Structural inquiry system 2.5

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
STRUCTURAL INQUIRY SYSTEM v2.5
Engineering-Focused Knowledge Discovery with Concrete Improvements
"""

from enum import Enum
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple, Mapping, Callable
import hashlib
from datetime import datetime
from types import MappingProxyType
import numpy as np

# === CORE SYMBOLS ===
KNOWLEDGE_NODE = "●"
PATTERN_RECOGNITION = "⟁"
INQUIRY_MARKER = "?"
VALIDATION_SYMBOL = "✓"

# === KNOWLEDGE STATE TYPES ===

class KnowledgeStateType(Enum):
    """Knowledge state types with clear semantics"""
    PATTERN_DETECTION = "pattern_detection"
    DATA_CORRELATION = "data_correlation"
    CONTEXTUAL_ALIGNMENT = "contextual_alignment"
    METHODOLOGICAL_STRUCTURE = "methodological_structure"
    SOURCE_VERIFICATION = "source_verification"
    TEMPORAL_CONSISTENCY = "temporal_consistency"
    CROSS_DOMAIN_SYNTHESIS = "cross_domain_synthesis"
    KNOWLEDGE_GAP_IDENTIFICATION = "knowledge_gap_identification"

@dataclass(frozen=True)
class KnowledgeState:
    """Immutable knowledge state with provenance tracking"""
    state_id: str
    state_type: KnowledgeStateType
    confidence_score: float
    confidence_provenance: str  # Track where confidence came from
    methodological_rigor: float
    data_patterns: Tuple[float, ...]
    knowledge_domains: Tuple[str, ...]
    temporal_markers: Tuple[str, ...]
    research_constraints: Tuple[str, ...]
    structural_description: str
    validation_signature: str
    state_hash: str = field(init=False)
    
    def __post_init__(self):
        hash_input = f"{self.state_id}:{self.state_type.value}:{self.confidence_score}:"
        hash_input += f"{self.confidence_provenance}:{self.methodological_rigor}:"
        hash_input += ":".join(str(v) for v in self.data_patterns[:10])
        hash_input += ":".join(self.knowledge_domains)
        
        state_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32]
        object.__setattr__(self, 'state_hash', state_hash)

# === INQUIRY CATEGORIES ===

class InquiryCategory(Enum):
    """Inquiry categories with clear prioritization semantics"""
    CONFIDENCE_DISCREPANCY_ANALYSIS = "confidence_discrepancy_analysis"
    METHODOLOGICAL_CONSISTENCY_CHECK = "methodological_consistency_check"
    PATTERN_ANOMALY_DETECTION = "pattern_anomaly_detection"
    TEMPORAL_ALIGNMENT_VALIDATION = "temporal_alignment_validation"
    SOURCE_RELIABILITY_ASSESSMENT = "source_reliability_assessment"
    CROSS_REFERENCE_VALIDATION = "cross_reference_validation"
    KNOWLEDGE_COMPLETENESS_EVALUATION = "knowledge_completeness_evaluation"

# === PLUGGABLE ANALYSIS INTERFACE ===

class AnalysisResult:
    """Structured analysis result for inquiry generation"""
    def __init__(
        self,
        category: InquiryCategory,
        basis_code: str,
        basis_kwargs: Dict[str, Any],
        verification_requirements: List[str],
        investigation_confidence: float,
        research_completion_estimate: float,
        priority_score: float
    ):
        self.category = category
        self.basis_code = basis_code
        self.basis_kwargs = basis_kwargs
        self.verification_requirements = verification_requirements
        self.investigation_confidence = investigation_confidence
        self.research_completion_estimate = research_completion_estimate
        self.priority_score = priority_score

class InquiryAnalyzer:
    """Protocol for pluggable analysis"""
    def analyze(self, state: KnowledgeState) -> List[AnalysisResult]:
        """Analyze state and return multiple potential inquiries"""
        raise NotImplementedError

# === DEFAULT ANALYZER IMPLEMENTATION ===

class DefaultInquiryAnalyzer(InquiryAnalyzer):
    """Default analyzer that generates multiple inquiry candidates"""
    
    def __init__(self, basis_templates: Dict[str, Dict[str, Any]]):
        self.basis_templates = basis_templates
    
    def analyze(self, state: KnowledgeState) -> List[AnalysisResult]:
        """Generate multiple inquiry candidates from state"""
        results = []
        
        # Check multiple independent criteria
        if state.confidence_score < 0.7:
            results.append(self._confidence_analysis(state))
        
        if state.methodological_rigor < 0.65:
            results.append(self._methodological_analysis(state))
        
        if len(state.data_patterns) < 8:
            results.append(self._pattern_analysis(state))
        
        if len(state.temporal_markers) < 3:
            results.append(self._temporal_analysis(state))
        
        if len(state.knowledge_domains) > 2:
            results.append(self._cross_domain_analysis(state))
        
        # Always provide at least one analysis
        if not results:
            results.append(self._default_analysis(state))
        
        return results
    
    def _confidence_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Analyze confidence discrepancies"""
        confidence_factor = max(0.1, 0.8 - state.confidence_score)
        return AnalysisResult(
            category=InquiryCategory.CONFIDENCE_DISCREPANCY_ANALYSIS,
            basis_code="CONFIDENCE_ANOMALY_INVESTIGATION",
            basis_kwargs={
                "score": state.confidence_score * 100,
                "expected": 75.0,
                "provenance": state.confidence_provenance
            },
            verification_requirements=[
                "statistical_reanalysis",
                "source_review",
                "methodology_audit"
            ],
            investigation_confidence=confidence_factor,
            research_completion_estimate=self._calculate_completion_estimate(3, confidence_factor),
            priority_score=self._calculate_priority_score(confidence_factor, 0.9)
        )
    
    def _methodological_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Analyze methodological issues"""
        rigor_factor = max(0.1, 0.7 - state.methodological_rigor)
        return AnalysisResult(
            category=InquiryCategory.METHODOLOGICAL_CONSISTENCY_CHECK,
            basis_code="METHODOLOGICAL_CONSISTENCY_QUESTION",
            basis_kwargs={
                "rigor": state.methodological_rigor * 100,
                "method_type": "research_protocol"
            },
            verification_requirements=[
                "protocol_review",
                "reproducibility_check",
                "peer_validation"
            ],
            investigation_confidence=rigor_factor,
            research_completion_estimate=self._calculate_completion_estimate(3, rigor_factor),
            priority_score=self._calculate_priority_score(rigor_factor, 0.8)
        )
    
    def _pattern_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Analyze pattern anomalies"""
        pattern_factor = len(state.data_patterns) / 10.0
        return AnalysisResult(
            category=InquiryCategory.PATTERN_ANOMALY_DETECTION,
            basis_code="PATTERN_DEVIATION_ANALYSIS",
            basis_kwargs={
                "pattern_completeness": pattern_factor * 100,
                "expected_patterns": 8
            },
            verification_requirements=[
                "pattern_completeness_check",
                "data_collection_review",
                "statistical_validation"
            ],
            investigation_confidence=1.0 - pattern_factor,
            research_completion_estimate=self._calculate_completion_estimate(3, pattern_factor),
            priority_score=self._calculate_priority_score(1.0 - pattern_factor, 0.7)
        )
    
    def _temporal_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Analyze temporal issues"""
        temporal_factor = len(state.temporal_markers) / 3.0
        return AnalysisResult(
            category=InquiryCategory.TEMPORAL_ALIGNMENT_VALIDATION,
            basis_code="TEMPORAL_CONSISTENCY_CHECK",
            basis_kwargs={
                "marker_count": len(state.temporal_markers),
                "expected_markers": 3
            },
            verification_requirements=[
                "temporal_sequence_verification",
                "chronological_consistency_check"
            ],
            investigation_confidence=1.0 - temporal_factor,
            research_completion_estimate=self._calculate_completion_estimate(2, temporal_factor),
            priority_score=self._calculate_priority_score(1.0 - temporal_factor, 0.6)
        )
    
    def _cross_domain_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Analyze cross-domain issues"""
        domain_factor = min(1.0, len(state.knowledge_domains) / 5.0)
        return AnalysisResult(
            category=InquiryCategory.CROSS_REFERENCE_VALIDATION,
            basis_code="CROSS_DOMAIN_ALIGNMENT_CHECK",
            basis_kwargs={
                "domain_count": len(state.knowledge_domains),
                "domains": list(state.knowledge_domains)[:3]
            },
            verification_requirements=[
                "cross_domain_correlation",
                "independent_verification"
            ],
            investigation_confidence=domain_factor,
            research_completion_estimate=self._calculate_completion_estimate(2, domain_factor),
            priority_score=self._calculate_priority_score(domain_factor, 0.5)
        )
    
    def _default_analysis(self, state: KnowledgeState) -> AnalysisResult:
        """Default analysis for well-formed states"""
        return AnalysisResult(
            category=InquiryCategory.KNOWLEDGE_COMPLETENESS_EVALUATION,
            basis_code="BASELINE_VERIFICATION",
            basis_kwargs={
                "confidence_score": state.confidence_score * 100,
                "rigor_score": state.methodological_rigor * 100
            },
            verification_requirements=["comprehensive_review"],
            investigation_confidence=0.3,
            research_completion_estimate=0.9,
            priority_score=2.0  # Low priority baseline check
        )
    
    def _calculate_completion_estimate(self, requirement_count: int, confidence: float) -> float:
        """Calculate research completion estimate"""
        base = 0.5
        requirement_impact = 0.9 ** requirement_count
        confidence_impact = confidence * 0.4
        return min(0.95, base * requirement_impact + confidence_impact)
    
    def _calculate_priority_score(self, investigation_confidence: float, weight: float) -> float:
        """Calculate priority score with clear semantics"""
        base_score = investigation_confidence * weight
        return round(base_score * 10, 2)

# === INQUIRY BASIS TEMPLATES ===

INQUIRY_BASIS_TEMPLATES = {
    "CONFIDENCE_ANOMALY_INVESTIGATION": {
        "template": "Confidence score of {score}% ({provenance}) differs from expected baseline of {expected}%",
        "investigation_focus": "confidence_validation"
    },
    "METHODOLOGICAL_CONSISTENCY_QUESTION": {
        "template": "Methodological rigor rating of {rigor}% suggests review of {method_type} may be beneficial",
        "investigation_focus": "methodological_review"
    },
    "PATTERN_DEVIATION_ANALYSIS": {
        "template": "Pattern completeness at {pattern_completeness}% with {expected_patterns} expected patterns",
        "investigation_focus": "pattern_analysis"
    },
    "TEMPORAL_CONSISTENCY_CHECK": {
        "template": "Temporal markers: {marker_count} present, {expected_markers} expected",
        "investigation_focus": "temporal_validation"
    },
    "CROSS_DOMAIN_ALIGNMENT_CHECK": {
        "template": "Cross-domain analysis across {domain_count} domains: {domains}",
        "investigation_focus": "cross_domain_validation"
    },
    "BASELINE_VERIFICATION": {
        "template": "Baseline verification: confidence={confidence_score}%, rigor={rigor_score}%",
        "investigation_focus": "comprehensive_review"
    }
}

# === INQUIRY ARTIFACT ===

@dataclass(frozen=True)
class InquiryArtifact:
    """Deterministic inquiry artifact with robust priority calculation"""
    artifact_id: str
    source_state_hash: str
    inquiry_category: InquiryCategory
    investigation_priority: int  # 1-10 scale with clear semantics
    knowledge_domains_involved: Tuple[str, ...]
    basis_code: str
    inquiry_description: str
    verification_requirements: Tuple[str, ...]
    investigation_confidence: float
    research_completion_estimate: float
    confidence_provenance: str
    artifact_hash: str
    creation_context: 'CreationContext'
    
    @classmethod
    def create(
        cls,
        knowledge_state: KnowledgeState,
        analysis_result: AnalysisResult,
        basis_templates: Dict[str, Dict[str, Any]],
        creation_context: 'CreationContext'
    ) -> 'InquiryArtifact':
        """Create inquiry artifact with deterministic hash"""
        
        # Format inquiry description
        template_data = basis_templates.get(analysis_result.basis_code, {})
        description_template = template_data.get("template", "Analysis required")
        inquiry_description = description_template.format(**analysis_result.basis_kwargs)
        
        # Calculate deterministic priority (1-10)
        priority_value = max(1, min(10, int(round(analysis_result.priority_score))))
        
        # Generate deterministic hash
        hash_input = f"{knowledge_state.state_hash}:{analysis_result.category.value}:"
        hash_input += f"{analysis_result.basis_code}:{priority_value}:"
        hash_input += ":".join(analysis_result.verification_requirements)
        hash_input += creation_context.context_hash
        
        artifact_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32]
        artifact_id = f"inq_{artifact_hash[:16]}"
        
        return cls(
            artifact_id=artifact_id,
            source_state_hash=knowledge_state.state_hash,
            inquiry_category=analysis_result.category,
            investigation_priority=priority_value,
            knowledge_domains_involved=knowledge_state.knowledge_domains,
            basis_code=analysis_result.basis_code,
            inquiry_description=inquiry_description,
            verification_requirements=tuple(analysis_result.verification_requirements),
            investigation_confidence=analysis_result.investigation_confidence,
            research_completion_estimate=analysis_result.research_completion_estimate,
            confidence_provenance=knowledge_state.confidence_provenance,
            artifact_hash=artifact_hash,
            creation_context=creation_context
        )
    
    def reference_information(self) -> Mapping[str, Any]:
        """Immutable reference information"""
        return MappingProxyType({
            "artifact_id": self.artifact_id,
            "source_state": self.source_state_hash[:12],
            "inquiry_category": self.inquiry_category.value,
            "investigation_priority": self.investigation_priority,
            "priority_semantics": self._priority_semantics(),
            "knowledge_domains": list(self.knowledge_domains_involved),
            "basis": {
                "code": self.basis_code,
                "description": self.inquiry_description,
                "confidence_provenance": self.confidence_provenance
            },
            "verification_requirements": list(self.verification_requirements),
            "investigation_confidence": round(self.investigation_confidence, 3),
            "research_completion_estimate": round(self.research_completion_estimate, 3),
            "artifact_hash": self.artifact_hash,
            "creation_context": self.creation_context.reference_data()
        })
    
    def _priority_semantics(self) -> str:
        """Document priority semantics"""
        if self.investigation_priority >= 9:
            return "critical_immediate_attention"
        elif self.investigation_priority >= 7:
            return "high_priority_review"
        elif self.investigation_priority >= 5:
            return "moderate_priority"
        elif self.investigation_priority >= 3:
            return "low_priority_backlog"
        else:
            return "informational_only"

# === CREATION CONTEXT ===

@dataclass(frozen=True)
class CreationContext:
    """Immutable creation context"""
    system_version: str
    generation_timestamp: str
    research_environment: str
    deterministic_seed: Optional[int]
    context_hash: str = field(init=False)
    
    def __post_init__(self):
        hash_input = f"{self.system_version}:{self.generation_timestamp}:"
        hash_input += f"{self.research_environment}:{self.deterministic_seed or 'none'}"
        
        context_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32]
        object.__setattr__(self, 'context_hash', context_hash)
    
    @classmethod
    def create(
        cls,
        research_environment: str = "knowledge_discovery_system",
        deterministic_seed: Optional[int] = None,
        clock_source: Callable[[], datetime] = datetime.now
    ) -> 'CreationContext':
        """Factory method with optional determinism"""
        return cls(
            system_version="structural_inquiry_v2.5",
            generation_timestamp=clock_source().isoformat(),
            research_environment=research_environment,
            deterministic_seed=deterministic_seed
        )
    
    def reference_data(self) -> Mapping[str, Any]:
        return MappingProxyType({
            "system_version": self.system_version,
            "generation_timestamp": self.generation_timestamp,
            "research_environment": self.research_environment,
            "deterministic_mode": self.deterministic_seed is not None,
            "context_hash": self.context_hash[:12]
        })

# === INQUIRY GENERATOR ===

class InquiryGenerator:
    """
    Deterministic inquiry generator with pluggable analysis
    """
    
    def __init__(
        self,
        analyzer: Optional[InquiryAnalyzer] = None,
        creation_context: Optional[CreationContext] = None,
        deterministic_seed: Optional[int] = None
    ):
        self.analyzer = analyzer or DefaultInquiryAnalyzer(INQUIRY_BASIS_TEMPLATES)
        self.creation_context = creation_context or CreationContext.create(
            deterministic_seed=deterministic_seed
        )
        self.generated_inquiries: List[InquiryArtifact] = []
        
        # Set deterministic seed if provided
        if deterministic_seed is not None:
            np.random.seed(deterministic_seed)
    
    def generate_inquiries(
        self,
        knowledge_states: Tuple[KnowledgeState, ...],
        confidence_threshold: float = 0.7
    ) -> Tuple[InquiryArtifact, ...]:
        """Generate inquiries from knowledge states"""
        
        inquiries = []
        
        for state in knowledge_states:
            # Use analyzer to get multiple potential inquiries
            analysis_results = self.analyzer.analyze(state)
            
            for result in analysis_results:
                # Only generate inquiries that meet threshold
                if result.investigation_confidence >= confidence_threshold:
                    inquiry = InquiryArtifact.create(
                        knowledge_state=state,
                        analysis_result=result,
                        basis_templates=INQUIRY_BASIS_TEMPLATES,
                        creation_context=self.creation_context
                    )
                    inquiries.append(inquiry)
                    self.generated_inquiries.append(inquiry)
        
        return tuple(inquiries)

# === RESEARCH SYSTEM INTERFACE ===

class ResearchSystem:
    """Abstract research system interface"""
    
    async def research(self, topic: str, **kwargs) -> Dict[str, Any]:
        """Conduct research on topic (must be implemented)"""
        raise NotImplementedError

# === INTEGRATED KNOWLEDGE DISCOVERY ===

class IntegratedKnowledgeDiscovery:
    """
    Integrated system with clear async boundaries and determinism
    """
    
    def __init__(
        self,
        research_system: ResearchSystem,
        deterministic_seed: Optional[int] = None
    ):
        """
        Initialize with concrete research system
        
        Args:
            research_system: Must implement ResearchSystem interface
            deterministic_seed: Optional seed for reproducible results
        """
        if not isinstance(research_system, ResearchSystem):
            raise TypeError("research_system must implement ResearchSystem interface")
        
        self.research_system = research_system
        self.deterministic_seed = deterministic_seed
        self.inquiry_generator = InquiryGenerator(deterministic_seed=deterministic_seed)
        self.discovery_history: List[Dict[str, Any]] = []
    
    async def conduct_research_with_inquiries(
        self,
        research_topic: str,
        confidence_threshold: float = 0.7,
        **research_kwargs
    ) -> Dict[str, Any]:
        """Conduct research and generate knowledge inquiries"""
        
        # 1. Conduct research using the provided system
        research_result = await self.research_system.research(research_topic, **research_kwargs)
        
        # 2. Convert to knowledge state
        knowledge_state = self._convert_to_knowledge_state(research_result)
        
        # 3. Generate inquiries
        knowledge_states = (knowledge_state,)
        inquiry_artifacts = self.inquiry_generator.generate_inquiries(
            knowledge_states,
            confidence_threshold
        )
        
        # 4. Create inquiry collection
        inquiry_collection = {
            "collection_id": f"inq_coll_{hashlib.sha256(knowledge_state.state_hash.encode()).hexdigest()[:16]}",
            "research_topic": research_topic,
            "knowledge_state_hash": knowledge_state.state_hash[:12],
            "inquiry_count": len(inquiry_artifacts),
            "generation_timestamp": datetime.utcnow().isoformat(),
            "confidence_threshold": confidence_threshold,
            "deterministic_mode": self.deterministic_seed is not None,
            "inquiries": [i.reference_information() for i in inquiry_artifacts]
        }
        
        # 5. Store and return
        self.discovery_history.append({
            "research_topic": research_topic,
            "research_result": research_result,
            "knowledge_state": knowledge_state,
            "inquiry_collection": inquiry_collection,
            "inquiry_artifacts": inquiry_artifacts
        })
        
        return {
            "research_topic": research_topic,
            "research_summary": {
                "confidence_score": research_result.get("confidence_score", 0.5),
                "methodological_rigor": research_result.get("methodological_rigor", 0.5),
                "domains": research_result.get("knowledge_domains", [])
            },
            "inquiry_generation": {
                "inquiries_generated": len(inquiry_artifacts),
                "inquiry_collection_id": inquiry_collection["collection_id"],
                "priority_distribution": self._summarize_priorities(inquiry_artifacts),
                "confidence_threshold_met": len(inquiry_artifacts) > 0
            }
        }
    
    def _convert_to_knowledge_state(
        self,
        research_result: Dict[str, Any]
    ) -> KnowledgeState:
        """Convert research result to knowledge state"""
        
        # Extract with provenance tracking
        confidence_score = research_result.get("confidence_score", 0.5)
        confidence_provenance = research_result.get(
            "confidence_provenance", 
            "derived_from_research"
        )
        
        # Determine state type
        if confidence_score < 0.6:
            state_type = KnowledgeStateType.SOURCE_VERIFICATION
        elif "pattern" in str(research_result.get("structural_description", "")).lower():
            state_type = KnowledgeStateType.PATTERN_DETECTION
        elif len(research_result.get("knowledge_domains", [])) > 2:
            state_type = KnowledgeStateType.CROSS_DOMAIN_SYNTHESIS
        else:
            state_type = KnowledgeStateType.DATA_CORRELATION
        
        # Generate patterns deterministically
        if self.deterministic_seed is not None:
            # Deterministic pattern generation
            pattern_seed = hash(f"{self.deterministic_seed}:{research_result.get('content_hash', '')}")
            np.random.seed(pattern_seed % (2**32))
            data_patterns = tuple(np.random.randn(8).tolist())
        else:
            # Use provided pattern or generate default
            provided_patterns = research_result.get("data_patterns", [])
            data_patterns = tuple(provided_patterns[:8]) if provided_patterns else tuple(np.sin(np.arange(8) * 0.785).tolist())
        
        # Generate structural description
        structural_description = self._generate_structural_description(research_result)
        
        # Generate validation signature
        validation_signature = hashlib.sha3_512(
            f"{research_result.get('content_hash', '')}:{self.deterministic_seed or 'stochastic'}".encode()
        ).hexdigest()[:32]
        
        return KnowledgeState(
            state_id=f"knowledge_state_{research_result.get('content_hash', 'unknown')[:12]}",
            state_type=state_type,
            confidence_score=confidence_score,
            confidence_provenance=confidence_provenance,
            methodological_rigor=research_result.get("methodological_rigor", 0.5),
            data_patterns=data_patterns,
            knowledge_domains=tuple(research_result.get("knowledge_domains", ["general"])),
            temporal_markers=(
                research_result.get("timestamp", ""),
                datetime.utcnow().isoformat()
            ),
            research_constraints=self._extract_constraints(research_result),
            structural_description=structural_description,
            validation_signature=validation_signature
        )
    
    def _generate_structural_description(
        self,
        research_result: Dict[str, Any]
    ) -> str:
        """Generate structural description"""
        components = []
        
        confidence = research_result.get("confidence_score", 0.5)
        provenance = research_result.get("confidence_provenance", "unstated")
        
        if confidence < 0.6:
            components.append(f"Low confidence ({confidence:.2f}) from {provenance}")
        elif confidence > 0.8:
            components.append(f"High confidence ({confidence:.2f}) from {provenance}")
        
        rigor = research_result.get("methodological_rigor", 0.5)
        if rigor < 0.6:
            components.append(f"Methodological rigor: {rigor:.2f}")
        
        domains = research_result.get("knowledge_domains", [])
        if len(domains) > 2:
            components.append(f"Cross-domain: {len(domains)} domains")
        
        if not components:
            components.append("Standard research structure")
        
        return f"{KNOWLEDGE_NODE} " + "; ".join(components)
    
    def _extract_constraints(
        self,
        research_result: Dict[str, Any]
    ) -> Tuple[str, ...]:
        """Extract research constraints"""
        constraints = []
        
        if research_result.get("confidence_score", 0) < 0.7:
            constraints.append("confidence_verification_needed")
        
        if research_result.get("methodological_rigor", 0) < 0.6:
            constraints.append("methodology_review_recommended")
        
        if not research_result.get("source_references", []):
            constraints.append("source_corroboration_required")
        
        if not constraints:
            constraints.append("standard_verification_protocol")
        
        return tuple(constraints)
    
    def _summarize_priorities(
        self,
        inquiry_artifacts: Tuple[InquiryArtifact, ...]
    ) -> Dict[str, Any]:
        """Summarize inquiry priorities with clear semantics"""
        if not inquiry_artifacts:
            return {"message": "No inquiries generated", "priority_levels": {}}
        
        priority_summary = {}
        for artifact in inquiry_artifacts:
            priority = artifact.investigation_priority
            if priority not in priority_summary:
                priority_summary[priority] = {
                    "count": 0,
                    "domains": set(),
                    "semantics": artifact._priority_semantics()
                }
            
            priority_summary[priority]["count"] += 1
            priority_summary[priority]["domains"].update(artifact.knowledge_domains_involved)
        
        # Convert sets to lists
        for priority in priority_summary:
            priority_summary[priority]["domains"] = list(priority_summary[priority]["domains"])
        
        return {
            "total_priorities": len(priority_summary),
            "highest_priority": max(priority_summary.keys()),
            "priority_distribution": priority_summary
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get system statistics"""
        total_inquiries = len(self.inquiry_generator.generated_inquiries)
        
        # Calculate category distribution
        category_counts = {}
        for inquiry in self.inquiry_generator.generated_inquiries:
            category = inquiry.inquiry_category.value
            category_counts[category] = category_counts.get(category, 0) + 1
        
        # Calculate average metrics
        if total_inquiries > 0:
            avg_confidence = np.mean([i.investigation_confidence for i in self.inquiry_generator.generated_inquiries])
            avg_priority = np.mean([i.investigation_priority for i in self.inquiry_generator.generated_inquiries])
        else:
            avg_confidence = 0.0
            avg_priority = 0.0
        
        return {
            "system": "Integrated Knowledge Discovery v2.5",
            "research_sessions": len(self.discovery_history),
            "total_inquiries_generated": total_inquiries,
            "category_distribution": category_counts,
            "average_investigation_confidence": round(float(avg_confidence), 3),
            "average_investigation_priority": round(float(avg_priority), 1),
            "deterministic_mode": self.deterministic_seed is not None,
            "engineering_properties": {
                "immutable_data_structures": True,
                "deterministic_hashes": True,
                "pluggable_analyzers": True,
                "clear_async_boundaries": True,
                "priority_semantics_documented": True
            }
        }

# === CONCRETE RESEARCH SYSTEM EXAMPLE ===

class ConcreteResearchSystem(ResearchSystem):
    """Example research system with proper async implementation"""
    
    def __init__(self, deterministic_seed: Optional[int] = None):
        self.deterministic_seed = deterministic_seed
        if deterministic_seed is not None:
            np.random.seed(deterministic_seed)
    
    async def research(self, topic: str, **kwargs) -> Dict[str, Any]:
        """Conduct research (simulated for example)"""
        # Simulate async research delay
        import asyncio
        await asyncio.sleep(0.1)  # Simulate network/processing
        
        # Generate deterministic or random results
        if self.deterministic_seed is not None:
            # Deterministic based on topic
            topic_hash = hash(topic) % 1000
            confidence = 0.5 + (topic_hash % 500) / 1000  # 0.5-1.0
            rigor = 0.4 + (topic_hash % 600) / 1000  # 0.4-1.0
        else:
            # Random results
            confidence = np.random.random() * 0.3 + 0.5  # 0.5-0.8
            rigor = np.random.random() * 0.4 + 0.4  # 0.4-0.8
        
        return {
            "topic": topic,
            "content_hash": hashlib.sha256(topic.encode()).hexdigest()[:32],
            "confidence_score": confidence,
            "confidence_provenance": "simulated_analysis",
            "methodological_rigor": rigor,
            "knowledge_domains": self._identify_domains(topic),
            "structural_description": f"Research on {topic}",
            "timestamp": datetime.utcnow().isoformat(),
            "data_patterns": np.sin(np.arange(10) * 0.628).tolist(),
            "source_references": [f"ref_{i}" for i in range(np.random.randint(1, 4))]
        }
    
    def _identify_domains(self, topic: str) -> List[str]:
        """Identify domains from topic"""
        domains = []
        topic_lower = topic.lower()
        
        if any(word in topic_lower for word in ["quantum", "physics"]):
            domains.append("physics")
        if any(word in topic_lower for word in ["history", "ancient"]):
            domains.append("history")
        if any(word in topic_lower for word in ["consciousness", "mind"]):
            domains.append("psychology")
        if any(word in topic_lower for word in ["pattern", "analysis"]):
            domains.append("mathematics")
        
        return domains if domains else ["interdisciplinary"]

# === TEST UTILITIES ===

def run_deterministic_test() -> bool:
    """Test deterministic reproducibility"""
    print("Testing deterministic reproducibility...")
    
    # Run with same seed
    research_system1 = ConcreteResearchSystem(deterministic_seed=42)
    system1 = IntegratedKnowledgeDiscovery(research_system1, deterministic_seed=42)
    
    research_system2 = ConcreteResearchSystem(deterministic_seed=42)
    system2 = IntegratedKnowledgeDiscovery(research_system2, deterministic_seed=42)
    
    import asyncio
    
    # Run same research
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    
    result1 = loop.run_until_complete(
        system1.conduct_research_with_inquiries("Test topic")
    )
    result2 = loop.run_until_complete(
        system2.conduct_research_with_inquiries("Test topic")
    )
    
    loop.close()
    
    # Compare results
    inquiries1 = result1["inquiry_generation"]["inquiries_generated"]
    inquiries2 = result2["inquiry_generation"]["inquiries_generated"]
    
    print(f"  System 1 inquiries: {inquiries1}")
    print(f"  System 2 inquiries: {inquiries2}")
    print(f"  Results identical: {inquiries1 == inquiries2}")
    
    return inquiries1 == inquiries2

# === MAIN ===

async def main():
    """Demonstrate the system"""
    print(f"""
    {'='*70}
    STRUCTURAL INQUIRY SYSTEM v2.5
    Engineering-Focused Knowledge Discovery
    {'='*70}
    """)
    
    # Run deterministic test
    if run_deterministic_test():
        print(f"\n{VALIDATION_SYMBOL} Deterministic reproducibility verified")
    else:
        print(f"\n{INQUIRY_MARKER} Non-deterministic behavior detected")
    
    # Create and run system
    research_system = ConcreteResearchSystem()
    discovery_system = IntegratedKnowledgeDiscovery(research_system)
    
    topics = [
        "Quantum pattern analysis techniques",
        "Historical methodology consistency",
        "Cross-domain verification protocols"
    ]
    
    for i, topic in enumerate(topics, 1):
        print(f"\n{PATTERN_RECOGNITION} RESEARCH SESSION {i}: {topic}")
        print(f"{'-'*60}")
        
        result = await discovery_system.conduct_research_with_inquiries(
            topic,
            confidence_threshold=0.6
        )
        
        inquiries = result["inquiry_generation"]["inquiries_generated"]
        priorities = result["inquiry_generation"]["priority_distribution"]
        
        print(f"  {VALIDATION_SYMBOL} Research completed")
        print(f"  {KNOWLEDGE_NODE} Inquiries generated: {inquiries}")
        
        if inquiries > 0:
            for priority, data in priorities.get("priority_distribution", {}).items():
                semantics = data.get("semantics", "unknown")
                print(f"    Priority {priority} ({semantics}): {data['count']} inquiries")
    
    # Display statistics
    stats = discovery_system.get_statistics()
    print(f"\n{'='*70}")
    print("SYSTEM STATISTICS")
    print(f"{'='*70}")
    
    print(f"\nResearch sessions: {stats['research_sessions']}")
    print(f"Total inquiries: {stats['total_inquiries_generated']}")
    print(f"\nEngineering properties:")
    for prop, value in stats["engineering_properties"].items():
        status = "✓" if value else "✗"
        print(f"  {status} {prop}: {value}")

if __name__ == "__main__":
    import asyncio
    
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print(f"\n\n{KNOWLEDGE_NODE} System shutdown complete.")