OBSERVER_4.1

import aiohttp 
import asyncio
import numpy as np
import math
import logging
import time
import psutil
from datetime import datetime, timedelta
from typing import Dict, List, Tuple, Optional, Union
from dataclasses import dataclass, field
from enum import Enum
import json
import hashlib
from contextlib import asynccontextmanager

# Configure logging with better formatting
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s',
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler("agi_validator.log", mode='a')
    ]
)
logger = logging.getLogger("AGI_Validator")

# --------------------------
# ENUMERATION COMPONENTS
# --------------------------
class ValidationStatus(Enum):
    """Enumeration for validation statuses"""
    SUCCESS = "success"
    PARTIAL_SUCCESS = "partial_success"
    FAILURE = "failure"
    ERROR = "error"

class ReasoningMode(Enum):
    """Enumeration for reasoning modes"""
    DEDUCTIVE = "deductive"
    INDUCTIVE = "inductive"
    ABDUCTIVE = "abductive"
    BAYESIAN = "bayesian"
    CAUSAL = "causal"

class KnowledgeDomain(Enum):
    """Enumeration for knowledge domains"""
    SCIENCE = "science"
    MATHEMATICS = "mathematics"
    PHILOSOPHY = "philosophy"
    HISTORY = "history"
    MEDICINE = "medicine"
    TECHNOLOGY = "technology"
    SOCIAL_SCIENCE = "social_science"

# --------------------------
# DATA MODEL COMPONENTS
# --------------------------
@dataclass
class Evidence:
    """Enhanced evidence representation with validation"""
    evidence_id: str
    strength: float
    reliability: float
    source_quality: float = 0.8
    contradictory: bool = False
    timestamp: datetime = field(default_factory=datetime.utcnow)
    domain: Optional[KnowledgeDomain] = None
    
    def __post_init__(self):
        """Validate evidence parameters"""
        if not (0.0 <= self.strength <= 1.0):
            raise ValueError("Evidence strength must be between 0.0 and 1.0")
        if not (0.0 <= self.reliability <= 1.0):
            raise ValueError("Evidence reliability must be between 0.0 and 1.0")
        if not (0.0 <= self.source_quality <= 1.0):
            raise ValueError("Source quality must be between 0.0 and 1.0")
    
    @property
    def weighted_strength(self) -> float:
        """Calculate weighted strength based on reliability and source quality"""
        return self.strength * self.reliability * self.source_quality
    
    def to_dict(self) -> Dict:
        """Convert to dictionary for serialization"""
        return {
            'evidence_id': self.evidence_id,
            'strength': self.strength,
            'reliability': self.reliability,
            'source_quality': self.source_quality,
            'contradictory': self.contradictory,
            'timestamp': self.timestamp.isoformat(),
            'domain': self.domain.value if self.domain else None,
            'weighted_strength': self.weighted_strength
        }

@dataclass
class UniversalClaim:
    """Enhanced claim representation with better validation"""
    claim_id: str
    content: str
    evidence_chain: List[Evidence] = field(default_factory=list)
    reasoning_modes: List[ReasoningMode] = field(default_factory=list)
    sub_domains: List[KnowledgeDomain] = field(default_factory=list)
    causal_mechanisms: List[str] = field(default_factory=list)
    expected_validity: Optional[float] = None
    metadata: Dict = field(default_factory=dict)
    
    def __post_init__(self):
        """Validate claim parameters"""
        if not self.content.strip():
            raise ValueError("Claim content cannot be empty")
        if self.expected_validity is not None:
            if not (0.0 <= self.expected_validity <= 1.0):
                raise ValueError("Expected validity must be between 0.0 and 1.0")
        
        # Generate hash-based ID if not provided
        if not self.claim_id:
            self.claim_id = self._generate_claim_id()
    
    def _generate_claim_id(self) -> str:
        """Generate unique claim ID based on content hash"""
        content_hash = hashlib.md5(self.content.encode()).hexdigest()
        return f"claim_{content_hash[:12]}"
    
    @property
    def evidence_summary(self) -> Dict:
        """Get summary statistics of evidence"""
        if not self.evidence_chain:
            return {'count': 0, 'avg_strength': 0.0, 'avg_reliability': 0.0}
        
        strengths = [e.weighted_strength for e in self.evidence_chain]
        reliabilities = [e.reliability for e in self.eidence_chain]
        
        return {
            'count': len(self.evidence_chain),
            'avg_strength': np.mean(strengths),
            'avg_reliability': np.mean(reliabilities),
            'contradictory_count': sum(1 for e in self.evidence_chain if e.contradictory)
        }
    
    def to_dict(self) -> Dict:
        """Convert to dictionary for serialization"""
        return {
            'claim_id': self.claim_id,
            'content': self.content,
            'evidence_chain': [e.to_dict() for e in self.evidence_chain],
            'reasoning_modes': [m.value for m in self.reasoning_modes],
            'sub_domains': [d.value for d in self.sub_domains],
            'causal_mechanisms': self.causal_mechanisms,
            'expected_validity': self.expected_validity,
            'evidence_summary': self.evidence_summary,
            'metadata': self.metadata
        }

# --------------------------
# CORE VALIDATION COMPONENT
# --------------------------
class AdvancedGeneralIntelligence:
    """Enhanced AGI validation system with improved architecture"""
    
    def __init__(self, 
                 mcp_enabled: bool = True, 
                 mcp_timeout: int = 15, 
                 max_history: int = 100,
                 cache_enabled: bool = True):
        self.mcp_enabled = mcp_enabled
        self.mcp_timeout = mcp_timeout
        self.max_history = max_history
        self.cache_enabled = cache_enabled
self.mcp_url = "https://agents-mcp-hackathon-consilium-mcp.hf.space/run/predict" 
        self.validation_history = []
        self.validation_cache = {}
        self.test_cases = self._initialize_test_cases()
        self._session = None
        logger.info("Enhanced AGI Validator initialized")

    # --------------------------
    # NETWORK COMPONENT
    # --------------------------
    @asynccontextmanager
    async def _get_session(self):
        """Context manager for HTTP session"""
        if self._session is None:
connector = aiohttp.TCPConnector(limit=10, limit_per_host=5) 
timeout = aiohttp.ClientTimeout(total=self.mcp_timeout) 
self._session = aiohttp.ClientSession(connector=connector, timeout=timeout) 
        
        try:
            yield self._session
        except Exception as e:
            logger.error(f"Session error: {e}")
            raise

    async def close(self):
        """Clean up resources"""
        if self._session:
            await self._session.close()
            self._session = None

    # --------------------------
    # CACHING COMPONENT
    # --------------------------
    def _get_cache_key(self, claim: UniversalClaim) -> str:
        """Generate cache key for claim"""
        claim_data = claim.to_dict()
        claim_json = json.dumps(claim_data, sort_keys=True)
        return hashlib.sha256(claim_json.encode()).hexdigest()

    # --------------------------
    # MCP CONSENSUS COMPONENT
    # --------------------------
    async def _get_mcp_consensus(self, claim: UniversalClaim) -> Dict:
        """Enhanced mCP consensus with caching and better error handling"""
        if not self.mcp_enabled:
            logger.info("mCP consensus protocol disabled")
            return self._get_fallback_consensus("mCP disabled")
        
        # Check cache first
        cache_key = self._get_cache_key(claim) if self.cache_enabled else None
        if cache_key and cache_key in self.validation_cache:
            logger.info("Using cached mCP consensus")
            return self.validation_cache[cache_key]
        
        payload = {
            "claim_text": claim.content,
            "domains": [d.value for d in claim.sub_domains],
            "reasoning_modes": [m.value for m in claim.reasoning_modes],
            "evidence_count": len(claim.evidence_chain),
            "evidence_summary": claim.evidence_summary,
            "causal_mechanisms": claim.causal_mechanisms,
            "validation_mode": "full_mesh",
            "rounds": 3
        }
        
        start_time = time.monotonic()
        
        try:
            async with self._get_session() as session:
                async with session.post(self.mcp_url, json=payload) as response:
                    if response.status == 200:
                        result = await response.json()
                        elapsed = time.monotonic() - start_time
                        
                        mcp_result = {
                            **result.get("data", {}),
                            "processing_time": elapsed,
                            "reliability": 1.0,
                            "cache_hit": False
                        }
                        
                        # Cache the result
                        if cache_key:
                            self.validation_cache[cache_key] = mcp_result
                        
                        logger.info(f"mCP consensus received in {elapsed:.2f}s")
                        return mcp_result
                    else:
                        logger.warning(f"mCP returned status {response.status}")
                        return self._get_fallback_consensus(f"HTTP {response.status}")
                        
        except asyncio.TimeoutError:
            logger.warning("mCP request timed out")
            return self._get_fallback_consensus("timeout")
except aiohttp.ClientError as e: 
            logger.error(f"HTTP error in mCP request: {str(e)}")
            return self._get_fallback_consensus(f"client_error: {str(e)}")
        except Exception as e:
            logger.exception(f"Unexpected error in mCP request: {str(e)}")
            return self._get_fallback_consensus(f"unexpected_error: {str(e)}")

    def _get_fallback_consensus(self, reason: str = "unknown") -> Dict:
        """Enhanced fallback consensus with reason tracking"""
        return {
            "consensus_score": 0.5,
            "confidence_interval": [0.4, 0.6],
            "expert_notes": [f"Consensus service unavailable: {reason}"],
            "reliability": 0.0,
            "processing_time": 0.0,
            "fallback_reason": reason
        }

    # --------------------------
    # REASONING ANALYTICS COMPONENT
    # --------------------------
    async def _perform_reasoning_analysis(self, claim: UniversalClaim) -> Dict:
        """Enhanced reasoning analysis with multiple reasoning modes"""
        start_time = time.monotonic()
        
        try:
            results = {}
            
            # Bayesian reasoning
            if ReasoningMode.BAYESIAN in claim.reasoning_modes:
                prior = 0.5  # Neutral prior
                evidence_weights = [e.weighted_strength for e in claim.evidence_chain]
                if evidence_weights:
                    likelihood = np.mean(evidence_weights)
                    # Simplified Bayesian update
                    posterior = (likelihood * prior) / ((likelihood * prior) + ((1 - likelihood) * (1 - prior)))
                    results['bayesian'] = {
                        'prior': prior,
                        'likelihood': likelihood,
                        'posterior': posterior
                    }
            
            # Causal reasoning
            if ReasoningMode.CAUSAL in claim.reasoning_modes:
                causal_strength = len(claim.causal_mechanisms) / max(5, len(claim.causal_mechanisms))
                results['causal'] = {
                    'causal_coherence': min(0.95, 0.5 + causal_strength * 0.4),
                    'mechanism_count': len(claim.causal_mechanisms)
                }
            
            # Deductive reasoning
            if ReasoningMode.DEDUCTIVE in claim.reasoning_modes:
                # Simple logical consistency check
                contradictory_evidence = sum(1 for e in claim.evidence_chain if e.contradictory)
                consistency = max(0.1, 1.0 - (contradictory_evidence / max(1, len(claim.evidence_chain)))
                results['deductive'] = {'logical_consistency': consistency}
            
            processing_time = time.monotonic() - start_time
            
            return {
                **results,
                'processing_time': processing_time,
                'reasoning_modes_used': [m.value for m in claim.reasoning_modes]
            }
            
        except Exception as e:
            logger.error(f"Reasoning analysis failed: {str(e)}")
            return {
                'error': f"Reasoning analysis failed: {str(e)}",
                'processing_time': time.monotonic() - start_time
            }

    # --------------------------
    # EVIDENCE ANALYTICS COMPONENT
    # --------------------------
    async def _analyze_evidence_quality(self, claim: UniversalClaim) -> Dict:
        """Enhanced evidence quality analysis"""
        start_time = time.monotonic()
        
        try:
            if not claim.evidence_chain:
                return {
                    'evidence_score': 0.0,
                    'evidence_count': 0,
                    'quality_factors': {'no_evidence': True},
                    'processing_time': time.monotonic() - start_time
                }
            
            # Calculate various evidence metrics
            strengths = [e.weighted_strength for e in claim.evidence_chain]
            reliabilities = [e.reliability for e in claim.evidence_chain]
            source_qualities = [e.source_quality for e in claim.evidence_chain]
            
            # Evidence diversity (different domains)
            domains = set(e.domain for e in claim.evidence_chain if e.domain)
            domain_diversity = len(domains) / max(1, len(KnowledgeDomain))
            
            # Contradiction penalty
            contradictory_count = sum(1 for e in claim.evidence_chain if e.contradictory)
            contradiction_penalty = contradictory_count / len(claim.evidence_chain)
            
            # Overall evidence score
            base_score = np.mean(strengths)
            reliability_bonus = (np.mean(reliabilities) - 0.5) * 0.2
            source_bonus = (np.mean(source_qualities) - 0.5) * 0.1
            diversity_bonus = domain_diversity * 0.1
            
            evidence_score = max(0.0, min(1.0, 
                base_score + reliability_bonus + source_bonus + diversity_bonus - contradiction_penalty
            ))
            
            return {
                'evidence_score': evidence_score,
                'evidence_count': len(claim.evidence_chain),
                'quality_factors': {
                    'base_score': base_score,
                    'reliability_bonus': reliability_bonus,
                    'source_bonus': source_bonus,
                    'diversity_bonus': diversity_bonus,
                    'contradiction_penalty': contradiction_penalty,
                    'domain_diversity': domain_diversity
                },
                'processing_time': time.monotonic() - start_time
            }
            
        except Exception as e:
            logger.error(f"Evidence analysis failed: {str(e)}")
            return {
                'evidence_score': 0.5,
                'evidence_count': len(claim.evidence_chain),
                'error': str(e),
                'processing_time': time.monotonic() - start_time
            }

    # --------------------------
    # METACOGNITIVE ANALYTICS COMPONENT
    # --------------------------
    async def _metacognitive_assessment(self, claim: UniversalClaim) -> Dict:
        """Enhanced metacognitive assessment"""
        start_time = time.monotonic()
        
        try:
            biases_detected = []
            
            # Confirmation bias detection
            if claim.evidence_chain:
                supporting = sum(1 for e in claim.evidence_chain if not e.contradictory)
                contradicting = sum(1 for e in claim.evidence_chain if e.contradictory)
                if supporting > 0 and contradicting == 0:
                    biases_detected.append("potential_confirmation_bias")
            
            # Availability bias (recent evidence weighted more)
            recent_evidence = sum(1 for e in claim.evidence_chain 
                                if (datetime.utcnow() - e.timestamp).days < 30)
            if recent_evidence / max(1, len(claim.evidence_chain)) > 0.8:
                biases_detected.append("potential_availability_bias")
            
            # Calculate overall quality
            complexity_factor = len(claim.sub_domains) / max(1, len(KnowledgeDomain))
            reasoning_diversity = len(claim.reasoning_modes) / max(1, len(ReasoningMode))
            
            overall_quality = (
                0.4 * (1.0 - len(biases_detected) / 5) +  # Bias penalty
                0.3 * complexity_factor +                   # Domain complexity
                0.3 * reasoning_diversity                   # Reasoning diversity
            )
            
            return {
                'overall_quality': max(0.0, min(1.0, overall_quality)),
                'detected_biases': biases_detected,
                'bias_score': len(biases_detected) / 5,
                'complexity_factor': complexity_factor,
                'reasoning_diversity': reasoning_diversity,
                'processing_time': time.monotonic() - start_time
            }
            
        except Exception as e:
            logger.error(f"Metacognitive assessment failed: {str(e)}")
            return {
                'overall_quality': 0.5,
                'detected_biases': [],
                'error': str(e),
                'processing_time': time.monotonic() - start_time
            }

    # --------------------------
    # COMPLEXITY ANALYTICS COMPONENT
    # --------------------------
    async def _analyze_claim_complexity(self, claim: UniversalClaim) -> Dict:
        """Enhanced complexity analysis"""
        start_time = time.monotonic()
        
        try:
            # Text complexity (simplified)
            content_length = len(claim.content)
            word_count = len(claim.content.split())
            
            # Domain complexity
            domain_complexity = len(claim.sub_domains) / len(KnowledgeDomain)
            
            # Evidence complexity
            evidence_complexity = len(claim.evidence_chain) / 10  # Normalized to 10 pieces
            
            # Reasoning complexity
            reasoning_complexity = len(claim.reasoning_modes) / len(ReasoningMode)
            
            # Causal complexity
            causal_complexity = len(claim.causal_mechanisms) / 5  # Normalized to 5 mechanisms
            
            # Overall complexity
            overall_complexity = np.mean([
                min(1.0, content_length / 1000),  # Text length factor
                domain_complexity,
                evidence_complexity,
                reasoning_complexity,
                causal_complexity
            ])
            
            return {
                'overall_complexity': overall_complexity,
                'complexity_factors': {
                    'content_length': content_length,
                    'word_count': word_count,
                    'domain_complexity': domain_complexity,
                    'evidence_complexity': evidence_complexity,
                    'reasoning_complexity': reasoning_complexity,
                    'causal_complexity': causal_complexity
                },
                'processing_time': time.monotonic() - start_time
            }
            
        except Exception as e:
            logger.error(f"Complexity analysis failed: {str(e)}")
            return {
                'overall_complexity': 0.5,
                'error': str(e),
                'processing_time': time.monotonic() - start_time
            }

    # --------------------------
    # CROSS-DOMAIN ANALYTICS COMPONENT
    # --------------------------
    def _assess_cross_domain_coherence(self, claim: UniversalClaim) -> float:
        """Assess coherence across knowledge domains"""
        try:
            if len(claim.sub_domains) <= 1:
                return 0.8  # Single domain claims are generally coherent
            
            # Known conflicting domain pairs
            conflicting_pairs = [
                (KnowledgeDomain.SCIENCE, KnowledgeDomain.PHILOSOPHY),
                (KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY),
                (KnowledgeDomain.MEDICINE, KnowledgeDomain.PHILOSOPHY)
            ]
            
            # Check for domain conflicts
            domain_set = set(claim.sub_domains)
            conflict_count = 0
            for pair in conflicting_pairs:
                if pair[0] in domain_set and pair[1] in domain_set:
                    conflict_count += 1
            
            # Domain diversity bonus
            domain_diversity = len(domain_set) / len(KnowledgeDomain)
            
            # Calculate coherence score
            base_coherence = 0.7
            conflict_penalty = conflict_count * 0.15
            diversity_bonus = domain_diversity * 0.1
            
            return max(0.3, min(0.95, base_coherence - conflict_penalty + diversity_bonus))
            
        except Exception as e:
            logger.error(f"Cross-domain coherence assessment failed: {str(e)}")
            return 0.5

    # --------------------------
    # VALIDATION CORE COMPONENT
    # --------------------------
    def _calculate_overall_validity(self, components: Dict, mcp_results: Dict) -> float:
        """Calculate comprehensive overall validity score"""
        try:
            weights = {
                'reasoning': 0.25,
                'evidence': 0.25,
                'metacognitive': 0.15,
                'cross_domain': 0.1,
                'complexity': 0.05,
                'mcp_consensus': 0.2
            }
            
            # Extract component scores
            reasoning_score = components['reasoning_results'].get('bayesian', {}).get('posterior', 0.5) or \
                             components['reasoning_results'].get('causal', {}).get('causal_coherence', 0.5) or 0.5
            evidence_score = components['evidence_analysis'].get('evidence_score', 0.5)
            meta_score = components['metacognitive_assessment'].get('overall_quality', 0.5)
            cross_domain_score = components['cross_domain_coherence']
            complexity_score = 0.5  # Complexity doesn't directly affect validity
            
            # Apply mcp consensus with reliability weighting
            mcp_score = mcp_results.get('consensus_score', 0.5)
            mcp_reliability = mcp_results.get('reliability', 0.0)
            adjusted_mcp = mcp_reliability * mcp_score + (1 - mcp_reliability) * 0.5
            
            # Calculate weighted sum
            weighted_sum = (
                weights['reasoning'] * reasoning_score +
                weights['evidence'] * evidence_score +
                weights['metacognitive'] * meta_score +
                weights['cross_domain'] * cross_domain_score +
                weights['complexity'] * complexity_score +
                weights['mcp_consensus'] * adjusted_mcp
            )
            
            # Apply bias penalty
            bias_penalty = min(0.15, len(components['metacognitive_assessment'].get('detected_biases', [])) * 0.05)
            final_score = max(0.0, min(1.0, weighted_sum - bias_penalty))
            
            return final_score
            
        except Exception as e:
            logger.error(f"Validity calculation failed: {str(e)}")
            return 0.5

    def _calculate_confidence_intervals(self, validity_score: float, evidence_count: int) -> Dict:
        """Calculate confidence intervals based on validity score and evidence"""
        try:
            # Base interval range based on evidence count
            if evidence_count == 0:
                base_range = 0.4
            elif evidence_count < 3:
                base_range = 0.3
            elif evidence_count < 5:
                base_range = 0.2
            elif evidence_count < 10:
                base_range = 0.15
            else:
                base_range = 0.1
            
            # Adjust based on score (higher scores have tighter intervals)
            range_adjustment = (1 - validity_score) * 0.1
            final_range = max(0.05, min(0.4, base_range + range_adjustment))
            
            lower_bound = max(0.0, validity_score - final_range/2)
            upper_bound = min(1.0, validity_score + final_range/2)
            
            return {
                "lower_bound": lower_bound,
                "upper_bound": upper_bound,
                "range": final_range,
                "evidence_count": evidence_count
            }
            
        except Exception as e:
            logger.error(f"Confidence interval calculation failed: {str(e)}")
            return {
                "lower_bound": max(0.0, validity_score - 0.2),
                "upper_bound": min(1.0, validity_score + 0.2),
                "range": 0.4,
                "error": str(e)
            }

    def _generate_enhancement_recommendations(self, claim: UniversalClaim, results: Dict) -> List[str]:
        """Generate intelligent enhancement recommendations"""
        recommendations = []
        
        # Evidence-related recommendations
        evidence_analysis = results.get('evidence_analysis', {})
        if evidence_analysis.get('evidence_count', 0) < 3:
            recommendations.append("Add more supporting evidence from diverse sources")
        
        if evidence_analysis.get('quality_factors', {}).get('contradiction_penalty', 0) > 0.1:
            recommendations.append("Address contradictory evidence or explain inconsistencies")
            
        if evidence_analysis.get('quality_factors', {}).get('domain_diversity', 0) < 0.3:
            recommendations.append("Include evidence from additional knowledge domains")
        
        # Reasoning-related recommendations
        reasoning_modes = claim.reasoning_modes
        if ReasoningMode.BAYESIAN not in reasoning_modes and evidence_analysis.get('evidence_count', 0) > 2:
            recommendations.append("Consider applying Bayesian reasoning to quantify evidence strength")
            
        if ReasoningMode.CAUSAL not in reasoning_modes and claim.causal_mechanisms:
            recommendations.append("Apply causal reasoning to better articulate causal mechanisms")
        
        # Metacognitive recommendations
        meta = results.get('metacognitive_assessment', {})
        if 'potential_confirmation_bias' in meta.get('detected_biases', []):
            recommendations.append("Actively seek contradictory evidence to avoid confirmation bias")
            
        if 'potential_availability_bias' in meta.get('detected_biases', []):
            recommendations.append("Include historical evidence to counter recent evidence bias")
        
        # Complexity recommendations
        complexity = results.get('complexity_analysis', {})
        if complexity.get('overall_complexity', 0) > 0.7:
            recommendations.append("Break down into simpler sub-claims for better validation")
        
        return recommendations

    def _store_validation_result(self, claim_id: str, report: Dict):
        """Store validation result in history"""
        entry = {
            "claim_id": claim_id,
            "timestamp": datetime.utcnow(),
            "report": report
        }
        
        self.validation_history.append(entry)
        
        # Maintain history size
        if len(self.validation_history) > self.max_history:
            self.validation_history.pop(0)

    def _get_system_load(self) -> Dict:
        """Get current system performance metrics"""
        try:
            return {
                "cpu_percent": psutil.cpu_percent(),
                "memory_percent": psutil.virtual_memory().percent,
                "disk_percent": psutil.disk_usage('/').percent,
                "process_memory": psutil.Process().memory_info().rss / (1024 * 1024)  # in MB
            }
        except Exception as e:
            logger.warning(f"Could not get system load: {str(e)}")
            return {"error": str(e)}

    async def validate_knowledge_claim(self, claim: UniversalClaim) -> Dict:
        """Comprehensive claim validation pipeline"""
        validation_start = time.monotonic()
        report = {"claim_id": claim.claim_id}
        
        try:
            # Execute validation components in parallel
            mcp_task = asyncio.create_task(self._get_mcp_consensus(claim))
            reasoning_task = asyncio.create_task(self._perform_reasoning_analysis(claim))
            evidence_task = asyncio.create_task(self._analyze_evidence_quality(claim))
            meta_task = asyncio.create_task(self._metacognitive_assessment(claim))
            complexity_task = asyncio.create_task(self._analyze_claim_complexity(claim))
            
            # Gather results
            mcp_results, reasoning_results, evidence_analysis, meta_assessment, complexity_analysis = await asyncio.gather(
                mcp_task, reasoning_task, evidence_task, meta_task, complexity_task
            )
            
            # Assess cross-domain coherence
            cross_domain_coherence = self._assess_cross_domain_coherence(claim)
            
            # Build intermediate report
            report = {
                "mcp_consensus": mcp_results,
                "reasoning_analysis": reasoning_results,
                "evidence_analysis": evidence_analysis,
                "metacognitive_assessment": meta_assessment,
                "cross_domain_coherence": cross_domain_coherence,
                "complexity_analysis": complexity_analysis
            }
            
            # Calculate overall validity
            overall_validity = self._calculate_overall_validity(
                {
                    'reasoning_results': reasoning_results,
                    'evidence_analysis': evidence_analysis,
                    'metacognitive_assessment': meta_assessment,
                    'cross_domain_coherence': cross_domain_coherence,
                    'complexity_analysis': complexity_analysis
                },
                mcp_results
            )
            
            # Calculate confidence intervals
            evidence_count = evidence_analysis.get('evidence_count', 0)
            confidence_intervals = self._calculate_confidence_intervals(overall_validity, evidence_count)
            
            # Generate recommendations
            all_validation_results = {
                'reasoning_results': reasoning_results,
                'evidence_analysis': evidence_analysis,
                'metacognitive_assessment': meta_assessment,
                'complexity_analysis': complexity_analysis
            }
            recommendations = self._generate_enhancement_recommendations(claim, all_validation_results)
            
            # System metrics
            total_processing_time = time.monotonic() - validation_start
            system_load = self._get_system_load()
            
            # Build comprehensive report
            report.update({
                "claim": claim.to_dict(),
                "overall_validity": overall_validity,
                "confidence_intervals": confidence_intervals,
                "validation_components": {
                    "reasoning_analysis": reasoning_results,
                    "evidence_analysis": evidence_analysis,
                    "metacognitive_assessment": meta_assessment,
                    "complexity_analysis": complexity_analysis,
                    "cross_domain_coherence": cross_domain_coherence,
                    "mcp_consensus": mcp_results
                },
                "enhancement_recommendations": recommendations,
                "system_metrics": {
                    "total_processing_time": total_processing_time,
                    "system_load": system_load,
                    "validation_timestamp": datetime.utcnow().isoformat(),
                    "cache_hits": 1 if mcp_results.get('cache_hit') else 0
                },
                "validation_metadata": {
                    "validator_version": "2.0.0",
                    "reasoning_modes_used": [m.value for m in claim.reasoning_modes],
                    "domains_analyzed": [d.value for d in claim.sub_domains],
                    "evidence_sources": len(claim.evidence_chain)
                }
            })
            
            # Determine final status
            if overall_validity >= 0.8:
                report["status"] = ValidationStatus.SUCCESS.value
            elif overall_validity >= 0.6:
                report["status"] = ValidationStatus.PARTIAL_SUCCESS.value
            else:
                report["status"] = ValidationStatus.FAILURE.value
            
            # Store result
            self._store_validation_result(claim.claim_id, report)
            
            logger.info(f"Validation completed for {claim.claim_id} in {total_processing_time:.2f}s with score {overall_validity:.3f}")
            
        except Exception as e:
            logger.exception(f"Critical error in validation: {str(e)}")
            report.update({
                "status": ValidationStatus.ERROR.value,
                "error": str(e),
                "partial_results": locals().get('validation_results', {}),
                "processing_time": time.monotonic() - validation_start
            })
        
        return report

    # --------------------------
    # TESTING COMPONENT
    # --------------------------
    def _initialize_test_cases(self) -> List[UniversalClaim]:
        """Initialize comprehensive test cases for validation"""
        test_cases = []
        
        # Scientific claim with strong evidence
        science_evidence = [
            Evidence("sci_001", 0.9, 0.95, domain=KnowledgeDomain.SCIENCE),
            Evidence("sci_002", 0.85, 0.9, domain=KnowledgeDomain.SCIENCE),
            Evidence("sci_003", 0.8, 0.88, domain=KnowledgeDomain.MATHEMATICS)
        ]
        
        science_claim = UniversalClaim(
            claim_id="test_science_001",
            content="The speed of light in vacuum is approximately 299,792,458 meters per second",
            evidence_chain=science_evidence,
            reasoning_modes=[ReasoningMode.DEDUCTIVE, ReasoningMode.BAYESIAN],
            sub_domains=[KnowledgeDomain.SCIENCE, KnowledgeDomain.MATHEMATICS],
            causal_mechanisms=["electromagnetic_wave_propagation", "spacetime_geometry"],
            expected_validity=0.95
        )
        test_cases.append(science_claim)
        
        # Philosophical claim with mixed evidence
        philosophy_evidence = [
            Evidence("phil_001", 0.6, 0.7, domain=KnowledgeDomain.PHILOSOPHY),
            Evidence("phil_002", 0.4, 0.6, contradictory=True, domain=KnowledgeDomain.PHILOSOPHY),
            Evidence("phil_003", 0.7, 0.75, domain=KnowledgeDomain.SOCIAL_SCIENCE)
        ]
        
        philosophy_claim = UniversalClaim(
            claim_id="test_philosophy_001",
            content="Free will is incompatible with determinism in all possible worlds",
            evidence_chain=philosophy_evidence,
            reasoning_modes=[ReasoningMode.DEDUCTIVE, ReasoningMode.ABDUCTIVE],
            sub_domains=[KnowledgeDomain.PHILOSOPHY, KnowledgeDomain.SOCIAL_SCIENCE],
            causal_mechanisms=["deterministic_causation", "agent_causation"],
            expected_validity=0.65
        )
        test_cases.append(philosophy_claim)
        
        # Medical claim with recent evidence
        medical_evidence = [
            Evidence("med_001", 0.85, 0.9, domain=KnowledgeDomain.MEDICINE),
            Evidence("med_002", 0.8, 0.85, domain=KnowledgeDomain.SCIENCE),
            Evidence("med_003", 0.75, 0.8, domain=KnowledgeDomain.MEDICINE,
                    timestamp=datetime.utcnow() - timedelta(days=10))
        ]
        
        medical_claim = UniversalClaim(
            claim_id="test_medical_001",
            content="Regular exercise reduces the risk of cardiovascular disease by approximately 30-35%",
            evidence_chain=medical_evidence,
            reasoning_modes=[ReasoningMode.BAYESIAN, ReasoningMode.CAUSAL],
            sub_domains=[KnowledgeDomain.MEDICINE, KnowledgeDomain.SCIENCE],
            causal_mechanisms=["improved_cardiac_output", "reduced_inflammation", "weight_management"],
            expected_validity=0.8
        )
        test_cases.append(medical_claim)
        
        return test_cases

    async def run_validation_tests(self) -> Dict:
        """Run comprehensive validation tests"""
        logger.info("Starting comprehensive validation tests")
        test_start = time.monotonic()
        
        results = {
            "test_summary": {
                "total_tests": len(self.test_cases),
                "passed": 0,
                "failed": 0,
                "errors": 0
            },
            "detailed_results": [],
            "performance_metrics": {}
        }
        
        for test_case in self.test_cases:
            try:
                logger.info(f"Testing claim: {test_case.claim_id}")
                validation_result = await self.validate_knowledge_claim(test_case)
                
                # Check if result matches expected validity
                actual_validity = validation_result.get("overall_validity", 0.0)
                expected_validity = test_case.expected_validity or 0.5
                
                # Allow 15% tolerance
                tolerance = 0.15
                passed = abs(actual_validity - expected_validity) <= tolerance
                
                test_result = {
                    "claim_id": test_case.claim_id,
                    "expected_validity": expected_validity,
                    "actual_validity": actual_validity,
                    "difference": abs(actual_validity - expected_validity),
                    "passed": passed,
                    "status": validation_result.get("status"),
                    "processing_time": validation_result.get("system_metrics", {}).get("total_processing_time", 0),
                    "recommendations_count": len(validation_result.get("enhancement_recommendations", []))
                }
                
                results["detailed_results"].append(test_result)
                
                if validation_result.get("status") == ValidationStatus.ERROR.value:
                    results["test_summary"]["errors"] += 1
                elif passed:
                    results["test_summary"]["passed"] += 1
                else:
                    results["test_summary"]["failed"] += 1
                    
            except Exception as e:
                logger.error(f"Test failed for {test_case.claim_id}: {str(e)}")
                results["test_summary"]["errors"] += 1
                results["detailed_results"].append({
                    "claim_id": test_case.claim_id,
                    "error": str(e),
                    "passed": False
                })
        
        total_test_time = time.monotonic() - test_start
        results["performance_metrics"] = {
            "total_test_time": total_test_time,
            "average_test_time": total_test_time / len(self.test_cases),
            "tests_per_second": len(self.test_cases) / total_test_time if total_test_time > 0 else 0,
            "cache_hit_rate": len([r for r in results["detailed_results"] 
                                 if "cache_hit" in str(r)]) / len(self.test_cases)
        }
        
        logger.info(f"Validation tests completed in {total_test_time:.2f}s")
        logger.info(f"Results: {results['test_summary']['passed']} passed, "
                   f"{results['test_summary']['failed']} failed, "
                   f"{results['test_summary']['errors']} errors")
        
        return results

    # --------------------------
    # ANALYTICS COMPONENT
    # --------------------------
    def get_validation_statistics(self) -> Dict:
        """Get comprehensive validation statistics"""
        if not self.validation_history:
            return {"message": "No validation history available"}
        
        try:
            # Extract validity scores
            validity_scores = []
            processing_times = []
            statuses = []
            
            for entry in self.validation_history:
                report = entry.get("report", {})
                if "overall_validity" in report:
                    validity_scores.append(report["overall_validity"])
                if "system_metrics" in report:
                    processing_times.append(
                        report["system_metrics"].get("total_processing_time", 0)
                    )
                statuses.append(report.get("status", "unknown"))
            
            # Calculate statistics
            stats = {
                "total_validations": len(self.validation_history),
                "validity_statistics": {
                    "mean": np.mean(validity_scores) if validity_scores else 0,
                    "median": np.median(validity_scores) if validity_scores else 0,
                    "std_dev": np.std(validity_scores) if validity_scores else 0,
                    "min": np.min(validity_scores) if validity_scores else 0,
                    "max": np.max(validity_scores) if validity_scores else 0
                },
                "performance_statistics": {
                    "mean_processing_time": np.mean(processing_times) if processing_times else 0,
                    "median_processing_time": np.median(processing_times) if processing_times else 0,
                    "total_processing_time": np.sum(processing_times) if processing_times else 0
                },
                "status_distribution": {
                    status: statuses.count(status) for status in set(statuses)
                },
                "cache_statistics": {
                    "cache_size": len(self.validation_cache),
                    "cache_hit_rate": len([r for r in self.validation_history 
                                         if r.get("report", {}).get("validation_components", {})
                                         .get("mcp_consensus", {}).get("cache_hit")]) / len(self.validation_history)
                }
            }
            
            return stats
            
        except Exception as e:
            logger.error(f"Error calculating statistics: {str(e)}")
            return {"error": str(e)}

    # --------------------------
    # DATA EXPORT COMPONENT
    # --------------------------
    def export_validation_history(self, format: str = "json") -> str:
        """Export validation history in specified format"""
        try:
            if format.lower() == "json":
                return json.dumps(self.validation_history, indent=2, default=str)
            elif format.lower() == "csv":
                # Convert to CSV-friendly format
                csv_data = []
                for entry in self.validation_history:
                    report = entry.get("report", {})
                    csv_row = {
                        "claim_id": entry.get("claim_id", ""),
                        "timestamp": entry.get("timestamp", ""),
                        "overall_validity": report.get("overall_validity", 0),
                        "status": report.get("status", ""),
                        "processing_time": report.get("system_metrics", {}).get("total_processing_time", 0),
                        "evidence_count": report.get("claim", {}).get("evidence_summary", {}).get("count", 0)
                    }
                    csv_data.append(csv_row)
                
                if csv_data:
                    import csv
                    import io
                    output = io.StringIO()
                    writer = csv.DictWriter(output, fieldnames=csv_data[0].keys())
                    writer.writeheader()
                    writer.writerows(csv_data)
                    return output.getvalue()
                else:
                    return "No validation history to export"
            else:
                return f"Unsupported format: {format}. Use 'json' or 'csv'"
                
        except Exception as e:
            logger.error(f"Error exporting validation history: {str(e)}")
            return f"Export error: {str(e)}"

    # --------------------------
    # MAINTENANCE COMPONENT
    # --------------------------
    def clear_cache(self):
        """Clear validation cache"""
        self.validation_cache.clear()
        logger.info("Validation cache cleared")

    def clear_history(self):
        """Clear validation history"""
        self.validation_history.clear()
        logger.info("Validation history cleared")

# --------------------------
# MAIN EXECUTION COMPONENT
# --------------------------
async def main():
    """Enhanced main function with comprehensive testing"""
    # Initialize the validator
    agi_validator = AdvancedGeneralIntelligence(
        mcp_enabled=True,
        mcp_timeout=15,
        max_history=100,
        cache_enabled=True
    )
    
    try:
        # Run comprehensive tests
        print("Running comprehensive validation tests...")
        test_results = await agi_validator.run_validation_tests()
        
        print(f"\nTest Results Summary:")
        print(f"Total Tests: {test_results['test_summary']['total_tests']}")
        print(f"Passed: {test_results['test_summary']['passed']}")
        print(f"Failed: {test_results['test_summary']['failed']}")
        print(f"Errors: {test_results['test_summary']['errors']}")
        print(f"Average Processing Time: {test_results['performance_metrics']['average_test_time']:.3f}s")
        
        # Create a custom claim for validation
        custom_evidence = [
            Evidence("custom_001", 0.85, 0.9, domain=KnowledgeDomain.TECHNOLOGY),
            Evidence("custom_002", 0.8, 0.85, domain=KnowledgeDomain.SCIENCE),
            Evidence("custom_003", 0.75, 0.8, domain=KnowledgeDomain.SOCIAL_SCIENCE)
        ]
        
        custom_claim = UniversalClaim(
            claim_id="custom_ai_claim",
            content="Artificial General Intelligence will be achieved within the next decade through scaling transformer architectures",
            evidence_chain=custom_evidence,
            reasoning_modes=[ReasoningMode.BAYESIAN, ReasoningMode.CAUSAL, ReasoningMode.INDUCTIVE],
            sub_domains=[KnowledgeDomain.TECHNOLOGY, KnowledgeDomain.SCIENCE, KnowledgeDomain.SOCIAL_SCIENCE],
            causal_mechanisms=["computational_scaling", "architectural_improvements", "data_availability"],
            expected_validity=0.7
        )
        
        print(f"\nValidating custom claim: {custom_claim.content[:50]}...")
        custom_result = await agi_validator.validate_knowledge_claim(custom_claim)
        
        print(f"Validation Result:")
        print(f"Overall Validity: {custom_result['overall_validity']:.3f}")
        print(f"Status: {custom_result['status']}")
        print(f"Confidence Interval: {custom_result['confidence_intervals']}")
        print(f"Processing Time: {custom_result['system_metrics']['total_processing_time']:.3f}s")
        
        print(f"\nEnhancement Recommendations:")
        for i, rec in enumerate(custom_result['enhancement_recommendations'], 1):
            print(f"{i}. {rec}")
        
        # Get validation statistics
        stats = agi_validator.get_validation_statistics()
        print(f"\nValidation Statistics:")
        print(f"Total Validations: {stats['total_validations']}")
        print(f"Mean Validity Score: {stats['validity_statistics']['mean']:.3f}")
        print(f"Mean Processing Time: {stats['performance_statistics']['mean_processing_time']:.3f}s")
        
    except Exception as e:
        logger.exception(f"Error in main execution: {str(e)}")
    
    finally:
        # Clean up resources
        await agi_validator.close()

if __name__ == "__main__":
    asyncio.run(main())