Create Structural inquiry system 2.5

Structural inquiry system 2.5

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+#!/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.")