Create OBSERVER_4.1

OBSERVER_4.1

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+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())