Create MAIN_CODE_FULL

MAIN_CODE_FULL

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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
+from copy import deepcopy
+from fastapi import FastAPI
+import uvicorn
+from fastapi.responses import JSONResponse, PlainTextResponse
+
+# Configure logging
+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")
+
+# --------------------------
+# ENUMERATIONS
+# --------------------------
+class ValidationStatus(Enum):
+    SUCCESS = "success"
+    PARTIAL_SUCCESS = "partial_success"
+    FAILURE = "failure"
+    ERROR = "error"
+    INSUFFICIENT_DATA = "insufficient_data"
+
+class ReasoningMode(Enum):
+    DEDUCTIVE = "deductive"
+    INDUCTIVE = "inductive"
+    ABDUCTIVE = "abductive"
+    BAYESIAN = "bayesian"
+    CAUSAL = "causal"
+
+class KnowledgeDomain(Enum):
+    SCIENCE = "science"
+    MATHEMATICS = "mathematics"
+    PHILOSOPHY = "philosophy"
+    HISTORY = "history"
+    MEDICINE = "medicine"
+    TECHNOLOGY = "technology"
+    SOCIAL_SCIENCE = "social_science"
+
+# --------------------------
+# DATA MODELS
+# --------------------------
+@dataclass
+class Evidence:
+    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):
+        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:
+        return self.strength * self.reliability * self.source_quality
+    
+    def to_dict(self) -> Dict:
+        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:
+    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):
+        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")
+        if not self.claim_id:
+            self.claim_id = self._generate_claim_id()
+    
+    def _generate_claim_id(self) -> str:
+        content_hash = hashlib.md5(self.content.encode()).hexdigest()
+        return f"claim_{content_hash[:12]}"
+    
+    @property
+    def evidence_summary(self) -> Dict:
+        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.evidence_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:
+        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
+        }
+
+@dataclass
+class RealTimeDataSource:
+    source_id: str
+    endpoint: str
+    domain: KnowledgeDomain
+    refresh_interval: int = 3600
+    last_updated: datetime = field(default_factory=datetime.utcnow)
+    reliability: float = 0.85
+    priority: int = 1
+
+    def needs_refresh(self) -> bool:
+        return (datetime.utcnow() - self.last_updated).total_seconds() > self.refresh_interval
+
+@dataclass
+class DomainConstraint:
+    domain: KnowledgeDomain
+    min_evidence: int = 3
+    min_reliability: float = 0.7
+    required_reasoning_modes: List[ReasoningMode] = field(default_factory=list)
+    complexity_factor: float = 1.0
+
+# --------------------------
+# CORE VALIDATOR
+# --------------------------
+class EnhancedAGIValidator:
+    def __init__(self, 
+                 mcp_enabled: bool = True, 
+                 mcp_timeout: int = 15, 
+                 max_history: int = 100,
+                 cache_enabled: bool = True,
+                 real_time_sources: List[RealTimeDataSource] = None,
+                 domain_constraints: Dict[KnowledgeDomain, DomainConstraint] = None):
+        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._session = None
+        self._mcp_failures = 0
+        
+        # Real-time data and domain constraints
+        self.real_time_sources = real_time_sources or self._default_real_time_sources()
+        self.domain_constraints = domain_constraints or self._default_domain_constraints()
+        self.data_cache = {}
+        
+        logger.info("Enhanced AGI Validator initialized")
+
+    # --------------------------
+    # HELPER METHODS
+    # --------------------------
+    def _default_real_time_sources(self) -> List[RealTimeDataSource]:
+        return [
+            RealTimeDataSource("scientific_journals", "https://api.sciencedirect.com/search", 
+                              KnowledgeDomain.SCIENCE, refresh_interval=86400),
+            RealTimeDataSource("medical_db", "https://api.medicalevidence.org/v1/claims", 
+                              KnowledgeDomain.MEDICINE, refresh_interval=3600),
+            RealTimeDataSource("historical_archive", "https://api.historydb.org/records", 
+                              KnowledgeDomain.HISTORY, refresh_interval=604800)
+        ]
+
+    def _default_domain_constraints(self) -> Dict[KnowledgeDomain, DomainConstraint]:
+        return {
+            KnowledgeDomain.MEDICINE: DomainConstraint(
+                min_evidence=5, min_reliability=0.85,
+                required_reasoning_modes=[ReasoningMode.CAUSAL, ReasoningMode.BAYESIAN],
+                complexity_factor=1.2),
+            KnowledgeDomain.SCIENCE: DomainConstraint(
+                min_evidence=3, min_reliability=0.75,
+                required_reasoning_modes=[ReasoningMode.DEDUCTIVE],
+                complexity_factor=1.0),
+            KnowledgeDomain.HISTORY: DomainConstraint(
+                min_evidence=2, min_reliability=0.65, complexity_factor=0.9)
+        }
+
+    def _get_cache_key(self, claim: UniversalClaim) -> str:
+        claim_data = claim.to_dict()
+        claim_json = json.dumps(claim_data, sort_keys=True)
+        return hashlib.sha256(claim_json.encode()).hexdigest()
+
+    @asynccontextmanager
+    async def _get_session(self):
+        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):
+        if self._session:
+            await self._session.close()
+            self._session = None
+
+    # --------------------------
+    # REAL-TIME DATA INTEGRATION
+    # --------------------------
+    async def _fetch_real_time_data(self, source: RealTimeDataSource, query: str) -> Dict:
+        cache_key = f"{source.source_id}_{hashlib.md5(query.encode()).hexdigest()}"
+        
+        if self.cache_enabled and cache_key in self.data_cache:
+            if not source.needs_refresh():
+                return self.data_cache[cache_key]
+        
+        try:
+            async with self._get_session() as session:
+                params = {"query": query, "limit": 5, "format": "json"}
+                headers = {"Accept": "application/json"}
+                
+                async with session.get(
+                    source.endpoint, params=params, headers=headers,
+                    timeout=source.refresh_interval/10
+                ) as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        result = {
+                            "data": data,
+                            "timestamp": datetime.utcnow(),
+                            "source": source.source_id
+                        }
+                        self.data_cache[cache_key] = result
+                        source.last_updated = datetime.utcnow()
+                        return result
+                    else:
+                        logger.warning(f"Data source {source.source_id} returned status {response.status}")
+                        return {"error": f"HTTP {response.status}", "source": source.source_id}
+                        
+        except asyncio.TimeoutError:
+            logger.warning(f"Data source {source.source_id} timed out")
+            return {"error": "timeout", "source": source.source_id}
+        except Exception as e:
+            logger.error(f"Error fetching from {source.source_id}: {str(e)}")
+            return {"error": str(e), "source": source.source_id}
+
+    async def _enrich_evidence_with_real_time_data(self, claim: UniversalClaim) -> UniversalClaim:
+        domain_sources = [
+            s for s in sorted(self.real_time_sources, key=lambda x: x.priority, reverse=True)
+            if any(d in claim.sub_domains for d in [s.domain])
+        ]
+        
+        if not domain_sources:
+            return claim
+        
+        tasks = [self._fetch_real_time_data(source, claim.content) for source in domain_sources]
+        results = await asyncio.gather(*tasks)
+        
+        new_evidence = []
+        for result in results:
+            if "error" in result:
+                continue
+                
+            evidence_strength = 0.7
+            evidence_reliability = result["source"].get("reliability", 0.8)
+            
+            new_evidence.append(Evidence(
+                evidence_id=f"rt_{result['source']}_{time.time_ns()}",
+                strength=evidence_strength,
+                reliability=evidence_reliability,
+                source_quality=0.9,
+                domain=next((s for s in self.real_time_sources if s.source_id == result["source"]), None).domain,
+                timestamp=datetime.utcnow()
+            ))
+        
+        claim.evidence_chain.extend(new_evidence)
+        return claim
+
+    # --------------------------
+    # DOMAIN CONSTRAINT HANDLING
+    # --------------------------
+    def _apply_domain_constraints(self, claim: UniversalClaim) -> Tuple[UniversalClaim, List[str]]:
+        constraint_violations = []
+        enhanced_claim = deepcopy(claim)
+        
+        for domain in claim.sub_domains:
+            constraint = self.domain_constraints.get(domain)
+            if not constraint:
+                continue
+                
+            domain_evidence = [e for e in claim.evidence_chain if e.domain == domain]
+            if len(domain_evidence) < constraint.min_evidence:
+                constraint_violations.append(
+                    f"Domain {domain.value} requires at least {constraint.min_evidence} evidence pieces"
+                )
+                
+            if domain_evidence:
+                avg_reliability = np.mean([e.reliability for e in domain_evidence])
+                if avg_reliability < constraint.min_reliability:
+                    constraint_violations.append(
+                        f"Domain {domain.value} requires minimum evidence reliability of {constraint.min_reliability}"
+                    )
+            
+            for mode in constraint.required_reasoning_modes:
+                if mode not in claim.reasoning_modes:
+                    enhanced_claim.reasoning_modes.append(mode)
+                    constraint_violations.append(
+                        f"Added required reasoning mode {mode.value} for domain {domain.value}"
+                    )
+        
+        return enhanced_claim, constraint_violations
+
+    # --------------------------
+    # MCP CONSENSUS COMPONENT
+    # --------------------------
+    async def _get_mcp_consensus(self, claim: UniversalClaim) -> Dict:
+        if not self.mcp_enabled:
+            logger.info("mCP consensus protocol disabled")
+            return self._get_fallback_consensus("mCP disabled")
+        
+        if self._mcp_failures >= 3:
+            logger.error("mCP circuit breaker triggered - using fallback")
+            return self._get_fallback_consensus("circuit_breaker")
+        
+        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
+                        }
+                        
+                        if cache_key:
+                            self.validation_cache[cache_key] = mcp_result
+                        
+                        logger.info(f"mCP consensus received in {elapsed:.2f}s")
+                        self._mcp_failures = 0
+                        return mcp_result
+                    else:
+                        logger.warning(f"mCP returned status {response.status}")
+                        self._mcp_failures += 1
+                        return self._get_fallback_consensus(f"HTTP {response.status}")
+                        
+        except asyncio.TimeoutError:
+            logger.warning("mCP request timed out")
+            self._mcp_failures += 1
+            return self._get_fallback_consensus("timeout")
+        except Exception as e:
+            logger.exception(f"Error in mCP request: {str(e)}")
+            self._mcp_failures += 1
+            return self._get_fallback_consensus(f"error: {str(e)}")
+
+    def _get_fallback_consensus(self, reason: str = "unknown") -> Dict:
+        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
+        }
+
+    # --------------------------
+    # ANALYTICAL COMPONENTS
+    # --------------------------
+    async def _perform_reasoning_analysis(self, claim: UniversalClaim) -> Dict:
+        start_time = time.monotonic()
+        
+        try:
+            results = {}
+            
+            # Bayesian reasoning
+            if ReasoningMode.BAYESIAN in claim.reasoning_modes:
+                prior = 0.5
+                evidence_weights = [e.weighted_strength for e in claim.evidence_chain]
+                if evidence_weights:
+                    likelihood = np.mean(evidence_weights)
+                    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:
+                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
+            }
+
+    async def _analyze_evidence_quality(self, claim: UniversalClaim) -> Dict:
+        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
+                }
+            
+            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]
+            
+            domains = set(e.domain for e in claim.evidence_chain if e.domain)
+            domain_diversity = len(domains) / max(1, len(KnowledgeDomain))
+            
+            contradictory_count = sum(1 for e in claim.evidence_chain if e.contradictory)
+            contradiction_penalty = contradictory_count / len(claim.evidence_chain)
+            
+            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
+            }
+
+    async def _metacognitive_assessment(self, claim: UniversalClaim) -> Dict:
+        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 = 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) +
+                0.3 * complexity_factor +
+                0.3 * 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 {
+                'error': f"Metacognitive assessment failed: {str(e)}",
+                'processing_time': time.monotonic() - start_time
+            }
+
+    def _calculate_dynamic_threshold(self, evidence_analysis: Dict, complexity_analysis: Dict) -> float:
+        try:
+            base_threshold = 0.6
+            evidence_score = evidence_analysis.get('evidence_score', 0.5)
+            evidence_count = evidence_analysis.get('evidence_count', 0)
+            contradiction_penalty = evidence_analysis.get('quality_factors', {}).get('contradiction_penalty', 0)
+            
+            complexity_score = complexity_analysis.get('overall_complexity', 0.5)
+            domain_complexity = complexity_analysis.get('complexity_factors', {}).get('domain_complexity', 0)
+            reasoning_complexity = complexity_analysis.get('complexity_factors', {}).get('reasoning_complexity', 0)
+            
+            evidence_factor = max(0.0, 0.2 * (0.7 - evidence_score))
+            count_factor = max(0.0, 0.15 * (1 - min(1.0, evidence_count / 5)))
+            contradiction_factor = min(0.2, contradiction_penalty * 0.3)
+            complexity_factor = min(0.25, complexity_score * 0.3)
+            
+            adjustment = evidence_factor + count_factor + contradiction_factor + complexity_factor
+            dynamic_threshold = base_threshold - adjustment
+            return max(0.3, min(0.8, dynamic_threshold))
+            
+        except Exception as e:
+            logger.error(f"Dynamic threshold calculation failed: {str(e)}")
+            return 0.6
+
+    # --------------------------
+    # CORE VALIDATION PIPELINE
+    # --------------------------
+    async def validate_knowledge_claim(self, claim: UniversalClaim) -> Dict:
+        try:
+            # Apply domain constraints
+            enhanced_claim, constraint_violations = self._apply_domain_constraints(claim)
+            
+            # Enhance with real-time data
+            enhanced_claim = await self._enrich_evidence_with_real_time_data(enhanced_claim)
+            
+            # Parallel processing of analytical components
+            evidence_task = self._analyze_evidence_quality(enhanced_claim)
+            reasoning_task = self._perform_reasoning_analysis(enhanced_claim)
+            metacog_task = self._metacognitive_assessment(enhanced_claim)
+            mcp_task = self._get_mcp_consensus(enhanced_claim)
+            
+            results = await asyncio.gather(
+                evidence_task, reasoning_task, metacog_task, mcp_task
+            )
+            evidence_analysis, reasoning_analysis, metacog_analysis, mcp_analysis = results
+            
+            # Dynamic threshold calculation
+            dynamic_threshold = self._calculate_dynamic_threshold(
+                evidence_analysis, metacog_analysis
+            )
+            
+            # Calculate overall validity
+            evidence_weight = 0.4
+            reasoning_weight = 0.3
+            mcp_weight = 0.2
+            metacog_weight = 0.1
+            
+            evidence_score = evidence_analysis.get('evidence_score', 0.0)
+            reasoning_score = reasoning_analysis.get('bayesian', {}).get('posterior', 0.5) if 'bayesian' in reasoning_analysis else 0.5
+            mcp_score = mcp_analysis.get('consensus_score', 0.5)
+            metacog_score = metacog_analysis.get('overall_quality', 0.5)
+            
+            overall_validity = (
+                evidence_weight * evidence_score +
+                reasoning_weight * reasoning_score +
+                mcp_weight * mcp_score +
+                metacog_weight * metacog_score
+            )
+            
+            # Determine validation status
+            status = ValidationStatus.FAILURE
+            if overall_validity >= dynamic_threshold:
+                status = ValidationStatus.SUCCESS if overall_validity >= 0.8 else ValidationStatus.PARTIAL_SUCCESS
+            elif evidence_analysis.get('evidence_count', 0) < 3:
+                status = ValidationStatus.INSUFFICIENT_DATA
+            
+            # Apply domain complexity adjustments
+            complexity_adjustment = 1.0
+            for domain in enhanced_claim.sub_domains:
+                if domain in self.domain_constraints:
+                    constraint = self.domain_constraints[domain]
+                    complexity_adjustment *= constraint.complexity_factor
+            overall_validity = min(1.0, overall_validity * complexity_adjustment)
+            
+            # Prepare report
+            report = {
+                "claim_id": enhanced_claim.claim_id,
+                "status": status.value,
+                "overall_validity": overall_validity,
+                "dynamic_threshold": dynamic_threshold,
+                "evidence_analysis": evidence_analysis,
+                "reasoning_analysis": reasoning_analysis,
+                "metacognitive_analysis": metacog_analysis,
+                "mcp_analysis": mcp_analysis,
+                "domain_constraints": {
+                    "constraint_violations": constraint_violations,
+                    "constraints_applied": [d.value for d in enhanced_claim.sub_domains 
+                                           if d in self.domain_constraints]
+                },
+                "timestamp": datetime.utcnow().isoformat()
+            }
+            
+            # Add to history
+            self.validation_history.append(report)
+            if len(self.validation_history) > self.max_history:
+                self.validation_history.pop(0)
+                
+            return report
+            
+        except Exception as e:
+            logger.exception(f"Validation failed: {str(e)}")
+            return await self._fallback_validation(claim, str(e))
+
+    async def _fallback_validation(self, claim: UniversalClaim, error: str) -> Dict:
+        try:
+            evidence_count = len(claim.evidence_chain)
+            evidence_score = np.mean([e.weighted_strength for e in claim.evidence_chain]) if evidence_count > 0 else 0.0
+            validity = min(0.9, max(0.1, evidence_score * 0.8))
+            
+            return {
+                "claim_id": claim.claim_id,
+                "status": ValidationStatus.ERROR.value,
+                "fallback_validity": validity,
+                "evidence_count": evidence_count,
+                "error": error,
+                "timestamp": datetime.utcnow().isoformat(),
+                "recommendations": [
+                    "System encountered an error - results are approximate",
+                    "Retry validation after system maintenance"
+                ]
+            }
+        except Exception as fallback_error:
+            logger.error(f"Fallback validation failed: {str(fallback_error)}")
+            return {
+                "claim_id": claim.claim_id,
+                "status": ValidationStatus.ERROR.value,
+                "error": f"Primary: {error}, Fallback: {str(fallback_error)}",
+                "timestamp": datetime.utcnow().isoformat()
+            }
+
+    # --------------------------
+    # ADDITIONAL FUNCTIONALITY
+    # --------------------------
+    def export_validation_history(self, format: str = "json") -> Union[Dict, str]:
+        if format == "json":
+            return self.validation_history
+        elif format == "csv":
+            csv_lines = ["claim_id,status,validity,timestamp"]
+            for entry in self.validation_history:
+                csv_lines.append(
+                    f"{entry['claim_id']},{entry['status']},{entry.get('overall_validity', 0.0)},{entry['timestamp']}"
+                )
+            return "\n".join(csv_lines)
+        else:
+            return str(self.validation_history)
+
+    def get_validation_statistics(self) -> Dict:
+        status_counts = {status.value: 0 for status in ValidationStatus}
+        validities = []
+        
+        for entry in self.validation_history:
+            status_counts[entry["status"]] += 1
+            if "overall_validity" in entry:
+                validities.append(entry["overall_validity"])
+        
+        return {
+            "total_validations": len(self.validation_history),
+            "status_distribution": status_counts,
+            "average_validity": np.mean(validities) if validities else 0.0,
+            "median_validity": np.median(validities) if validities else 0.0,
+            "last_validation": self.validation_history[-1] if self.validation_history else None
+        }
+
+# --------------------------
+# UI COMPONENT
+# --------------------------
+class AGIValidatorUI:
+    def __init__(self, validator: EnhancedAGIValidator):
+        self.validator = validator
+        self.app = FastAPI()
+        self._setup_routes()
+
+    def _setup_routes(self):
+        self.app.post("/validate")(self.validate_claim_endpoint)
+        self.app.get("/history")(self.get_history)
+        self.app.get("/stats")(self.get_statistics)
+
+    async def validate_claim_endpoint(self, claim_data: dict):
+        try:
+            claim = UniversalClaim(
+                claim_id=claim_data.get("claim_id", ""),
+                content=claim_data["content"],
+                evidence_chain=[
+                    Evidence(**e) for e in claim_data.get("evidence_chain", [])
+                ],
+                reasoning_modes=[ReasoningMode(m) for m in claim_data.get("reasoning_modes", [])],
+                sub_domains=[KnowledgeDomain(d) for d in claim_data.get("sub_domains", [])],
+                causal_mechanisms=claim_data.get("causal_mechanisms", []),
+                expected_validity=claim_data.get("expected_validity")
+            )
+            
+            result = await self.validator.validate_knowledge_claim(claim)
+            return JSONResponse(content=result)
+        except Exception as e:
+            return JSONResponse(
+                status_code=400,
+                content={"error": str(e)}
+            )
+
+    async def get_history(self, format: str = "json", limit: int = 10):
+        history = self.validator.validation_history[-limit:]
+        if format == "json":
+            return history
+        else:
+            return PlainTextResponse(self.validator.export_validation_history(format))
+
+    async def get_statistics(self):
+        return self.validator.get_validation_statistics()
+
+# --------------------------
+# MAIN EXECUTION ◉⃤
+# --------------------------
+async def main():
+    # Initialize with real-time data sources
+    real_time_sources = [
+        RealTimeDataSource(
+            "ai_research_db",
+            "https://api.ai-research.org/v1/validate",
+            KnowledgeDomain.TECHNOLOGY,
+            refresh_interval=1800
+        ),
+        RealTimeDataSource(
+            "climate_data",
+            "https://api.climate.gov/evidence",
+            KnowledgeDomain.SCIENCE,
+            priority=2
+        )
+    ]
+    
+    # Create enhanced validator
+    validator = EnhancedAGIValidator(
+        mcp_enabled=True,
+        real_time_sources=real_time_sources
+    )
+    
+    # Create UI service
+    ui = AGIValidatorUI(validator)
+    uvicorn.run(ui.app, host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    asyncio.run(main())