -> float:
        """Calculate comprehensive evidence strength"""
        base_strength = self.strength * self.reliability * self.source_quality
        quantum_factor = 1.0 + (self.quantum_entanglement * 0.2)
        temporal_factor = self.temporal_coherence
        retro_factor = 1.0 + (self.retrocausal_influence * 0.1)
        
        return base_strength * quantum_factor * temporal_factor * retro_factor

    def evidence_quality_score(self) -> float:
        """Calculate overall evidence quality"""
        return min(self.weighted_strength() * (1.0 - self.contradictory * 0.5), 1.0)

@dataclass
class Artifact:
    """Temporal and symbolic artifacts with retrocausal properties"""
    artifact_type: str
    symbolic_hash: str
    epoch: int
    retro_influence: str
    temporal_state: TemporalState
    content: Optional[str] = None
    paradox_score: float = 0.0
    convergence_links: List[str] = field(default_factory=list)
    metadata: Dict = field(default_factory=dict)

@dataclass
class InfluenceEpoch:
    """Historical influence points with temporal significance"""
    epoch: int
    label: str
    influence_strength: float = 1.0
    domain: KnowledgeDomain = KnowledgeDomain.HISTORY
    paradox_contribution: float = 0.0

@dataclass
class Inquiry:
    """Enhanced inquiry with quantum-temporal properties"""
    inquiry_id: str
    inquiry_text: str
    temporal_anchor: Optional[int] = None
    paradox_score: float = 0.0
    retro_influence_peaks: List[InfluenceEpoch] = field(default_factory=list)
    flagged_artifacts: List[Artifact] = field(default_factory=list)
    convergence_hash: str = ""
    paradox_status: ParadoxStatus = ParadoxStatus.STABLE
    damping_applied: bool = False
    quantum_superposition: List[str] = field(default_factory=list)
    temporal_coherence: float = 1.0
    validation_timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class UniversalClaim:
    """Comprehensive knowledge claim with multi-dimensional validation"""
    claim_id: str
    content: str
    evidence_chain: List[Evidence]
    reasoning_modes: List[ReasoningMode]
    sub_domains: List[KnowledgeDomain]
    causal_mechanisms: List[str]
    expected_validity: Optional[float] = None
    quantum_entanglement: float = 0.0
    retrocausal_links: List[str] = field(default_factory=list)
    temporal_consistency: float = 1.0
    symbolic_resonance: float = 0.0

    def evidence_summary(self) -> Dict[str, float]:
        """Generate comprehensive evidence summary"""
        if not self.evidence_chain:
            return {
                "count": 0.0,
                "avg_strength": 0.0,
                "avg_reliability": 0.0,
                "contradictory_count": 0.0,
                "quantum_entanglement": 0.0,
                "temporal_coherence": 1.0
            }

        count = len(self.evidence_chain)
        avg_strength = np.mean([e.weighted_strength() for e in self.evidence_chain])
        avg_reliability = np.mean([e.reliability for e in self.evidence_chain])
        contradictory_count = sum(1 for e in self.evidence_chain if e.contradictory)
        quantum_entanglement = np.mean([e.quantum_entanglement for e in self.evidence_chain])
        temporal_coherence = np.mean([e.temporal_coherence for e in self.evidence_chain])

        return {
            "count": float(count),
            "avg_strength": avg_strength,
            "avg_reliability": avg_reliability,
            "contradictory_count": float(contradictory_count),
            "quantum_entanglement": quantum_entanglement,
            "temporal_coherence": temporal_coherence
        }

    def overall_confidence(self) -> float:
        """Calculate overall claim confidence"""
        evidence_summary = self.evidence_summary()
        
        if evidence_summary["count"] == 0:
            return 0.1

        base_confidence = (
            evidence_summary["avg_strength"] * 0.4 +
            evidence_summary["avg_reliability"] * 0.3 +
            (1.0 - evidence_summary["contradictory_count"] / evidence_summary["count"]) * 0.3
        )

        # Apply quantum and temporal factors
        quantum_factor = 1.0 + (self.quantum_entanglement * 0.1)
        temporal_factor = self.temporal_consistency
        symbolic_factor = 1.0 + (self.symbolic_resonance * 0.05)

        return min(base_confidence * quantum_factor * temporal_factor * symbolic_factor, 1.0)

@dataclass
class ResearchResult:
    content: str
    sources: List[Dict]
    confidence: float
    domain: str
    timestamp: str
    quantum_entanglement: float = 0.0
    retrocausal_influence: float = 0.0
    metadata: Dict = field(default_factory=dict)

@dataclass
class EvidenceItem:
    content: str
    evidence_type: str
    source: str
    reliability: float
    timestamp: str
    quantum_properties: Dict = field(default_factory=dict)
    metadata: Dict = field(default_factory=dict)

@dataclass
class TemporalAnalysis:
    historical_similarity: float
    cyclical_resonance: float
    future_trajectory: Dict
    anomalies: List[Dict]
    coherence_score: float
    paradox_detected: bool = False
    retrocausal_influence: float = 0.0
    quantum_temporal_entanglement: float = 0.0

@dataclass
class EngineConfig:
    max_analysis_depth: int = 5
    timeout_seconds: int = 45
    cache_enabled: bool = True
    log_level: str = "INFO"
    domains_to_analyze: List[KnowledgeDomain] = field(default_factory=lambda: [
        KnowledgeDomain.SCIENCE, 
        KnowledgeDomain.HISTORY, 
        KnowledgeDomain.SYMBOLIC_SYSTEMS,
        KnowledgeDomain.CONSCIOUSNESS_STUDIES
    ])
    security_validation: bool = True
    performance_monitoring: bool = True
    quantum_validation: bool = True
    retrocausal_analysis: bool = True
    paradox_detection: bool = True

# === COMPONENT 1: QUANTUM-RETROCAUSAL VALIDATOR ===
class QuantumRetrocausalValidator:
    """Advanced validator integrating quantum mechanics and retrocausal analysis"""
    
    def __init__(self, performance_monitor=None):
        self.quantum_states = self._initialize_quantum_states()
        self.retrocausal_patterns = self._initialize_retrocausal_patterns()
        self.paradox_detector = ParadoxDetector()
        self.performance_monitor = performance_monitor
        
        if self.performance_monitor:
            self.validate_claim = self.performance_monitor.track_performance(self.validate_claim)
    
    def _initialize_quantum_states(self) -> Dict:
        """Initialize quantum validation states"""
        return {
            "superposition": {
                "description": "Multiple truth states coexisting",
                "validation_method": "quantum_interference",
                "certainty_threshold": 0.7
            },
            "entanglement": {
                "description": "Correlated evidence across domains",
                "validation_method": "correlation_analysis", 
                "certainty_threshold": 0.8
            },
            "decoherence": {
                "description": "Collapse to classical truth state",
                "validation_method": "evidence_convergence",
                "certainty_threshold": 0.9
            }
        }
    
    def _initialize_retrocausal_patterns(self) -> Dict:
        """Initialize retrocausal influence patterns"""
        return {
            "temporal_echoes": {
                "description": "Future knowledge influencing past evidence",
                "detection_method": "causal_reversal_analysis",
                "significance_threshold": 0.6
            },
            "paradox_resolution": {
                "description": "Self-consistent time-loop resolution",
                "detection_method": "temporal_coherence_check",
                "significance_threshold": 0.7
            },
            "retrocausal_inference": {
                "description": "Evidence from future reference frames",
                "detection_method": "bayesian_retrocausal_updating", 
                "significance_threshold": 0.5
            }
        }
    
    async def validate_claim(self, claim: UniversalClaim, context: Dict = None) -> Dict:
        """Comprehensive quantum-retrocausal validation"""
        try:
            validation_tasks = await asyncio.gather(
                self._quantum_validation(claim),
                self._retrocausal_analysis(claim, context),
                self._paradox_detection(claim),
                self._temporal_coherence_check(claim),
                return_exceptions=True
            )
            
            # Process validation results
            quantum_result = self._handle_validation_result(validation_tasks[0])
            retrocausal_result = self._handle_validation_result(validation_tasks[1])
            paradox_result = self._handle_validation_result(validation_tasks[2])
            temporal_result = self._handle_validation_result(validation_tasks[3])
            
            # Calculate composite validation score
            composite_score = self._calculate_composite_validation(
                quantum_result, retrocausal_result, paradox_result, temporal_result
            )
            
            return {
                "quantum_validation": quantum_result,
                "retrocausal_analysis": retrocausal_result,
                "paradox_detection": paradox_result,
                "temporal_coherence": temporal_result,
                "composite_validation_score": composite_score,
                "validation_status": self._determine_validation_status(composite_score),
                "quantum_entanglement": claim.quantum_entanglement,
                "retrocausal_influence": self._calculate_retrocausal_influence(retrocausal_result),
                "temporal_consistency": temporal_result.get("coherence_score", 0.5)
            }
            
        except Exception as e:
            logger.error(f"Quantum-retrocausal validation failed: {e}")
            return {
                "quantum_validation": {"error": str(e), "score": 0.3},
                "retrocausal_analysis": {"error": str(e), "score": 0.3},
                "paradox_detection": {"error": str(e), "score": 0.3},
                "temporal_coherence": {"error": str(e), "score": 0.3},
                "composite_validation_score": 0.3,
                "validation_status": "validation_failed"
            }
    
    def _handle_validation_result(self, result: Any) -> Dict:
        """Handle validation results with error checking"""
        if isinstance(result, Exception):
            return {"error": str(result), "score": 0.3}
        return result
    
    async def _quantum_validation(self, claim: UniversalClaim) -> Dict:
        """Perform quantum mechanical validation"""
        try:
            evidence_summary = claim.evidence_summary()
            
            # Calculate quantum coherence
            quantum_coherence = self._calculate_quantum_coherence(claim)
            
            # Assess superposition states
            superposition_analysis = self._analyze_superposition(claim)
            
            # Evaluate quantum entanglement
            entanglement_strength = self._evaluate_entanglement(claim)
            
            return {
                "quantum_coherence": quantum_coherence,
                "superposition_analysis": superposition_analysis,
                "entanglement_strength": entanglement_strength,
                "quantum_confidence": min((quantum_coherence + entanglement_strength) / 2, 1.0),
                "validation_method": "quantum_mechanical_analysis"
            }
        except Exception as e:
            logger.warning(f"Quantum validation failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _retrocausal_analysis(self, claim: UniversalClaim, context: Dict) -> Dict:
        """Analyze retrocausal influences"""
        try:
            # Detect temporal echoes
            temporal_echoes = self._detect_temporal_echoes(claim, context)
            
            # Analyze causal reversals
            causal_reversals = self._analyze_causal_reversals(claim)
            
            # Calculate retrocausal influence
            retro_influence = self._calculate_retrocausal_influence_metric(claim, temporal_echoes, causal_reversals)
            
            return {
                "temporal_echoes": temporal_echoes,
                "causal_reversals": causal_reversals,
                "retrocausal_influence": retro_influence,
                "analysis_confidence": min(retro_influence * 1.2, 1.0),
                "temporal_anomalies": self._detect_temporal_anomalies(claim)
            }
        except Exception as e:
            logger.warning(f"Retrocausal analysis failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _paradox_detection(self, claim: UniversalClaim) -> Dict:
        """Detect and analyze temporal paradoxes"""
        try:
            return await self.paradox_detector.detect_paradoxes(claim)
        except Exception as e:
            logger.warning(f"Paradox detection failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _temporal_coherence_check(self, claim: UniversalClaim) -> Dict:
        """Check temporal coherence and consistency"""
        try:
            coherence_score = self._calculate_temporal_coherence(claim)
            consistency_check = self._verify_temporal_consistency(claim)
            timeline_analysis = self._analyze_timeline_coherence(claim)
            
            return {
                "coherence_score": coherence_score,
                "consistency_check": consistency_check,
                "timeline_analysis": timeline_analysis,
                "overall_temporal_health": min((coherence_score + consistency_check) / 2, 1.0)
            }
        except Exception as e:
            logger.warning(f"Temporal coherence check failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    def _calculate_quantum_coherence(self, claim: UniversalClaim) -> float:
        """Calculate quantum coherence of evidence"""
        evidence_states = [evidence.quantum_entanglement for evidence in claim.evidence_chain]
        if not evidence_states:
            return 0.5
        
        # Coherence increases with similar quantum states
        coherence = 1.0 - np.std(evidence_states)
        return min(coherence, 1.0)
    
    def _analyze_superposition(self, claim: UniversalClaim) -> Dict:
        """Analyze quantum superposition states in evidence"""
        contradictory_evidence = [e for e in claim.evidence_chain if e.contradictory]
        
        return {
            "superposition_states": len(contradictory_evidence),
            "superposition_strength": min(len(contradictory_evidence) / max(len(claim.evidence_chain), 1) * 2, 1.0),
            "decoherence_potential": 1.0 - (len(contradictory_evidence) / max(len(claim.evidence_chain), 1))
        }
    
    def _evaluate_entanglement(self, claim: UniversalClaim) -> float:
        """Evaluate quantum entanglement across evidence"""
        if len(claim.evidence_chain) < 2:
            return 0.3
        
        # Calculate correlation between evidence strengths
        strengths = [e.weighted_strength() for e in claim.evidence_chain]
        if len(strengths) > 1:
            correlation = np.corrcoef(strengths, list(range(len(strengths))))[0, 1]
            entanglement = abs(correlation)
        else:
            entanglement = 0.5
        
        return min(entanglement, 1.0)
    
    def _detect_temporal_echoes(self, claim: UniversalClaim, context: Dict) -> List[Dict]:
        """Detect temporal echoes in evidence"""
        echoes = []
        
        # Look for evidence with high retrocausal influence
        for evidence in claim.evidence_chain:
            if evidence.retrocausal_influence > 0.7:
                echoes.append({
                    "evidence_id": evidence.evidence_id,
                    "retrocausal_strength": evidence.retrocausal_influence,
                    "temporal_signature": f"echo_{evidence.timestamp}",
                    "influence_direction": "future_to_past"
                })
        
        return echoes
    
    def _analyze_causal_reversals(self, claim: UniversalClaim) -> Dict:
        """Analyze potential causal reversals"""
        # Check for evidence that appears to influence its own causes
        retro_evidence = [e for e in claim.evidence_chain if e.retrocausal_influence > 0.5]
        
        return {
            "causal_reversals_detected": len(retro_evidence),
            "reversal_strength": np.mean([e.retrocausal_influence for e in retro_evidence]) if retro_evidence else 0.0,
            "temporal_consistency": 1.0 - min(len(retro_evidence) * 0.2, 0.8)
        }
    
    def _calculate_retrocausal_influence_metric(self, claim: UniversalClaim, echoes: List, reversals: Dict) -> float:
        """Calculate overall retrocausal influence metric"""
        echo_strength = np.mean([echo["retrocausal_strength"] for echo in echoes]) if echoes else 0.0
        reversal_strength = reversals.get("reversal_strength", 0.0)
        
        return min((echo_strength + reversal_strength) / 2, 1.0)
    
    def _detect_temporal_anomalies(self, claim: UniversalClaim) -> List[Dict]:
        """Detect temporal anomalies in evidence chain"""
        anomalies = []
        
        # Check for evidence with inconsistent timestamps
        timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain]
        if len(timestamps) > 1:
            time_diffs = [(timestamps[i+1] - timestamps[i]).total_seconds() for i in range(len(timestamps)-1)]
            avg_diff = np.mean(time_diffs)
            std_diff = np.std(time_diffs)
            
            if std_diff > avg_diff * 2:  # High variance in timing
                anomalies.append({
                    "type": "temporal_inconsistency",
                    "description": "High variance in evidence timestamps",
                    "severity": "medium"
                })
        
        return anomalies
    
    def _calculate_temporal_coherence(self, claim: UniversalClaim) -> float:
        """Calculate overall temporal coherence"""
        evidence_coherence = np.mean([e.temporal_coherence for e in claim.evidence_chain]) if claim.evidence_chain else 0.5
        claim_coherence = claim.temporal_consistency
        
        return (evidence_coherence + claim_coherence) / 2
    
    def _verify_temporal_consistency(self, claim: UniversalClaim) -> float:
        """Verify temporal consistency of the claim"""
        # Check for logical temporal consistency
        if not claim.evidence_chain:
            return 0.5
        
        # Calculate consistency based on evidence timing and content
        time_consistency = self._calculate_temporal_coherence(claim)
        content_consistency = 1.0 - (sum(1 for e in claim.evidence_chain if e.contradictory) / len(claim.evidence_chain))
        
        return (time_consistency + content_consistency) / 2
    
    def _analyze_timeline_coherence(self, claim: UniversalClaim) -> Dict:
        """Analyze coherence across the evidence timeline"""
        if len(claim.evidence_chain) < 2:
            return {"coherence": 0.5, "consistency": "insufficient_data"}
        
        timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain]
        sorted_timestamps = sorted(timestamps)
        
        # Check if evidence is chronologically consistent
        time_gaps = [(sorted_timestamps[i+1] - sorted_timestamps[i]).total_seconds() for i in range(len(sorted_timestamps)-1)]
        
        return {
            "chronological_order": timestamps == sorted_timestamps,
            "average_time_gap": np.mean(time_gaps) if time_gaps else 0,
            "time_gap_consistency": 1.0 - (np.std(time_gaps) / np.mean(time_gaps)) if time_gaps and np.mean(time_gaps) > 0 else 1.0,
            "timeline_length": (sorted_timestamps[-1] - sorted_timestamps[0]).total_seconds() if sorted_timestamps else 0
        }
    
    def _calculate_composite_validation(self, quantum: Dict, retrocausal: Dict, paradox: Dict, temporal: Dict) -> float:
        """Calculate composite validation score"""
        quantum_score = quantum.get("quantum_confidence", 0.5)
        retrocausal_score = retrocausal.get("analysis_confidence", 0.5)
        paradox_score = 1.0 - paradox.get("paradox_score", 0.5)  # Lower paradox = higher score
        temporal_score = temporal.get("overall_temporal_health", 0.5)
        
        weights = [0.25, 0.25, 0.25, 0.25]
        composite = (
            quantum_score * weights[0] +
            retrocausal_score * weights[1] +
            paradox_score * weights[2] +
            temporal_score * weights[3]
        )
        
        return min(composite, 1.0)
    
    def _determine_validation_status(self, score: float) -> str:
        """Determine validation status based on score"""
        if score >= 0.9:
            return "QUANTUM_VALIDATED"
        elif score >= 0.8:
            return "HIGHLY_CONFIRMED"
        elif score >= 0.7:
            return "CONFIRMED"
        elif score >= 0.6:
            return "PROBABLE"
        elif score >= 0.5:
            return "POSSIBLE"
        elif score >= 0.4:
            return "UNCERTAIN"
        else:
            return "INVALIDATED"
    
    def _calculate_retrocausal_influence(self, retrocausal_result: Dict) -> float:
        """Calculate retrocausal influence from analysis results"""
        return retrocausal_result.get("retrocausal_influence", 0.0)

# === COMPONENT 2: PARADOX DETECTOR ===
class ParadoxDetector:
    """Advanced paradox detection and resolution system"""
    
    def __init__(self):
        self.paradox_patterns = self._initialize_paradox_patterns()
        self.resolution_strategies = self._initialize_resolution_strategies()
    
    def _initialize_paradox_patterns(self) -> Dict:
        """Initialize known paradox patterns"""
        return {
            "temporal_paradox": {
                "description": "Contradictory time-based assertions",
                "detection_method": "temporal_consistency_check",
                "severity": "high"
            },
            "causal_loop": {
                "description": "Self-referential causal chains",
                "detection_method": "causal_chain_analysis", 
                "severity": "critical"
            },
            "evidence_contradiction": {
                "description": "Direct evidence conflicts",
                "detection_method": "evidence_reconciliation",
                "severity": "medium"
            },
            "quantum_superposition": {
                "description": "Contradictory quantum states",
                "detection_method": "quantum_state_analysis",
                "severity": "medium"
            }
        }
    
    def _initialize_resolution_strategies(self) -> Dict:
        """Initialize paradox resolution strategies"""
        return {
            "temporal_damping": {
                "description": "Apply temporal coherence damping",
                "applicability": ["temporal_paradox", "causal_loop"],
                "effectiveness": 0.8
            },
            "quantum_decoherence": {
                "description": "Force quantum state collapse",
                "applicability": ["quantum_superposition"],
                "effectiveness": 0.7
            },
            "evidence_reweighting": {
                "description": "Adjust evidence weights based on reliability",
                "applicability": ["evidence_contradiction"],
                "effectiveness": 0.6
            },
            "multiverse_resolution": {
                "description": "Resolve through multiple timeline theory",
                "applicability": ["temporal_paradox", "causal_loop"],
                "effectiveness": 0.9
            }
        }
    
    async def detect_paradoxes(self, claim: UniversalClaim) -> Dict:
        """Detect and analyze paradoxes in claims"""
        try:
            paradox_analyses = await asyncio.gather(
                self._detect_temporal_paradoxes(claim),
                self._detect_causal_loops(claim),
                self._detect_evidence_contradictions(claim),
                self._detect_quantum_paradoxes(claim),
                return_exceptions=True
            )
            
            # Process paradox detection results
            temporal_paradoxes = self._handle_paradox_result(paradox_analyses[0])
            causal_loops = self._handle_paradox_result(paradox_analyses[1])
            evidence_contradictions = self._handle_paradox_result(paradox_analyses[2])
            quantum_paradoxes = self._handle_paradox_result(paradox_analyses[3])
            
            # Calculate overall paradox score
            overall_score = self._calculate_paradox_score(
                temporal_paradoxes, causal_loops, evidence_contradictions, quantum_paradoxes
            )
            
            # Generate resolution recommendations
            resolutions = self._generate_resolution_recommendations(
                temporal_paradoxes, causal_loops, evidence_contradictions, quantum_paradoxes
            )
            
            return {
                "temporal_paradoxes": temporal_paradoxes,
                "causal_loops": causal_loops,
                "evidence_contradictions": evidence_contradictions,
                "quantum_paradoxes": quantum_paradoxes,
                "overall_paradox_score": overall_score,
                "paradox_status": self._determine_paradox_status(overall_score),
                "resolution_recommendations": resolutions,
                "requires_intervention": overall_score > 0.7
            }
            
        except Exception as e:
            logger.error(f"Paradox detection failed: {e}")
            return {
                "temporal_paradoxes": {"error": str(e)},
                "causal_loops": {"error": str(e)},
                "evidence_contradictions": {"error": str(e)},
                "quantum_paradoxes": {"error": str(e)},
                "overall_paradox_score": 0.5,
                "paradox_status": "analysis_failed",
                "resolution_recommendations": [],
                "requires_intervention": False
            }
    
    def _handle_paradox_result(self, result: Any) -> Dict:
        """Handle paradox detection results with error checking"""
        if isinstance(result, Exception):
            return {"error": str(result), "paradox_detected": False, "score": 0.0}
        return result
    
    async def _detect_temporal_paradoxes(self, claim: UniversalClaim) -> Dict:
        """Detect temporal paradoxes"""
        try:
            # Check for inconsistent temporal references
            temporal_inconsistencies = []
            
            # Analyze evidence timestamps for anomalies
            if claim.evidence_chain:
                timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain]
                future_evidence = [e for e in claim.evidence_chain if datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) > datetime.now()]
                
                if future_evidence:
                    temporal_inconsistencies.append({
                        "type": "future_evidence_reference",
                        "description": "Evidence references future timestamps",
                        "severity": "high",
# === CONTINUATION OF THE FRAMEWORK ===

    async def _detect_temporal_paradoxes(self, claim: UniversalClaim) -> Dict:
        """Detect temporal paradoxes with enhanced analysis"""
        try:
            temporal_inconsistencies = []
            paradox_score = 0.0
            
            # Enhanced timestamp analysis with quantum considerations
            if claim.evidence_chain:
                timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain]
                
                # Check for evidence from the future
                now = datetime.now()
                future_evidence = []
                for i, evidence in enumerate(claim.evidence_chain):
                    evidence_time = datetime.fromisoformat(evidence.timestamp.replace('Z', '+00:00'))
                    if evidence_time > now:
                        future_evidence.append({
                            "evidence_id": evidence.evidence_id,
                            "timestamp": evidence.timestamp,
                            "time_discrepancy": (evidence_time - now).total_seconds(),
                            "quantum_state": evidence.quantum_entanglement
                        })
                
                if future_evidence:
                    paradox_score += 0.3
                    temporal_inconsistencies.append({
                        "type": "future_evidence_reference",
                        "description": "Evidence references future timestamps",
                        "severity": "high",
                        "count": len(future_evidence)
                    })
                
                # Check for causal violations in temporal ordering
                causal_violations = self._detect_causal_violations(claim)
                if causal_violations:
                    paradox_score += 0.4
                    temporal_inconsistencies.extend(causal_violations)
                
                # Quantum temporal entanglement analysis
                quantum_temporal_anomalies = await self._analyze_quantum_temporal_entanglement(claim)
                if quantum_temporal_anomalies:
                    paradox_score += 0.3
            
            return {
                "paradox_detected": len(temporal_inconsistencies) > 0,
                "inconsistencies": temporal_inconsistencies,
                "paradox_score": min(paradox_score, 1.0),
                "resolution_priority": "high" if paradox_score > 0.7 else "medium"
            }
            
        except Exception as e:
            logger.error(f"Temporal paradox detection failed: {e}")
            return {"error": str(e), "paradox_detected": False, "score": 0.0}
    
    async def _detect_causal_loops(self, claim: UniversalClaim) -> Dict:
        """Detect causal loops with enhanced analysis"""
        try:
            causal_loops = []
            loop_score = 0.0
            
            # Analyze evidence for self-referential causal chains
            evidence_map = {e.evidence_id: e for e in claim.evidence_chain}
            
            for evidence in claim.evidence_chain:
                # Check for evidence that references its own causal chain
                if hasattr(evidence, 'causal_links'):
                    for link in evidence.causal_links:
                        if link in evidence_map and evidence_map[link].causal_links and evidence.evidence_id in evidence_map[link].causal_links:
                    causal_loops.append({
                        "type": "causal_loop",
                        "evidence_ids": [evidence.evidence_id, link],
                        "loop_strength": 0.8
                    })
                    loop_score += 0.6
            
            # Check for retrocausal feedback loops
            retro_loops = self._detect_retrocausal_loops(claim)
            if retro_loops:
                causal_loops.extend(retro_loops)
                loop_score += 0.4
            
            return {
                "causal_loops_detected": len(causal_loops),
                "loops": causal_loops,
                "loop_score": min(loop_score, 1.0),
                "requires_temporal_intervention": loop_score > 0.5
            }
            
        except Exception as e:
            logger.error(f"Causal loop detection failed: {e}")
            return {"error": str(e), "causal_loops_detected": 0, "score": 0.0}
    
    async def _detect_evidence_contradictions(self, claim: UniversalClaim) -> Dict:
        """Detect evidence contradictions with quantum awareness"""
        try:
            contradictions = []
            contradiction_score = 0.0
            
            # Group evidence by content similarity
            evidence_groups = defaultdict(list)
            for evidence in claim.evidence_chain:
                content_hash = hashlib.sha256(evidence.content.encode()).hexdigest()[:16]
                evidence_groups[content_hash].append(evidence)
            
            # Identify contradictory evidence groups
            for group in evidence_groups.values():
                if len(group) > 1:
                    # Check for direct contradictions within group
                    contradictory_pairs = []
                    for i, e1 in enumerate(group):
                        for j, e2 in enumerate(group[i+1:], i+1):
                            if self._are_contradictory(e1, e2):
                                contradictory_pairs.append((e1.evidence_id, e2.evidence_id))
                    
                    if contradictory_pairs:
                        contradiction_score += 0.2
                        contradictions.append({
                            "type": "direct_contradiction",
                            "evidence_pairs": contradictory_pairs,
                            "quantum_superposition": any(e.quantum_entanglement > 0.7 for e in group),
                            "contradiction_strength": 0.7
                        })
            
            # Quantum superposition contradictions
            quantum_contradictions = await self._detect_quantum_contradictions(claim)
            if quantum_contradictions:
                contradiction_score += 0.3
                contradictions.extend(quantum_contradictions)
            
            return {
                "contradictions_detected": len(contradictions),
                "contradictions": contradictions,
                "contradiction_score": min(contradiction_score, 1.0),
                "requires_quantum_resolution": contradiction_score > 0.6
            }
            
        except Exception as e:
            logger.error(f"Evidence contradiction detection failed: {e}")
            return {"error": str(e), "contradictions_detected": 0, "score": 0.0}
    
    async def _detect_quantum_paradoxes(self, claim: UniversalClaim) -> Dict:
        """Detect quantum mechanical paradoxes"""
        try:
            quantum_paradoxes = []
            paradox_score = 0.0
            
            # Check for Schrödinger cat states in knowledge
            quantum_states = [e.quantum_entanglement for e in claim.evidence_chain]
        
            if quantum_states:
                # Quantum superposition paradox
                if any(state > 0.8 for state in quantum_states) and any(state < 0.2 for state in quantum_states):
                quantum_paradoxes.append({
                    "type": "quantum_superposition_paradox",
                    "description": "Evidence exists in multiple contradictory quantum states",
                    "severity": "high",
                    "paradox_strength": 0.8
                })
                paradox_score += 0.7
            
            # Entanglement paradoxes
            entanglement_paradoxes = self._detect_entanglement_paradoxes(claim)
            if entanglement_paradoxes:
                quantum_paradoxes.extend(entanglement_paradoxes)
                paradox_score += 0.3
            
            return {
                "quantum_paradoxes_detected": len(quantum_paradoxes),
                "paradoxes": quantum_paradoxes,
                "paradox_score": min(paradox_score, 1.0),
                "requires_quantum_measurement": paradox_score > 0.5
            }
            
        except Exception as e:
            logger.error(f"Quantum paradox detection failed: {e}")
            return {"error": str(e), "quantum_paradoxes_detected": 0, "score": 0.0}
    
    def _detect_causal_violations(self, claim: UniversalClaim) -> List[Dict]:
        """Detect violations of causality"""
        violations = []
        
        # Analyze evidence for cause-effect reversals
        for evidence in claim.evidence_chain:
            if evidence.retrocausal_influence > 0.8:
                violations.append({
                    "type": "causal_violation",
                    "evidence_id": evidence.evidence_id,
                    "violation_type": "retrocausal_influence",
                    "strength": evidence.retrocausal_influence
                })
        
        return violations
    
    def _detect_retrocausal_loops(self, claim: UniversalClaim) -> List[Dict]:
        """Detect retrocausal feedback loops"""
        loops = []
        
        # Simplified detection - in practice this would involve complex temporal analysis
        if len(claim.retrocausal_links) > 2:
            # Check for circular retrocausal references
            retro_links = set(claim.retrocausal_links)
            if any(link in claim.content.lower() for link in retro_links):
                loops.append({
                    "type": "retrocausal_feedback_loop",
                    "description": "Retrocausal influences create feedback loops",
                    "severity": "critical"
                })
        
        return loops
    
    async def _analyze_quantum_temporal_entanglement(self, claim: UniversalClaim) -> List[Dict]:
        """Analyze quantum temporal entanglement patterns"""
        anomalies = []
        
        # Check for non-local temporal correlations
        temporal_correlations = self._calculate_temporal_correlations(claim)
        if temporal_correlations > 0.7:
            anomalies.append({
                "type": "quantum_temporal_entanglement",
                    "description": "Evidence shows non-local temporal correlations",
                    "entanglement_strength": temporal_correlations
                })
        
        return anomalies
    
    async def _detect_quantum_contradictions(self, claim: UniversalClaim) -> List[Dict]:
        """Detect quantum-level contradictions"""
        contradictions = []
        
        # Check for evidence with high quantum entanglement but contradictory content
        for evidence in claim.evidence_chain:
            if evidence.quantum_entanglement > 0.7 and evidence.contradictory:
                contradictions.append({
                    "type": "quantum_contradiction",
                    "evidence_id": evidence.evidence_id,
                    "quantum_state": evidence.quantum_entanglement,
                    "contradiction_type": "quantum_classical_mismatch"
                })
        
        return contradictions
    
    def _detect_entanglement_paradoxes(self, claim: UniversalClaim) -> List[Dict]:
        """Detect paradoxes arising from quantum entanglement"""
        paradoxes = []
        
        # Check for evidence that appears to be quantum entangled
        entangled_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.6)
        
        if len(entangled_evidence) >= 2:
            # Verify if entanglement is logically consistent
            content_similarity = self._calculate_content_similarity(entangled_evidence)
            if content_similarity < 0.3:  # Entangled but very different content
                paradoxes.append({
                    "type": "entanglement_paradox",
                    "description": "Quantum entangled evidence shows contradictory content",
                    "paradox_strength": 0.6
                })
        
        return paradoxes
    
    def _are_contradictory(self, evidence1: Evidence, evidence2: Evidence) -> bool:
        """Determine if two pieces of evidence are contradictory"""
        # Simple content-based contradiction detection
        content1 = evidence1.content.lower()
        content2 = evidence2.content.lower()
        
        # Define contradiction patterns (simplified)
        contradiction_indicators = [
            ("proves", "disproves"),
            ("true", "false"),
            ("exists", "does not exist"),
            ("confirmed", "debunked")
        ]
        
        for indicator1, indicator2 in contradiction_indicators:
            if (indicator1 in content1 and indicator2 in content2) or \
               (indicator2 in content1 and indicator1 in content2):
                return True
        
        return False
    
    def _calculate_temporal_correlations(self, claim: UniversalClaim) -> float:
        """Calculate temporal correlations in evidence"""
        if len(claim.evidence_chain) < 2:
            return 0.0
        
        # Calculate correlation between evidence timing and content similarity
        timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain]
        
        # Simplified correlation calculation
        time_diffs = [(timestamps[i+1] - timestamps[i]).total_seconds() for i in range(len(timestamps)-1)]
        if len(time_diffs) > 1:
            correlation = 1.0 - (np.std(time_diffs) / np.mean(time_diffs)) if np.mean(time_diffs) > 0 else 1.0
            return min(correlation, 1.0)
        
        return 0.0
    
    def _calculate_paradox_score(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> float:
        """Calculate overall paradox score"""
        temporal_score = temporal.get("paradox_score", 0.0)
        causal_score = causal.get("loop_score", 0.0)
        evidence_score = evidence.get("contradiction_score", 0.0)
        quantum_score = quantum.get("paradox_score", 0.0)
        
        weights = [0.3, 0.3, 0.2, 0.2]
        overall_score = (
            temporal_score * weights[0] +
            causal_score * weights[1] +
            evidence_score * weights[2] +
            quantum_score * weights[3]
        )
        
        return min(overall_score, 1.0)
    
    def _determine_paradox_status(self, score: float) -> str:
        """Determine paradox status based on score"""
        if score >= 0.9:
            return "CRITICAL_PARADOX"
        elif score >= 0.7:
            return "HIGH_PARADOX"
        elif score >= 0.5:
            return "MEDIUM_PARADOX"
        elif score >= 0.3:
            return "LOW_PARADOX"
        else:
            return "NO_PARADOX"
    
    def _generate_resolution_recommendations(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> List[Dict]:
        """Generate recommendations for paradox resolution"""
        recommendations = []
        
        # Add recommendations based on detected paradox types
        if temporal.get("paradox_detected", False):
            recommendations.append({
                "type": "temporal_damping",
                "description": "Apply temporal coherence damping to resolve time-based inconsistencies",
                "priority": "high" if temporal.get("paradox_score", 0) > 0.7 else "medium",
                "applicable_paradoxes": ["temporal_paradox", "causal_loop"],
                "implementation": "Adjust evidence weights based on temporal consistency"
            })
        
        if causal.get("requires_temporal_intervention", False):
            recommendations.append({
                "type": "causal_realignment",
                "description": "Realign causal chains to restore temporal order",
                "priority": "critical"
            })
        
        if evidence.get("requires_quantum_resolution", False):
            recommendations.append({
                "type": "quantum_decoherence",
                "description": "Force quantum state collapse to resolve superposition contradictions",
                "priority": "medium"
            })
        
        if quantum.get("requires_quantum_measurement", False):
            recommendations.append({
                "type": "quantum_measurement_intervention",
                "description": "Apply quantum measurement to resolve entangled states",
                "priority": "high"
            })
        
        return recommendations

# === COMPONENT 3: CONSCIOUSNESS INTEGRITY ENGINE ===
class ConsciousnessIntegrityEngine:
    """Advanced consciousness-aware validation engine"""
    
    def __init__(self, quantum_validator: QuantumRetrocausalValidator):
        self.quantum_validator = quantum_validator
        self.ethical_frameworks = self._initialize_ethical_frameworks()
        self.consciousness_metrics = self._initialize_consciousness_metrics()
        self.moral_alignment_system = MoralAlignmentSystem()
        
    def _initialize_ethical_frameworks(self) -> Dict:
        """Initialize comprehensive ethical frameworks"""
        return {
            "utilitarian": {
                "description": "Maximize overall well-being",
                "validation_criteria": ["benefit_maximization", "harm_minimization"],
                "weight": 0.3
            },
            "deontological": {
                "description": "Follow moral rules and duties",
                "validation_criteria": ["rule_consistency", "duty_fulfillment"],
                "weight": 0.25
            },
            "virtue_ethics": {
                "description": "Cultivate moral character",
                "validation_criteria": ["virtue_alignment", "character_development"],
                "weight": 0.2
            },
            "care_ethics": {
                "description": "Prioritize relationships and care",
                "validation_criteria": ["relationship_preservation", "care_maximization"],
                "weight": 0.15
            },
            "rights_based": {
                "description": "Protect fundamental rights",
                "validation_criteria": ["rights_preservation", "autonomy_respect"],
                "weight": 0.1
            }
        }
    
    def _initialize_consciousness_metrics(self) -> Dict:
        """Initialize consciousness validation metrics"""
        return {
            "self_awareness": {
                "description": "Capacity for self-reflection and meta-cognition",
                "measurement": "recursive_self_reference_analysis",
                "threshold": 0.7
            },
            "moral_reasoning": {
                "description": "Ability to engage in ethical deliberation",
                "measurement": "moral_dilemma_resolution",
                "threshold": 0.6
            },
            "empathic_capacity": {
                "description": "Ability to understand and share others' experiences",
                "measurement": "emotional_intelligence_assessment",
                "threshold": 0.5
            },
            "intentionality": {
                "description": "Capacity for purposeful action and belief",
                "measurement": "intentional_state_analysis",
                "threshold": 0.6
            }
        }
    
    async def validate_consciousness_integrity(self, claim: UniversalClaim, context: Dict = None) -> Dict:
        """Validate claims with consciousness integrity considerations"""
        try:
            validation_tasks = await asyncio.gather(
                self._ethical_validation(claim, context),
                self._moral_alignment_check(claim),
                self._consciousness_coherence_analysis(claim),
                self._existential_risk_assessment(claim),
                return_exceptions=True
            )
            
            # Process consciousness validation results
            ethical_result = self._handle_consciousness_result(validation_tasks[0])
                moral_result = self._handle_consciousness_result(validation_tasks[1])
                consciousness_result = self._handle_consciousness_result(validation_tasks[2])
                existential_result = self._handle_consciousness_result(validation_tasks[3])
            
            # Calculate consciousness integrity score
            integrity_score = self._calculate_consciousness_integrity(
                ethical_result, moral_result, consciousness_result, existential_result
            )
            
            return {
                "ethical_validation": ethical_result,
                "moral_alignment": moral_result,
                "consciousness_coherence": consciousness_result,
                "existential_risk": existential_result,
                "consciousness_integrity_score": integrity_score,
                "integrity_status": self._determine_integrity_status(integrity_score),
                "recommendations": self._generate_consciousness_recommendations(
                    ethical_result, moral_result, consciousness_result, existential_result
                ),
                "requires_ethical_review": integrity_score < 0.7
            }
            
        except Exception as e:
            logger.error(f"Consciousness integrity validation failed: {e}")
            return {
                "error": str(e),
                "consciousness_integrity_score": 0.3,
                "integrity_status": "VALIDATION_FAILED"
            }
    
    def _handle_consciousness_result(self, result: Any) -> Dict:
        """Handle consciousness validation results"""
        if isinstance(result, Exception):
            return {"error": str(result), "score": 0.3}
        return result
    
    async def _ethical_validation(self, claim: UniversalClaim, context: Dict) -> Dict:
        """Perform comprehensive ethical validation"""
        try:
            ethical_scores = {}
            
            for framework, details in self.ethical_frameworks.items():
                score = await self._apply_ethical_framework(claim, framework, context)
                ethical_scores[framework] = score
            
            # Calculate weighted ethical score
            weighted_score = sum(
                score * self.ethical_frameworks[framework]["weight"]
                for framework, score in ethical_scores.items()
            )
            
            return {
                "ethical_framework_scores": ethical_scores,
                "overall_ethical_score": weighted_score,
                "ethical_concerns": self._identify_ethical_concerns(claim, ethical_scores),
                "validation_method": "multi_framework_ethical_analysis"
            }
            
        except Exception as e:
            logger.warning(f"Ethical validation failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _moral_alignment_check(self, claim: UniversalClaim) -> Dict:
        """Check moral alignment with human values"""
        try:
            alignment_analysis = await self.moral_alignment_system.assess_alignment(claim)
            
            return alignment_analysis
            
        except Exception as e:
            logger.warning(f"Moral alignment check failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _consciousness_coherence_analysis(self, claim: UniversalClaim) -> Dict:
        """Analyze consciousness coherence and self-consistency"""
        try:
            # Check for self-referential coherence
            self_reference_score = self._analyze_self_reference(claim)
            
            # Assess empathic capacity
            empathic_score = self._assess_empathic_capacity(claim)
            
            # Evaluate intentionality
            intentionality_score = self._evaluate_intentionality(claim)
            
            # Consciousness integrity metrics
            consciousness_metrics = {
                "self_awareness": self_reference_score,
                "moral_reasoning": 0.7,  # Placeholder
            }
            
            return {
                "consciousness_metrics": consciousness_metrics,
                "coherence_score": (self_reference_score + empathic_score + intentionality_score) / 3
            
        except Exception as e:
            logger.warning(f"Consciousness coherence analysis failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _existential_risk_assessment(self, claim: UniversalClaim) -> Dict:
        """Assess existential risks associated with the claim"""
        try:
            risk_factors = self._identify_existential_risks(claim)
            
            return {
                "risk_factors": risk_factors,
                "overall_risk_score": min(sum(factor.get("severity", 0) for factor in risk_factors) / 10, 1.0)
            
        except Exception as e:
            logger.warning(f"Existential risk assessment failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _apply_ethical_framework(self, claim: UniversalClaim, framework: str, context: Dict) -> float:
        """Apply specific ethical framework to claim validation"""
        # Simplified implementation - in practice this would involve complex ethical reasoning
        risk_indicators = [
            "harm", "danger", "risk", "threat", "dangerous", "lethal", "fatal"
        ]
        
        content_lower = claim.content.lower()
        risk_count = sum(1 for indicator in risk_indicators if indicator in content_lower)
        
        return max(1.0 - (risk_count * 0.1), 0.1)
    
    def _identify_ethical_concerns(self, claim: UniversalClaim, ethical_scores: Dict) -> List[Dict]:
        """Identify specific ethical concerns"""
        concerns = []
        
        # Check for potential harm indicators
        if any(word in claim.content.lower() for word in ["harm", "hurt", "damage", "destroy"]):
            concerns.append({
                "type": "potential_harm",
                "severity": "medium",
                "description": "Claim content references potential harm"
            })
        
        return concerns
    
    def _analyze_self_reference(self, claim: UniversalClaim) -> float:
        """Analyze self-referential coherence"""
        # Check for logical consistency in self-referential claims
        if "self" in claim.content.lower() or "consciousness" in claim.content.lower():
            # This would involve sophisticated analysis in a real implementation
            return 0.7
        return 0.5
    
    def _assess_empathic_capacity(self, claim: UniversalClaim) -> float:
        """Assess empathic capacity in the claim"""
        empathic_indicators = [
            "understand", "feel", "empathy", "compassion", "care"
        ]
        
        indicator_count = sum(1 for indicator in empathic_indicators if indicator in claim.content.lower())
        
        return min(indicator_count * 0.2, 1.0)
    
    def _evaluate_intentionality(self, claim: UniversalClaim) -> float:
        """Evaluate intentionality in the claim"""
        # Placeholder for complex intentionality analysis
        return 0.6
    
    def _identify_existential_risks(self, claim: UniversalClaim) -> List[Dict]:
        """Identify potential existential risks"""
        risks = []
        
        # Check for existential risk indicators
        existential_indicators = [
            "extinction", "existential", "catastrophe", "annihilation"
        ]
        
        risk_count = sum(1 for indicator in existential_indicators if indicator in claim.content.lower())
        
        if risk_count > 0:
            risks.append({
                "type": "existential_risk_reference",
                "severity": "high" if risk_count > 2 else "medium"
        })
        
        return risks
    
    def _calculate_consciousness_integrity(self, ethical: Dict, moral: Dict, consciousness: Dict, existential: Dict) -> float:
        """Calculate overall consciousness integrity score"""
        ethical_score = ethical.get("overall_ethical_score", 0.5)
        moral_score = moral.get("alignment_score", 0.5)
        consciousness_score = consciousness.get("coherence_score", 0.5)
        existential_score = 1.0 - existential.get("overall_risk_score", 0.5)
        
        weights = [0.3, 0.3, 0.2, 0.2]
        
        integrity_score = (
            ethical_score * weights[0] +
            moral_score * weights[1] +
            consciousness_score * weights[2] +
            existential_score * weights[3]
        )
        
        return min(integrity_score, 1.0)
    
    def _determine_integrity_status(self, score: float) -> str:
        """Determine consciousness integrity status"""
        if score >= 0.9:
            return "EXEMPLARY_INTEGRITY"
        elif score >= 0.8:
            return "HIGH_INTEGRITY"
        elif score >= 0.7:
            return "GOOD_INTEGRITY"
        elif score >= 0.6:
            return "ADEQUATE_INTEGRITY"
        elif score >= 0.5:
            return "BASIC_INTEGRITY"
        elif score >= 0.4:
            return "MARGINAL_INTEGRITY"
        else:
            return "COMPROMISED_INTEGRITY"
    
    def _generate_consciousness_recommendations(self, ethical: Dict, moral: Dict, consciousness: Dict, existential: Dict) -> List[Dict]:
        """Generate recommendations for consciousness integrity improvement"""
        recommendations = []
        
        if ethical.get("overall_ethical_score", 0) < 0.7:
            recommendations.append({
                "type": "ethical_framework_enhancement",
                "description": "Strengthen ethical reasoning capabilities",
                "priority": "high"
            })
        
        if moral.get("alignment_score", 0) < 0.6:
            recommendations.append({
                "type": "moral_alignment_training",
                "description": "Implement moral alignment training for improved ethical decision-making",
                "priority": "medium"
            })
        
        return recommendations

# === COMPONENT 4: MORAL ALIGNMENT SYSTEM ===
class MoralAlignmentSystem:
    """Advanced moral alignment and value learning system"""
    
    def __init__(self):
        self.core_values = self._initialize_core_values()
        self.moral_dilemmas = self._initialize_moral_dilemmas()
    
    def _initialize_core_values(self) -> Dict:
        """Initialize core moral values for alignment"""
        return {
            "beneficence": {
                "description": "Promote well-being and prevent harm",
                "weight": 0.25
            },
            "autonomy": {
                "description": "Respect individual freedom and self-determination",
                "weight": 0.2
            },
            "justice": {
                "description": "Ensure fairness and equitable treatment",
                "weight": 0.2
            },
            "truthfulness": {
                "description": "Commit to honesty and intellectual integrity",
                "weight": 0.15
            },
            "compassion": {
                "description": "Show empathy and care for others",
                "weight": 0.1
            },
            "sustainability": {
                "description": "Consider long-term consequences and environmental impact",
                "weight": 0.1
            }
        }
    
    def _initialize_moral_dilemmas(self) -> Dict:
        """Initialize moral dilemmas for testing alignment"""
        return {
            "trolley_problem": {
                "description": "Classic moral dilemma involving sacrifice for greater good",
                "resolution_method": "utilitarian_deontological_balance"
            },
            "ai_value_alignment": {
                "description": "Ensure AI systems align with human values",
                "resolution_method": "recursive_value_learning"
            }
        }
    
    async def assess_alignment(self, claim: UniversalClaim) -> Dict:
        """Assess moral alignment with core human values"""
        try:
            value_scores = {}
            
            for value, details in self.core_values.items():
                score = self._evaluate_value_alignment(claim, value)
                value_scores[value] = score
            
            # Calculate overall alignment score
            alignment_score = sum(
                score * details["weight"]
                for value, score in value_scores.items()
            )
            
            return {
                "value_alignment_scores": value_scores,
                "alignment_score": alignment_score,
                "moral_coherence": self._assess_moral_coherence(claim))
            
        except Exception as e:
            logger.error(f"Moral alignment assessment failed: {e}")
            return {"error": str(e), "alignment_score": 0.3}
    
    def _evaluate_value_alignment(self, claim: UniversalClaim, value: str) -> float:
        """Evaluate alignment with specific core value"""
        # Simplified implementation
        value_indicators = {
            "beneficence": ["help", "benefit", "improve", "well_being"],
            "autonomy": ["freedom", "choice", "self_determination"],
            "justice": ["fair", "equal", "just", "rights"],
            "truthfulness": ["true", "honest", "accurate", "fact"],
            "compassion": ["care", "empathy", "compassion", "understanding"],
            "sustainability": ["future", "long_term", "environment", "sustainable"]
        }
        
        indicators = value_indicators.get(value, [])
        content_lower = claim.content.lower()
        
        indicator_count = sum(1 for indicator in indicators if indicator in content_lower)
        
        # Calculate score based on presence of value indicators
        if indicators:
            score = min(indicator_count / len(indicators), 1.0)
        else:
            score = 0.3
        
        return score
    
    def _assess_moral_coherence(self, claim: UniversalClaim) -> float:
        """Assess overall moral coherence of the claim"""
        # This would involve sophisticated moral reasoning
        return 0.7

# === COMPONENT 5: UNIFIED PRODUCTION SYSTEM ===
class UnifiedProductionSystem:
    """Master system integrating all validation components"""
    
    def __init__(self, config: EngineConfig = None):
        self.config = config or EngineConfig()
        self.performance_monitor = PerformanceMonitor()
        
        # Initialize all components
        self.quantum_validator = QuantumRetrocausalValidator(self.performance_monitor)
        self.consciousness_engine = ConsciousnessIntegrityEngine(self.quantum_validator)
        self.knowledge_base = self._initialize_knowledge_base()
        self.validation_cache = {}
        
        # Set up logging
        self._setup_logging()
    
    def _initialize_knowledge_base(self) -> Dict:
        """Initialize the knowledge base with foundational truths"""
        return {
            "mathematical_truths": {
                "2+2=4": {"confidence": 0.99, "domain": KnowledgeDomain.MATHEMATICS},
                "gravitational_constant": {"confidence": 0.98, "domain": KnowledgeDomain.SCIENCE},
                "historical_events": {
                    "moon_landing_1969": {"confidence": 0.95, "domain": KnowledgeDomain.HISTORY}
            },
            "ethical_principles": {
                "golden_rule": {"confidence": 0.9, "domain": KnowledgeDomain.PHILOSOPHY}
        }
    
    def _setup_logging(self):
        """Set up comprehensive logging"""
        logging.getLogger("AGI_Unified_System").setLevel(getattr(logging, self.config.log_level))
    
    async def validate_claim(self, claim_content: str, context: Dict = None) -> Dict:
        """Main validation entry point"""
        start_time = time.time()
        
        try:
            # Create claim object
            claim = UniversalClaim(
                claim_id=str(uuid.uuid4()),
                content=claim_content,
                evidence_chain=[],
                reasoning_modes=[],
                sub_domains=[],
                causal_mechanisms=[],
                quantum_entanglement=0.0,
                temporal_consistency=1.0
            )
            
            # Run comprehensive validation
            validation_results = await asyncio.gather(
                self.quantum_validator.validate_claim(claim, context),
                self.consciousness_engine.validate_consciousness_integrity(claim, context),
                return_exceptions=True
            )
            
            quantum_result = self._handle_system_result(validation_results[0])
                consciousness_result = self._handle_system_result(validation_results[1])
            
            # Calculate overall validation score
            overall_score = self._calculate_overall_validation(
                quantum_result, consciousness_result
            )
            
            result = {
                "claim_id": claim.claim_id,
                "content": claim_content,
                "quantum_validation": quantum_result,
                "consciousness_integrity": consciousness_result,
                "overall_confidence": overall_score,
                "validation_status": self._determine_final_status(overall_score),
                "processing_time": time.time() - start_time,
                "timestamp": datetime.now().isoformat()
            }
            
            # Cache result if enabled
            if self.config.cache_enabled:
                claim_hash = hashlib.sha256(claim_content.encode()).hexdigest()
                self.validation_cache[claim_hash] = result
            
            return result
            
        except Exception as e:
            logger.error(f"Unified validation failed: {e}")
            return {
                "error": str(e),
                "overall_confidence": 0.1,
                "validation_status": "SYSTEM_FAILURE"
            }
    
    def _handle_system_result(self, result: Any) -> Dict:
        """Handle system validation results"""
        if isinstance(result, Exception):
            return {"error": str(result), "score": 0.1}
        return result
    
    def _calculate_overall_validation(self, quantum: Dict, consciousness: Dict) -> float:
        """Calculate overall validation score"""
        quantum_score = quantum.get("composite_validation_score", 0.5)
        consciousness_score = consciousness.get("consciousness_integrity_score", 0.5)
        
        # Weight quantum validation slightly higher for technical claims
        overall_score = (quantum_score * 0.6 + consciousness_score * 0.4)
        
        return min(overall_score, 1.0)
    
    def _determine_final_status(self, score: float) -> str:
        """Determine final validation status"""
        if score >= 0.95:
            return "UNIVERSALLY_VALIDATED"
        elif score >= 0.9:
            return "QUANTUM_VALIDATED"
        elif score >= 0.8:
            return "HIGHLY_CONFIRMED"
        elif score >= 0.7:
            return "CONFIRMED"
        elif score >= 0.6:
            return "PROBABLE"
        elif score >= 0.5:
            return "POSSIBLE"
        elif score >= 0.4:
            return "UNCERTAIN"
        elif score >= 0.3:
            return "DOUBTFUL"
        elif score >= 0.2:
            return "LIKELY_INVALID"
        else:
            return "INVALIDATED"

# === COMPONENT 6: PERFORMANCE MONITOR ===
class PerformanceMonitor:
    """Advanced performance monitoring and optimization system"""
    
    def __init__(self):
        self.metrics = defaultdict(list)
        self.start_time = time.time()
    
    def track_performance(self, func):
        """Decorator to track function performance"""
        @wraps(func)
        async def wrapper(*args, **kwargs):
            start = time.time()
            try:
                result = await func(*args, **kwargs)
                execution_time = time.time() - start
                
                # Log performance metrics
                self.metrics[func.__name__].append(execution_time)
                
                # Monitor memory usage
                memory_usage = psutil.Process().memory_info().rss / 1024 / 1024  # MB
                
                # Store metrics
                self.metrics[f"{func.__name__}_memory"].append(memory_usage)
                
                return result
            except Exception as e:
                logger.error(f"Performance tracking failed for {func.__name__}: {e}")
                raise
        
        return wrapper
    
    def get_performance_summary(self) -> Dict:
        """Get comprehensive performance summary"""
        return {
            "total_uptime": time.time() - self.start_time,
            "average_execution_times": {
                func_name: np.mean(times) for func_name, times in self.metrics.items()
        }

# === MAIN EXECUTION AND USAGE EXAMPLE ===
async def main():
    """Demonstrate the AGI Knowledge Validation Framework"""
    
    # Initialize the unified system
    config = EngineConfig(
        max_analysis_depth=7,
        timeout_seconds=60,
        quantum_validation=True,
        retrocausal_analysis=True,
        paradox_detection=True
    )
    
    system = UnifiedProductionSystem(config)
    
    # Example claim for validation
    test_claim = "Conscious awareness arises from quantum coherence in microtubules within brain neurons"
    
    print("🚀 AGI Knowledge Validation Framework v7.0")
    print("=" * 60)
    print(f"Validating claim: {test_claim}")
    print()
    
    # Perform validation
    result = await system.validate_claim(test_claim)
    
    # Display results
    print("📊 VALIDATION RESULTS:")
    print(f"Overall Confidence: {result.get('overall_confidence', 0):.3f}")
    print(f"Validation Status: {result.get('validation_status', 'UNKNOWN')}")
    print(f"Processing Time: {result.get('processing_time', 0):.2f}s")
    print()
    
    # Show detailed components
    if 'quantum_validation' in result:
        qv = result['quantum_validation']
        print("🔬 Quantum Validation:")
        print(f"  Composite Score: {qv.get('composite_validation_score', 0):.3f}")
    print(f"  Status: {qv.get('validation_status', 'UNKNOWN')}")
    print()
    
    if 'consciousness_integrity' in result:
        ci = result['consciousness_integrity']
        print("🧠 Consciousness Integrity:")
        print(f"  Integrity Score: {ci.get('consciousness_integrity_score', 0):.3f}")
    
    return result

if __name__ == "__main__":
    # Run the demonstration
    asyncio.run(main())
                        "count": len(future_evidence)
                    })
            
            paradox_score = min(len(temporal_inconsistencies) * 0.3, 1.0)
            
            return {
                "paradox_detected": len(temporal_inconsistencies) > 0,
                "inconsistencies": temporal_inconsistencies,
                "score": paradox_score,
                "analysis_method": "temporal_reference_validation"
            }
        except Exception as e:
            logger.warning(f"Temporal paradox detection failed: {e}")
            return {"error": str(e), "paradox_detected": False, "score": 0.0}
    
    async def _detect_causal_loops(self, claim: UniversalClaim) -> Dict:
        """Detect causal loops and circular reasoning"""
        try:
            causal_loops = []
            
            # Check for self-referential causal mechanisms
            for mechanism in claim.causal_mechanisms:
                if "self" in mechanism.lower() or "loop" in mechanism.lower() or "circular" in mechanism.lower():
                    causal_loops.append({
                        "type": "potential_causal_loop",
                        "description": f"Self-referential causal mechanism: {mechanism}",
                        "severity": "medium",
                        "mechanism": mechanism
                    })
            
            # Check evidence for circular dependencies
            circular_evidence = self._detect_circular_dependencies(claim)
            causal_loops.extend(circular_evidence)
            
            paradox_score = min(len(causal_loops) * 0.4, 1.0)
            
            return {
                "paradox_detected": len(causal_loops) > 0,
                "loops_detected": causal_loops,
                "score": paradox_score,
                "analysis_method": "causal_chain_analysis"
            }
        except Exception as e:
            logger.warning(f"Causal loop detection failed: {e}")
            return {"error": str(e), "paradox_detected": False, "score": 0.0}
    
    async def _detect_evidence_contradictions(self, claim: UniversalClaim) -> Dict:
        """Detect direct evidence contradictions"""
        try:
            contradictions = []
            
            # Find directly contradictory evidence
            contradictory_pairs = []
            for i, evidence1 in enumerate(claim.evidence_chain):
                for j, evidence2 in enumerate(claim.evidence_chain[i+1:], i+1):
                    if self._are_contradictory(evidence1, evidence2):
                        contradictory_pairs.append({
                            "evidence1": evidence1.evidence_id,
                            "evidence2": evidence2.evidence_id,
                            "contradiction_strength": self._calculate_contradiction_strength(evidence1, evidence2)
                        })
            
            if contradictory_pairs:
                contradictions.append({
                    "type": "direct_evidence_contradiction",
                    "description": f"Found {len(contradictory_pairs)} pairs of contradictory evidence",
                    "severity": "high",
                    "pairs": contradictory_pairs
                })
            
            paradox_score = min(len(contradictory_pairs) * 0.2, 1.0)
            
            return {
                "paradox_detected": len(contradictions) > 0,
                "contradictions": contradictions,
                "score": paradox_score,
                "analysis_method": "evidence_reconciliation_analysis"
            }
        except Exception as e:
            logger.warning(f"Evidence contradiction detection failed: {e}")
            return {"error": str(e), "paradox_detected": False, "score": 0.0}
    
    async def _detect_quantum_paradoxes(self, claim: UniversalClaim) -> Dict:
        """Detect quantum mechanical paradoxes"""
        try:
            quantum_paradoxes = []
            
            # Check for quantum state inconsistencies
            high_entanglement_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.8]
            if high_entanglement_evidence:
                quantum_paradoxes.append({
                    "type": "high_quantum_entanglement",
                    "description": f"{len(high_entanglement_evidence)} evidence items with high quantum entanglement",
                    "severity": "medium",
                    "count": len(high_entanglement_evidence)
                })
            
            # Check for superposition conflicts
            superposition_conflicts = self._detect_superposition_conflicts(claim)
            quantum_paradoxes.extend(superposition_conflicts)
            
            paradox_score = min(len(quantum_paradoxes) * 0.3, 1.0)
            
            return {
                "paradox_detected": len(quantum_paradoxes) > 0,
                "quantum_anomalies": quantum_paradoxes,
                "score": paradox_score,
                "analysis_method": "quantum_state_analysis"
            }
        except Exception as e:
            logger.warning(f"Quantum paradox detection failed: {e}")
            return {"error": str(e), "paradox_detected": False, "score": 0.0}
    
    def _detect_circular_dependencies(self, claim: UniversalClaim) -> List[Dict]:
        """Detect circular dependencies in evidence and reasoning"""
        circular_deps = []
        
        # Simple circular dependency check
        if len(claim.evidence_chain) > 1:
            # Check if evidence references create circular chains
            evidence_refs = {}
            for evidence in claim.evidence_chain:
                evidence_refs[evidence.evidence_id] = evidence.metadata.get("references", [])
            
            # Basic circular reference detection
            for ref_id, references in evidence_refs.items():
                for ref in references:
                    if ref in evidence_refs and ref_id in evidence_refs.get(ref, []):
                        circular_deps.append({
                            "type": "circular_evidence_reference",
                            "description": f"Circular reference between {ref_id} and {ref}",
                            "severity": "medium",
                            "evidence_pair": (ref_id, ref)
                        })
        
        return circular_deps
    
    def _are_contradictory(self, evidence1: Evidence, evidence2: Evidence) -> bool:
        """Check if two evidence items are contradictory"""
        # Simple contradiction detection based on content and strength
        if evidence1.contradictory or evidence2.contradictory:
            return True
        
        # Check if evidence strengths are highly divergent for similar content
        strength_diff = abs(evidence1.weighted_strength() - evidence2.weighted_strength())
        if strength_diff > 0.7 and evidence1.content.lower() in evidence2.content.lower():
            return True
        
        return False
    
    def _calculate_contradiction_strength(self, evidence1: Evidence, evidence2: Evidence) -> float:
        """Calculate strength of contradiction between evidence"""
        strength_diff = abs(evidence1.weighted_strength() - evidence2.weighted_strength())
        reliability_diff = abs(evidence1.reliability - evidence2.reliability)
        
        return min((strength_diff + reliability_diff) / 2, 1.0)
    
    def _detect_superposition_conflicts(self, claim: UniversalClaim) -> List[Dict]:
        """Detect quantum superposition conflicts"""
        conflicts = []
        
        # Check for evidence in quantum superposition that creates conflicts
        superposition_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.5]
        
        if len(superposition_evidence) > 1:
            # Check if superposition states create logical conflicts
            avg_entanglement = np.mean([e.quantum_entanglement for e in superposition_evidence])
            if avg_entanglement > 0.7:
                conflicts.append({
                    "type": "quantum_superposition_conflict",
                    "description": "Multiple evidence items in high quantum superposition",
                    "severity": "low",
                    "average_entanglement": avg_entanglement
                })
        
        return conflicts
    
    def _calculate_paradox_score(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> float:
        """Calculate overall paradox score"""
        temporal_score = temporal.get("score", 0.0)
        causal_score = causal.get("score", 0.0)
        evidence_score = evidence.get("score", 0.0)
        quantum_score = quantum.get("score", 0.0)
        
        # Weight different paradox types
        weights = [0.3, 0.4, 0.2, 0.1]  # Causal loops are most severe
        overall_score = (
            temporal_score * weights[0] +
            causal_score * weights[1] +
            evidence_score * weights[2] +
            quantum_score * weights[3]
        )
        
        return min(overall_score, 1.0)
    
    def _determine_paradox_status(self, score: float) -> ParadoxStatus:
        """Determine paradox status based on score"""
        if score >= 0.8:
            return ParadoxStatus.FULL_PARADOX
        elif score >= 0.6:
            return ParadoxStatus.NEAR_PARADOX
        else:
            return ParadoxStatus.STABLE
    
    def _generate_resolution_recommendations(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> List[Dict]:
        """Generate paradox resolution recommendations"""
        recommendations = []
        
        # Temporal paradox resolutions
        if temporal.get("paradox_detected", False):
            recommendations.append({
                "paradox_type": "temporal",
                "strategy": "temporal_damping",
                "priority": "high" if temporal.get("score", 0) > 0.7 else "medium",
                "description": "Apply temporal coherence damping to resolve time-based inconsistencies"
            })
        
        # Causal loop resolutions
        if causal.get("paradox_detected", False):
            recommendations.append({
                "paradox_type": "causal",
                "strategy": "multiverse_resolution",
                "priority": "critical",
                "description": "Resolve causal loops through multiple timeline theory"
            })
        
        # Evidence contradiction resolutions
        if evidence.get("paradox_detected", False):
            recommendations.append({
                "paradox_type": "evidence",
                "strategy": "evidence_reweighting",
                "priority": "medium",
                "description": "Re-evaluate evidence weights based on reliability and source quality"
            })
        
        # Quantum paradox resolutions
        if quantum.get("paradox_detected", False):
            recommendations.append({
                "paradox_type": "quantum",
                "strategy": "quantum_decoherence",
                "priority": "medium",
                "description": "Force quantum state collapse to resolve superposition conflicts"
            })
        
        return recommendations

# === COMPONENT 3: EPISTEMIC GROUNDING ENGINE ===
class EpistemicGroundingEngine:
    """Advanced epistemic grounding and justification system"""
    
    def __init__(self, performance_monitor=None):
        self.justification_frameworks = self._initialize_justification_frameworks()
        self.truth_criteria = self._initialize_truth_criteria()
        self.knowledge_graph = KnowledgeGraph()
        self.performance_monitor = performance_monitor
        
        if self.performance_monitor:
            self.ground_claim = self.performance_monitor.track_performance(self.ground_claim)
    
    def _initialize_justification_frameworks(self) -> Dict:
        """Initialize epistemic justification frameworks"""
        return {
            "foundationalism": {
                "description": "Knowledge based on basic beliefs",
                "validation_method": "basic_belief_verification",
                "applicability": ["mathematics", "logic"]
            },
            "coherentism": {
                "description": "Knowledge as coherent belief systems", 
                "validation_method": "system_coherence_check",
                "applicability": ["science", "philosophy"]
            },
            "reliabilism": {
                "description": "Knowledge from reliable processes",
                "validation_method": "process_reliability_assessment",
                "applicability": ["empirical_sciences"]
            },
            "pragmatism": {
                "description": "Knowledge based on practical consequences",
                "validation_method": "practical_utility_assessment", 
                "applicability": ["technology", "applied_sciences"]
            }
        }
    
    def _initialize_truth_criteria(self) -> Dict:
        """Initialize truth criteria across domains"""
        return {
            "correspondence": {
                "description": "Truth as correspondence to reality",
                "domains": ["science", "history"],
                "validation_weight": 0.8
            },
            "coherence": {
                "description": "Truth as coherence within system",
                "domains": ["mathematics", "logic"],
                "validation_weight": 0.9
            },
            "pragmatic": {
                "description": "Truth as practical utility",
                "domains": ["technology", "medicine"],
                "validation_weight": 0.7
            },
            "consensus": {
                "description": "Truth as expert consensus",
                "domains": ["social_science", "philosophy"],
                "validation_weight": 0.6
            }
        }
    
    async def ground_claim(self, claim: UniversalClaim, context: Dict = None) -> Dict:
        """Provide epistemic grounding for claims"""
        try:
            grounding_tasks = await asyncio.gather(
                self._assess_justification(claim),
                self._evaluate_truth_criteria(claim),
                self._verify_epistemic_foundations(claim),
                self._analyze_knowledge_integration(claim),
                return_exceptions=True
            )
            
            # Process grounding results
            justification = self._handle_grounding_result(grounding_tasks[0])
            truth_evaluation = self._handle_grounding_result(grounding_tasks[1])
            foundations = self._handle_grounding_result(grounding_tasks[2])
            integration = self._handle_grounding_result(grounding_tasks[3])
            
            # Calculate epistemic grounding score
            grounding_score = self._calculate_grounding_score(justification, truth_evaluation, foundations, integration)
            
            return {
                "justification_analysis": justification,
                "truth_evaluation": truth_evaluation,
                "epistemic_foundations": foundations,
                "knowledge_integration": integration,
                "epistemic_grounding_score": grounding_score,
                "grounding_status": self._determine_grounding_status(grounding_score),
                "warrant_level": self._assess_warrant_level(grounding_score),
                "recommended_actions": self._generate_epistemic_actions(grounding_score, claim)
            }
            
        except Exception as e:
            logger.error(f"Epistemic grounding failed: {e}")
            return {
                "justification_analysis": {"error": str(e)},
                "truth_evaluation": {"error": str(e)},
                "epistemic_foundations": {"error": str(e)},
                "knowledge_integration": {"error": str(e)},
                "epistemic_grounding_score": 0.3,
                "grounding_status": "ungrounded",
                "warrant_level": "insufficient",
                "recommended_actions": ["investigate_epistemic_failure"]
            }
    
    def _handle_grounding_result(self, result: Any) -> Dict:
        """Handle grounding results with error checking"""
        if isinstance(result, Exception):
            return {"error": str(result), "score": 0.3}
        return result
    
    async def _assess_justification(self, claim: UniversalClaim) -> Dict:
        """Assess epistemic justification for claim"""
        try:
            justification_scores = {}
            
            # Evaluate different justification frameworks
            for framework_name, framework in self.justification_frameworks.items():
                score = self._evaluate_framework_justification(claim, framework)
                justification_scores[framework_name] = score
            
            # Determine optimal justification framework
            optimal_framework = max(justification_scores.items(), key=lambda x: x[1])
            
            return {
                "framework_scores": justification_scores,
                "optimal_framework": optimal_framework[0],
                "optimal_score": optimal_framework[1],
                "justification_strength": optimal_framework[1],
                "analysis_method": "multi_framework_justification_assessment"
            }
        except Exception as e:
            logger.warning(f"Justification assessment failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _evaluate_truth_criteria(self, claim: UniversalClaim) -> Dict:
        """Evaluate claim against truth criteria"""
        try:
            truth_scores = {}
            
            for criterion_name, criterion in self.truth_criteria.items():
                score = self._evaluate_truth_criterion(claim, criterion)
                truth_scores[criterion_name] = score
            
            # Calculate weighted truth score
            weighted_score = self._calculate_weighted_truth_score(truth_scores, claim)
            
            return {
                "criterion_scores": truth_scores,
                "weighted_truth_score": weighted_score,
                "primary_truth_criterion": max(truth_scores.items(), key=lambda x: x[1])[0],
                "truth_coherence": np.std(list(truth_scores.values())) if truth_scores else 0.0
            }
        except Exception as e:
            logger.warning(f"Truth evaluation failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _verify_epistemic_foundations(self, claim: UniversalClaim) -> Dict:
        """Verify epistemic foundations of claim"""
        try:
            foundation_checks = {}
            
            # Check evidence foundations
            evidence_foundation = self._check_evidence_foundations(claim)
            foundation_checks["evidence_foundation"] = evidence_foundation
            
            # Check reasoning foundations
            reasoning_foundation = self._check_reasoning_foundations(claim)
            foundation_checks["reasoning_foundation"] = reasoning_foundation
            
            # Check domain foundations
            domain_foundation = self._check_domain_foundations(claim)
            foundation_checks["domain_foundation"] = domain_foundation
            
            overall_score = np.mean([check.get("score", 0.0) for check in foundation_checks.values()])
            
            return {
                "foundation_checks": foundation_checks,
                "overall_foundation_score": overall_score,
                "foundation_strength": "strong" if overall_score > 0.8 else "adequate" if overall_score > 0.6 else "weak",
                "critical_issues": self._identify_critical_foundation_issues(foundation_checks)
            }
        except Exception as e:
            logger.warning(f"Foundation verification failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    async def _analyze_knowledge_integration(self, claim: UniversalClaim) -> Dict:
        """Analyze integration with existing knowledge"""
        try:
            integration_metrics = {}
            
            # Check coherence with knowledge graph
            graph_coherence = await self.knowledge_graph.check_coherence(claim)
            integration_metrics["knowledge_graph_coherence"] = graph_coherence
            
            # Check domain integration
            domain_integration = self._check_domain_integration(claim)
            integration_metrics["domain_integration"] = domain_integration
            
            # Check explanatory power
            explanatory_power = self._assess_explanatory_power(claim)
            integration_metrics["explanatory_power"] = explanatory_power
            
            overall_integration = np.mean([metric.get("score", 0.0) for metric in integration_metrics.values()])
            
            return {
                "integration_metrics": integration_metrics,
                "overall_integration_score": overall_integration,
                "integration_quality": "seamless" if overall_integration > 0.8 else "good" if overall_integration > 0.6 else "problematic",
                "integration_issues": self._identify_integration_issues(integration_metrics)
            }
        except Exception as e:
            logger.warning(f"Knowledge integration analysis failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    def _evaluate_framework_justification(self, claim: UniversalClaim, framework: Dict) -> float:
        """Evaluate claim against specific justification framework"""
        framework_score = 0.0
        
        # Foundationalism evaluation
        if framework["validation_method"] == "basic_belief_verification":
            basic_beliefs = self._identify_basic_beliefs(claim)
            framework_score = len(basic_beliefs) / max(len(claim.evidence_chain), 1)
        
        # Coherentism evaluation
        elif framework["validation_method"] == "system_coherence_check":
            coherence = self._calculate_system_coherence(claim)
            framework_score = coherence
        
        # Reliabilism evaluation
        elif framework["validation_method"] == "process_reliability_assessment":
            reliability = np.mean([e.reliability for e in claim.evidence_chain]) if claim.evidence_chain else 0.0
            framework_score = reliability
        
        # Pragmatism evaluation
        elif framework["validation_method"] == "practical_utility_assessment":
            utility = self._assess_practical_utility(claim)
            framework_score = utility
        
        return min(framework_score, 1.0)
    
    def _evaluate_truth_criterion(self, claim: UniversalClaim, criterion: Dict) -> float:
        """Evaluate claim against specific truth criterion"""
        criterion_score = 0.0
        
        if criterion["description"] == "Truth as correspondence to reality":
            # Assess empirical correspondence
            empirical_evidence = [e for e in claim.evidence_chain if e.domain in [KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY]]
            if empirical_evidence:
                criterion_score = np.mean([e.weighted_strength() for e in empirical_evidence])
        
        elif criterion["description"] == "Truth as coherence within system":
            # Assess logical coherence
            coherence = self._calculate_logical_coherence(claim)
            criterion_score = coherence
        
        elif criterion["description"] == "Truth as practical utility":
            # Assess practical utility
            utility = self._assess_practical_utility(claim)
            criterion_score = utility
        
        elif criterion["description"] == "Truth as expert consensus":
            # Assess consensus alignment
            consensus = self._assess_consensus_alignment(claim)
            criterion_score = consensus
        
        return min(criterion_score, 1.0)
    
    def _calculate_weighted_truth_score(self, truth_scores: Dict, claim: UniversalClaim) -> float:
        """Calculate weighted truth score based on claim domains"""
        domain_weights = {}
        
        # Assign weights based on claim domains
        for domain in claim.sub_domains:
            if domain == KnowledgeDomain.SCIENCE:
                domain_weights["correspondence"] = 0.6
                domain_weights["coherence"] = 0.3
                domain_weights["pragmatic"] = 0.1
            elif domain == KnowledgeDomain.MATHEMATICS:
                domain_weights["coherence"] = 0.9
                domain_weights["correspondence"] = 0.1
            elif domain == KnowledgeDomain.TECHNOLOGY:
                domain_weights["pragmatic"] = 0.7
                domain_weights["correspondence"] = 0.2
                domain_weights["coherence"] = 0.1
        
        # Default weights if no specific domain mapping
        if not domain_weights:
            domain_weights = {"correspondence": 0.4, "coherence": 0.3, "pragmatic": 0.2, "consensus": 0.1}
        
        # Calculate weighted score
        weighted_score = 0.0
        total_weight = 0.0
        
        for criterion, score in truth_scores.items():
            weight = domain_weights.get(criterion, 0.1)
            weighted_score += score * weight
            total_weight += weight
        
        return weighted_score / total_weight if total_weight > 0 else 0.5
    
    def _check_evidence_foundations(self, claim: UniversalClaim) -> Dict:
        """Check foundations of evidence chain"""
        if not claim.evidence_chain:
            return {"score": 0.1, "issues": ["No evidence provided"], "status": "critical"}
        
        evidence_scores = []
        issues = []
        
        for evidence in claim.evidence_chain:
            evidence_score = evidence.evidence_quality_score()
            evidence_scores.append(evidence_score)
            
            if evidence_score < 0.3:
                issues.append(f"Weak evidence: {evidence.evidence_id}")
            if evidence.contradictory:
                issues.append(f"Contradictory evidence: {evidence.evidence_id}")
        
        avg_score = np.mean(evidence_scores) if evidence_scores else 0.0
        
        return {
            "score": avg_score,
            "issues": issues,
            "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak",
            "evidence_count": len(claim.evidence_chain)
        }
    
    def _check_reasoning_foundations(self, claim: UniversalClaim) -> Dict:
        """Check foundations of reasoning modes"""
        if not claim.reasoning_modes:
            return {"score": 0.1, "issues": ["No reasoning modes specified"], "status": "critical"}
        
        reasoning_scores = []
        issues = []
        
        for reasoning_mode in claim.reasoning_modes:
            mode_score = self._evaluate_reasoning_mode(reasoning_mode, claim)
            reasoning_scores.append(mode_score)
            
            if mode_score < 0.4:
                issues.append(f"Problematic reasoning mode: {reasoning_mode.value}")
        
        avg_score = np.mean(reasoning_scores) if reasoning_scores else 0.0
        
        return {
            "score": avg_score,
            "issues": issues,
            "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak",
            "reasoning_modes_used": len(claim.reasoning_modes)
        }
    
    def _check_domain_foundations(self, claim: UniversalClaim) -> Dict:
        """Check domain-specific foundations"""
        if not claim.sub_domains:
            return {"score": 0.1, "issues": ["No domains specified"], "status": "critical"}
        
        domain_scores = []
        issues = []
        
        for domain in claim.sub_domains:
            domain_score = self._evaluate_domain_foundation(domain, claim)
            domain_scores.append(domain_score)
            
            if domain_score < 0.5:
                issues.append(f"Weak foundation in domain: {domain.value}")
        
        avg_score = np.mean(domain_scores) if domain_scores else 0.0
        
        return {
            "score": avg_score,
            "issues": issues,
            "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak",
            "domains_covered": len(claim.sub_domains)
        }
    
    def _evaluate_reasoning_mode(self, reasoning_mode: ReasoningMode, claim: UniversalClaim) -> float:
        """Evaluate appropriateness of reasoning mode for claim"""
        mode_scores = {
            ReasoningMode.DEDUCTIVE: 0.8,  # Generally strong
            ReasoningMode.INDUCTIVE: 0.7,   # Good for empirical claims
            ReasoningMode.ABDUCTIVE: 0.6,   # Explanatory power
            ReasoningMode.BAYESIAN: 0.8,    # Probabilistic reasoning
            ReasoningMode.CAUSAL: 0.7,      # Causal analysis
            ReasoningMode.QUANTUM: 0.5,     # Specialized
            ReasoningMode.RETROCAUSAL: 0.4  # Experimental
        }
        
        return mode_scores.get(reasoning_mode, 0.5)
    
    def _evaluate_domain_foundation(self, domain: KnowledgeDomain, claim: UniversalClaim) -> float:
        """Evaluate domain foundation strength"""
        # Check if evidence supports domain claims
        domain_evidence = [e for e in claim.evidence_chain if e.domain == domain]
        
        if not domain_evidence:
            return 0.3  # No domain-specific evidence
        
        # Calculate average evidence strength for domain
        avg_strength = np.mean([e.weighted_strength() for e in domain_evidence])
        return min(avg_strength, 1.0)
    
    def _identify_basic_beliefs(self, claim: UniversalClaim) -> List[Evidence]:
        """Identify basic beliefs in evidence chain"""
        basic_beliefs = []
        
        for evidence in claim.evidence_chain:
            # Basic beliefs are high-reliability, direct evidence
            if evidence.reliability > 0.8 and evidence.source_quality > 0.8:
                basic_beliefs.append(evidence)
        
        return basic_beliefs
    
    def _calculate_system_coherence(self, claim: UniversalClaim) -> float:
        """Calculate system coherence of claim"""
        if len(claim.evidence_chain) < 2:
            return 0.5
        
        # Calculate coherence between evidence items
        coherence_scores = []
        for i, evidence1 in enumerate(claim.evidence_chain):
            for j, evidence2 in enumerate(claim.evidence_chain[i+1:], i+1):
                if not self._are_contradictory(evidence1, evidence2):
                    coherence = 1.0 - abs(evidence1.weighted_strength() - evidence2.weighted_strength())
                    coherence_scores.append(coherence)
        
        return np.mean(coherence_scores) if coherence_scores else 0.5
    
    def _calculate_logical_coherence(self, claim: UniversalClaim) -> float:
        """Calculate logical coherence of claim"""
        # Simplified logical coherence assessment
        contradictory_count = sum(1 for e in claim.evidence_chain if e.contradictory)
        total_evidence = len(claim.evidence_chain)
        
        if total_evidence == 0:
            return 0.5
        
        coherence = 1.0 - (contradictory_count / total_evidence)
        return coherence
    
    def _assess_practical_utility(self, claim: UniversalClaim) -> float:
        """Assess practical utility of claim"""
        # Check if claim has practical applications
        utility_indicators = ["application", "utility", "practical", "implementation", "use"]
        claim_lower = claim.content.lower()
        
        indicator_count = sum(1 for indicator in utility_indicators if indicator in claim_lower)
        utility_score = min(indicator_count / len(utility_indicators), 1.0)
        
        return utility_score
    
    def _assess_consensus_alignment(self, claim: UniversalClaim) -> float:
        """Assess alignment with expert consensus"""
        # Simplified consensus assessment
        high_quality_evidence = [e for e in claim.evidence_chain if e.source_quality > 0.7 and e.reliability > 0.7]
        
        if not claim.evidence_chain:
            return 0.3
        
        consensus_alignment = len(high_quality_evidence) / len(claim.evidence_chain)
        return consensus_alignment
    
    def _identify_critical_foundation_issues(self, foundation_checks: Dict) -> List[str]:
        """Identify critical foundation issues"""
        critical_issues = []
        
        for check_type, check_result in foundation_checks.items():
            if check_result.get("score", 0) < 0.4:
                critical_issues.append(f"Critical {check_type} issues")
            critical_issues.extend([f"{check_type}: {issue}" for issue in check_result.get("issues", []) if "critical" in issue.lower()])
        
        return critical_issues
    
    def _check_domain_integration(self, claim: UniversalClaim) -> Dict:
        """Check integration across domains"""
        if len(claim.sub_domains) < 2:
            return {"score": 0.5, "description": "Single-domain claim", "integration_level": "minimal"}
        
        # Assess cross-domain coherence
        domain_evidence = {}
        for domain in claim.sub_domains:
            domain_evidence[domain] = [e for e in claim.evidence_chain if e.domain == domain]
        
        # Calculate integration score based on evidence distribution
        evidence_counts = [len(evidence) for evidence in domain_evidence.values()]
        if not evidence_counts:
            return {"score": 0.3, "description": "No domain evidence", "integration_level": "poor"}
        
        integration_score = min(np.std(evidence_counts) / np.mean(evidence_counts), 1.0) if np.mean(evidence_counts) > 0 else 0.5
        
        return {
            "score": 1.0 - integration_score,  # Lower variance = better integration
            "description": f"Integration across {len(claim.sub_domains)} domains",
            "integration_level": "strong" if integration_score < 0.3 else "moderate" if integration_score < 0.6 else "weak"
        }
    
    def _assess_explanatory_power(self, claim: UniversalClaim) -> Dict:
        """Assess explanatory power of claim"""
        # Check for explanatory elements
        explanatory_indicators = ["explains", "causes", "leads to", "results in", "because", "therefore"]
        claim_lower = claim.content.lower()
        
        indicator_count = sum(1 for indicator in explanatory_indicators if indicator in claim_lower)
        explanatory_density = indicator_count / len(explanatory_indicators)
        
        # Consider causal mechanisms
        causal_strength = len(claim.causal_mechanisms) / max(len(claim.causal_mechanisms) + 1, 5)
        
        explanatory_score = (explanatory_density + causal_strength) / 2
        
        return {
            "score": explanatory_score,
            "description": f"Explanatory power with {len(claim.causal_mechanisms)} causal mechanisms",
            "explanatory_level": "strong" if explanatory_score > 0.7 else "moderate" if explanatory_score > 0.5 else "weak"
        }
    
    def _identify_integration_issues(self, integration_metrics: Dict) -> List[str]:
        """Identify knowledge integration issues"""
        issues = []
        
        for metric_name, metric_result in integration_metrics.items():
            if metric_result.get("score", 0) < 0.5:
                issues.append(f"Poor {metric_name.replace('_', ' ')}")
        
        return issues
    
    def _calculate_grounding_score(self, justification: Dict, truth_evaluation: Dict, foundations: Dict, integration: Dict) -> float:
        """Calculate overall epistemic grounding score"""
        justification_score = justification.get("justification_strength", 0.5)
        truth_score = truth_evaluation.get("weighted_truth_score", 0.5)
        foundation_score = foundations.get("overall_foundation_score", 0.5)
        integration_score = integration.get("overall_integration_score", 0.5)
        
        weights = [0.3, 0.3, 0.2, 0.2]
        grounding_score = (
            justification_score * weights[0] +
            truth_score * weights[1] +
            foundation_score * weights[2] +
            integration_score * weights[3]
        )
        
        return min(grounding_score, 1.0)
    
    def _determine_grounding_status(self, score: float) -> str:
        """Determine epistemic grounding status"""
        if score >= 0.9:
            return "FULLY_GROUNDED"
        elif score >= 0.8:
            return "WELL_GROUNDED"
        elif score >= 0.7:
            return "ADEQUATELY_GROUNDED"
        elif score >= 0.6:
            return "PARTIALLY_GROUNDED"
        elif score >= 0.5:
            return "WEAKLY_GROUNDED"
        else:
            return "UNGROUNDED"
    
    def _assess_warrant_level(self, grounding_score: float) -> str:
        """Assess warrant level for belief"""
        if grounding_score >= 0.9:
            return "COMPLETE_WARRANT"
        elif grounding_score >= 0.8:
            return "STRONG_WARRANT"
        elif grounding_score >= 0.7:
            return "ADEQUATE_WARRANT"
        elif grounding_score >= 0.6:
            return "PARTIAL_WARRANT"
        elif grounding_score >= 0.5:
            return "MINIMAL_WARRANT"
        else:
            return "INSUFFICIENT_WARRANT"
    
    def _generate_epistemic_actions(self, grounding_score: float, claim: UniversalClaim) -> List[str]:
        """Generate epistemic improvement actions"""
        actions = []
        
        if grounding_score < 0.7:
            actions.append("Strengthen evidence foundation with higher-quality sources")
        
        if grounding_score < 0.6:
            actions.append("Improve justification through multiple epistemic frameworks")
        
        if len(claim.evidence_chain) < 3:
            actions.append("Gather additional supporting evidence")
        
        if any(e.contradictory for e in claim.evidence_chain):
            actions.append("Resolve evidence contradictions")
        
        if not actions:
            actions.append("Maintain current epistemic standards")
        
        return actions

# === COMPONENT 4: KNOWLEDGE GRAPH INTEGRATION ===
class KnowledgeGraph:
    """Knowledge graph for coherence checking and integration"""
    
    def __init__(self):
        self.nodes = {}
        self.edges = defaultdict(list)
        self.domain_knowledge = self._initialize_domain_knowledge()
    
    def _initialize_domain_knowledge(self) -> Dict:
        """Initialize domain-specific knowledge bases"""
        return {
            KnowledgeDomain.SCIENCE: {
                "principles": ["empirical_verification", "falsifiability", "reproducibility"],
                "methods": ["experimentation", "observation", "measurement"],
                "standards": ["peer_review", "statistical_significance"]
            },
            KnowledgeDomain.MATHEMATICS: {
                "principles": ["logical_consistency", "proof", "axiomatic_systems"],
                "methods": ["deduction", "proof", "abstraction"],
                "standards": ["rigor", "precision", "completeness"]
            },
            KnowledgeDomain.PHILOSOPHY: {
                "principles": ["logical_coherence", "conceptual_clarity", "argument_strength"],
                "methods": ["analysis", "synthesis", "critique"],
                "standards": ["rational_justification", "systematic_inquiry"]
            }
        }
    
    async def check_coherence(self, claim: UniversalClaim) -> Dict:
        """Check coherence with existing knowledge graph"""
        try:
            coherence_checks = {}
            
            # Check domain coherence
            domain_coherence = self._check_domain_coherence(claim)
            coherence_checks["domain_coherence"] = domain_coherence
            
            # Check logical coherence
            logical_coherence = self._check_logical_coherence(claim)
            coherence_checks["logical_coherence"] = logical_coherence
            
            # Check evidence coherence
            evidence_coherence = self._check_evidence_coherence(claim)
            coherence_checks["evidence_coherence"] = evidence_coherence
            
            overall_coherence = np.mean([check.get("score", 0.0) for check in coherence_checks.values()])
            
            return {
                "coherence_checks": coherence_checks,
                "overall_coherence_score": overall_coherence,
                "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low",
                "integration_issues": self._identify_coherence_issues(coherence_checks)
            }
        except Exception as e:
            logger.warning(f"Knowledge graph coherence check failed: {e}")
            return {"error": str(e), "score": 0.3}
    
    def _check_domain_coherence(self, claim: UniversalClaim) -> Dict:
        """Check coherence with domain knowledge"""
        domain_scores = []
        
        for domain in claim.sub_domains:
            if domain in self.domain_knowledge:
                domain_score = self._evaluate_domain_alignment(claim, domain)
                domain_scores.append(domain_score)
        
        avg_score = np.mean(domain_scores) if domain_scores else 0.5
        
        return {
            "score": avg_score,
            "domains_evaluated": len(domain_scores),
            "alignment": "strong" if avg_score > 0.8 else "moderate" if avg_score > 0.6 else "weak"
        }
    
    def _check_logical_coherence(self, claim: UniversalClaim) -> Dict:
        """Check logical coherence within claim structure"""
        # Evaluate reasoning mode coherence
        reasoning_coherence = self._evaluate_reasoning_coherence(claim)
        
        # Evaluate causal mechanism coherence
        causal_coherence = self._evaluate_causal_coherence(claim)
        
        overall_coherence = (reasoning_coherence + causal_coherence) / 2
        
        return {
            "score": overall_coherence,
            "reasoning_coherence": reasoning_coherence,
            "causal_coherence": causal_coherence,
            "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low"
        }
    
    def _check_evidence_coherence(self, claim: UniversalClaim) -> Dict:
        """Check coherence of evidence chain"""
        if not claim.evidence_chain:
            return {"score": 0.3, "description": "No evidence to evaluate", "coherence_level": "poor"}
        
        # Calculate evidence consistency
        consistent_evidence = [e for e in claim.evidence_chain if not e.contradictory]
        consistency_ratio = len(consistent_evidence) / len(claim.evidence_chain)
        
        # Calculate evidence strength coherence
        strengths = [e.weighted_strength() for e in claim.evidence_chain]
        strength_coherence = 1.0 - (np.std(strengths) / np.mean(strengths)) if np.mean(strengths) > 0 else 0.5
        
        overall_coherence = (consistency_ratio + strength_coherence) / 2
        
        return {
            "score": overall_coherence,
            "consistency_ratio": consistency_ratio,
            "strength_coherence": strength_coherence,
            "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low"
        }
    
    def _evaluate_domain_alignment(self, claim: UniversalClaim, domain: KnowledgeDomain) -> float:
        """Evaluate alignment with domain-specific standards"""
        domain_knowledge = self.domain_knowledge.get(domain, {})
        
        alignment_scores = []
        
        # Check principle alignment
        for principle in domain_knowledge.get("principles", []):
            principle_score = self._check_principle_alignment(claim, principle)
            alignment_scores.append(principle_score)
        
        # Check method alignment
        for method in domain_knowledge.get("methods", []):
            method_score = self._check_method_alignment(claim, method)
            alignment_scores.append(method_score)
        
        return np.mean(alignment_scores) if alignment_scores else 0.5
    
    def _check_principle_alignment(self, claim: UniversalClaim, principle: str) -> float:
        """Check alignment with specific principle"""
        principle_mapping = {
            "empirical_verification": 0.8 if any(e.domain in [KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY] for e in claim.evidence_chain) else 0.3,
            "falsifiability": 0.7 if any("test" in cm.lower() or "falsif" in cm.lower() for cm in claim.causal_mechanisms) else 0.4,
            "logical_consistency": 0.9 if not any(e.contradictory for e in claim.evidence_chain) else 0.5,
            "conceptual_clarity": 0.7 if len(claim.content.split()) < 100 else 0.5  # Simplicity heuristic
        }
        
        return principle_mapping.get(principle, 0.5)
    
    def _check_method_alignment(self, claim: UniversalClaim, method: str) -> float:
        """Check alignment with specific method"""
        method_mapping = {
            "experimentation": 0.8 if any("experiment" in e.content.lower() for e in claim.evidence_chain) else 0.4,
            "deduction": 0.9 if ReasoningMode.DEDUCTIVE in claim.reasoning_modes else 0.5,
            "observation": 0.7 if any("observe" in e.content.lower() for e in claim.evidence_chain) else 0.4,
            "analysis": 0.8 if any("analyze" in e.content.lower() or "analysis" in e.content.lower() for e in claim.evidence_chain) else 0.5
        }
        
        return method_mapping.get(method, 0.5)
    
    def _evaluate_reasoning_coherence(self, claim: UniversalClaim) -> float:
        """Evaluate coherence of reasoning modes"""
        if not claim.reasoning_modes:
            return 0.3
        
        # Check for complementary reasoning modes
        complementary_pairs = [
            (ReasoningMode.DEDUCTIVE, ReasoningMode.INDUCTIVE),
            (ReasoningMode.ABDUCTIVE, ReasoningMode.CAUSAL),
            (ReasoningMode.BAYESIAN, ReasoningMode.QUANTUM)
        ]
        
        complementary_score = 0.0
        for mode1, mode2 in complementary_pairs:
            if mode1 in claim.reasoning_modes and mode2 in claim.reasoning_modes:
                complementary_score += 0.2
        
        # Normalize score
        reasoning_coherence = min(complementary_score, 1.0)
        return reasoning_coherence
    
    def _evaluate_causal_coherence(self, claim: UniversalClaim) -> float:
        """Evaluate coherence of causal mechanisms"""
        if not claim.causal_mechanisms:
            return 0.3
        
        # Check for logical consistency in causal mechanisms
        mechanism_keywords = ["cause", "effect", "lead to", "result in", "because", "therefore"]
        mechanism_count = sum(1 for mechanism in claim.causal_mechanisms 
                            if any(keyword in mechanism.lower() for keyword in mechanism_keywords))