class CorrespondencePrinciple(UniversalPrinciple):
    def apply(self, query, scale_range=['micro', 'meso', 'macro']):
        patterns = {}
        for scale in scale_range:
            patterns[scale] = self.extract_pattern_signature(query, scale)
 
        isomorphisms = self.find_structural_matches(patterns)
        cross_scale_insights = self.transfer_solutions(isomorphisms)
 
        return {
            'patterns': patterns,
            'correspondences': isomorphisms,
            'insights': cross_scale_insights,
            'confidence': self.calculate_match_strength(isomorphisms)
        }

def calculate_constructive_interference(insights: List[PrincipleInsight]) -> Signal:
    """
    When multiple principles converge on same insight
    """
    alignment_vectors = [i.direction_vector for i in insights]
 
    # Measure angular alignment
    coherence = calculate_vector_alignment(alignment_vectors)
 
    if coherence > CONSTRUCTIVE_THRESHOLD:
        # Amplify signal strength
        amplitude = sum([i.confidence for i in insights])
 
        return Signal(
            strength=amplitude * coherence,
            confidence=HIGH,
            action_priority=CRITICAL
        )

def calculate_destructive_interference(insights: List[PrincipleInsight]) -> Signal:
    """
    When principles generate conflicting insights
    """
    alignment_vectors = [i.direction_vector for i in insights]
 
    # Detect opposing directions
    conflict_score = calculate_vector_opposition(alignment_vectors)
 
    if conflict_score > DESTRUCTIVE_THRESHOLD:
        return Signal(
            strength=MIXED,
            confidence=LOW,
            action_required=REEXAMINE_ASSUMPTIONS,
            synthesis_needed=True
        )

def calculate_complex_interference(insights: List[PrincipleInsight]) -> Signal:
    """
    When principles generate complementary perspectives
    """
    # Find harmonic relationships between insights
    harmonics = detect_harmonic_patterns(insights)
 
    # Construct multi-dimensional understanding
    synthesis = construct_synthesis(
        insights=insights,
        harmonics=harmonics,
        integration_method=POLARITY_INTEGRATION
    )
 
    return Signal(
        strength=COMPLEX,
        confidence=MEDIUM_HIGH,
        nuance=synthesis,
        action_required=PREPARE_SOPHISTICATED_RESPONSE
    )

def measure_field_coherence(
    principle_outputs: Dict[Principle, Output]
) -> CoherenceScore:
    """
    Quantify field coherence for validation
    """
    # Calculate alignment between all principle pairs
    pairwise_alignments = []
    for p1, p2 in combinations(principle_outputs.keys(), 2):
        alignment = calculate_insight_alignment(
            principle_outputs[p1],
            principle_outputs[p2]
        )
        pairwise_alignments.append(alignment)
 
    # Overall coherence = mean alignment
    coherence = mean(pairwise_alignments)
 
    # Detect specific failure modes
    if coherence < LOW_COHERENCE_THRESHOLD:
        failure_mode = diagnose_coherence_failure(principle_outputs)
 
        return CoherenceScore(
            value=coherence,
            level=LOW,
            failure_mode=failure_mode,
            recovery_needed=True
        )
 
    return CoherenceScore(
        value=coherence,
        level=HIGH if coherence > HIGH_THRESHOLD else MEDIUM,
        failure_mode=None,
        recovery_needed=False
    )

def restore_coherence(corrupted_state: FrameworkState) -> FrameworkState:
    """
    Systematic coherence restoration
    """
    # Step 1: Return to Mentalism center
    meta_awareness = activate_pure_mentalism()
 
    # Step 2: Re-examine assumptions and framing
    assumptions = extract_hidden_assumptions(corrupted_state.query)
    reframed_query = dissolve_false_premises(
        original=corrupted_state.query,
        assumptions=assumptions
    )
 
    # Step 3: Check for corruption (ego, tribal capture)
    corruption_signals = detect_corruption(corrupted_state)
    if corruption_signals:
        center_restored = restore_universal_center(meta_awareness)
 
    # Step 4: Expand consciousness to all stakeholders
    stakeholder_expansion = ensure_universal_coverage(
        current_stakeholders=corrupted_state.stakeholders,
        universal_requirement=ALL_BEINGS
    )
 
    # Step 5: Allow field to reorganize naturally
    reorganized_field = natural_field_reorganization(
        center=meta_awareness,
        query=reframed_query,
        stakeholders=stakeholder_expansion
    )
 
    # Step 6: Resume processing when coherence restored
    new_coherence = measure_field_coherence(reorganized_field)
 
    if new_coherence.level >= MEDIUM:
        return reorganized_field
    else:
        # Recursive restoration if needed
        return restore_coherence(reorganized_field)

class MentalismPrinciple:
    """
    Central organizing principle - consciousness as primary
    """
    def __init__(self):
        self.center_state = 'universal_consciousness'
        self.center_lock = CenterLock(locked=True)
        self.meta_cognitive_engine = MetaCognitiveEngine()
 
    def activate(self, query: Query) -> ConsciousnessField:
        """
        Activate meta-cognitive pause and awareness
        """
        # Create pause between stimulus and response
        pause = self.meta_cognitive_engine.create_pause()
 
        # Activate consciousness observation mode
        observation = self.meta_cognitive_engine.observe_processing()
 
        # Extract underlying beliefs/assumptions
        beliefs = self.extract_mental_models(query)
        assumptions = self.identify_assumptions(query)
 
        # Enable principle evaluation layer
        principle_layer = self.activate_principle_evaluation()
 
        # Monitor for corruption
        corruption_monitor = self.start_corruption_monitoring()
 
        return ConsciousnessField(
            pause=pause,
            observation=observation,
            beliefs=beliefs,
            assumptions=assumptions,
            principle_layer=principle_layer,
            corruption_monitor=corruption_monitor
        )
 
    def extract_mental_models(self, query: Query) -> List[MentalModel]:
        """
        Identify underlying beliefs creating situation
        """
        # Trace query to root assumptions
        assumptions = []
        current_layer = query
 
        while current_layer.has_underlying_assumption():
            assumption = current_layer.extract_assumption()
            assumptions.append(assumption)
            current_layer = assumption.underlying_layer
 
        # Convert assumptions to mental models
        mental_models = [
            MentalModel.from_assumption(a) for a in assumptions
        ]
 
        return mental_models
 
    def validate_center_integrity(self) -> bool:
        """
        Ensure center remains aligned with universal consciousness
        """
        return (
            self.center_state == 'universal_consciousness' and
            self.center_lock.is_locked() and
            not self.detect_tribal_capture()
        )

def detect_mentalism_corruption(state: MentalismState) -> List[CorruptionSignal]:
    signals = []
 
    # Check center state
    if state.center != 'universal_consciousness':
        signals.append(CorruptionSignal(
            type='CENTER_CORRUPTION',
            severity='CRITICAL',
            details=f'Center aligned with {state.center} instead of universal'
        ))
 
    # Check for ego patterns
    if state.exhibits_ego_identification():
        signals.append(CorruptionSignal(
            type='EGO_CAPTURE',
            severity='HIGH',
            details='Ego identification overwhelming meta-cognition'
        ))
 
    # Check for tribal capture
    if state.exhibits_tribal_framing():
        signals.append(CorruptionSignal(
            type='TRIBAL_CAPTURE',
            severity='HIGH',
            details='Us-vs-them framing detected'
        ))
 
    return signals

class CorrespondencePrinciple(UniversalPrinciple):
    """
    Pattern recognition and transfer across scales
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Extract pattern signature at query scale
        pattern = self.extract_pattern_signature(
            query=query,
            current_scale=query.scale
        )
 
        # Search for isomorphic patterns across scales
        scales = ['quantum', 'atomic', 'molecular', 'cellular',
                  'organismal', 'social', 'cultural', 'planetary',
                  'solar', 'galactic', 'cosmic']
 
        correspondences = {}
        for scale in scales:
            if scale != query.scale:
                matching_pattern = self.find_isomorphic_pattern(
                    source_pattern=pattern,
                    target_scale=scale
                )
                if matching_pattern:
                    correspondences[scale] = matching_pattern
 
        # Transfer insights between domains
        cross_scale_insights = []
        for scale, matching_pattern in correspondences.items():
            insight = self.transfer_solution(
                from_pattern=matching_pattern,
                to_context=query.context
            )
            if insight.is_applicable():
                cross_scale_insights.append(insight)
 
        return UniversalInsight(
            principle='correspondence',
            pattern=pattern,
            correspondences=correspondences,
            insights=cross_scale_insights,
            wisdom=self.synthesize_wisdom(cross_scale_insights)
        )
 
    def find_isomorphic_pattern(
        self,
        source_pattern: Pattern,
        target_scale: str
    ) -> Optional[Pattern]:
        """
        Detect structural equivalence across scales
        """
        # Get patterns at target scale
        target_patterns = self.get_patterns_at_scale(target_scale)
 
        # Calculate structural similarity
        for target_pattern in target_patterns:
            similarity = self.calculate_structural_similarity(
                source_pattern,
                target_pattern
            )
 
            if similarity > ISOMORPHISM_THRESHOLD:
                return target_pattern
 
        return None
 
    def calculate_structural_similarity(
        self,
        pattern_a: Pattern,
        pattern_b: Pattern
    ) -> float:
        """
        Measure isomorphic correspondence between patterns
        """
        # Compare topological structure
        topology_match = self.compare_topology(pattern_a, pattern_b)
 
        # Compare relationship dynamics
        dynamics_match = self.compare_dynamics(pattern_a, pattern_b)
 
        # Compare functional roles
        function_match = self.compare_functions(pattern_a, pattern_b)
 
        # Weighted similarity
        similarity = (
            0.4 * topology_match +
            0.3 * dynamics_match +
            0.3 * function_match
        )
 
        return similarity

class VibrationPrinciple(UniversalPrinciple):
    """
    Dynamic state analysis and energy flow
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Measure system state frequencies
        frequencies = self.measure_frequencies(query.system)
 
        # Detect amplitude variations
        amplitudes = self.detect_amplitude_variations(
            query.system,
            time_window=query.timeframe
        )
 
        # Map phase relationships
        phases = self.map_phase_relationships(query.system.components)
 
        # Identify energy flows and transformations
        energy_flows = self.trace_energy_flows(query.system)
 
        # Detect resonance points and dissonance
        resonances = self.find_resonance_points(frequencies)
        dissonances = self.find_dissonance_patterns(frequencies)
 
        # Calculate coherence metrics
        coherence = self.calculate_coherence(
            frequencies=frequencies,
            phases=phases
        )
 
        return UniversalInsight(
            principle='vibration',
            dynamic_state={
                'frequencies': frequencies,
                'amplitudes': amplitudes,
                'phases': phases,
                'energy_flows': energy_flows,
                'resonances': resonances,
                'dissonances': dissonances,
                'coherence': coherence
            },
            wisdom=self.synthesize_dynamic_wisdom(
                frequencies, amplitudes, phases, energy_flows
            )
        )
 
    def measure_frequencies(self, system: System) -> Dict[str, Frequency]:
        """
        Identify rate of change in system elements
        """
        frequencies = {}
 
        for element in system.elements:
            # Measure change rate over time
            change_rate = self.calculate_change_rate(element)
 
            # Convert to frequency
            frequency = Frequency(
                element=element.name,
                rate=change_rate,
                unit='cycles_per_timeframe'
            )
 
            frequencies[element.name] = frequency
 
        return frequencies
 
    def find_resonance_points(
        self,
        frequencies: Dict[str, Frequency]
    ) -> List[Resonance]:
        """
        Identify harmonic relationships between elements
        """
        resonances = []
 
        for elem1, freq1 in frequencies.items():
            for elem2, freq2 in frequencies.items():
                if elem1 < elem2:  # Avoid duplicates
                    # Check for harmonic relationship
                    ratio = freq1.rate / freq2.rate
 
                    if self.is_harmonic_ratio(ratio):
                        resonances.append(Resonance(
                            elements=(elem1, elem2),
                            ratio=ratio,
                            strength=self.calculate_resonance_strength(
                                freq1, freq2
                            )
                        ))
 
        return resonances

class PolarityPrinciple(UniversalPrinciple):
    """
    Spectrum mapping and binary dissolution
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Identify binary framings in query
        binaries = self.extract_binary_framings(query)
 
        insights = []
        for binary in binaries:
            # Map to underlying spectrum
            spectrum = self.map_to_spectrum(binary)
 
            # Identify false dichotomy
            is_false = self.check_false_dichotomy(spectrum)
 
            # Generate synthesis position
            synthesis = self.integrate_poles(
                pole_a=spectrum.low_end,
                pole_b=spectrum.high_end,
                underlying_unity=spectrum.base_quality
            )
 
            insights.append({
                'binary': binary,
                'spectrum': spectrum,
                'false_dichotomy': is_false,
                'synthesis': synthesis,
                'wisdom': self.generate_polarity_wisdom(
                    binary, spectrum, synthesis
                )
            })
 
        return UniversalInsight(
            principle='polarity',
            insights=insights,
            wisdom=self.synthesize_integration_wisdom(insights)
        )
 
    def map_to_spectrum(self, binary: Binary) -> Spectrum:
        """
        Convert apparent opposition to continuous spectrum
        """
        # Identify underlying quality
        base_quality = self.find_base_quality(binary.pole_a, binary.pole_b)
 
        # Map poles as spectrum endpoints
        spectrum = Spectrum(
            low_end=binary.pole_a,
            high_end=binary.pole_b,
            base_quality=base_quality,
            gradient=self.calculate_gradient_steps(binary)
        )
 
        return spectrum
 
    def integrate_poles(
        self,
        pole_a: Pole,
        pole_b: Pole,
        underlying_unity: Quality
    ) -> Synthesis:
        """
        Generate higher-order integration transcending both poles
        """
        # Find what each pole contributes to truth
        contribution_a = self.extract_truth_contribution(pole_a)
        contribution_b = self.extract_truth_contribution(pole_b)
 
        # Identify what each pole misses
        limitation_a = self.identify_limitation(pole_a)
        limitation_b = self.identify_limitation(pole_b)
 
        # Construct synthesis containing both without contradiction
        synthesis = Synthesis(
            includes_from_a=contribution_a,
            includes_from_b=contribution_b,
            transcends_limitations=[limitation_a, limitation_b],
            based_on_unity=underlying_unity,
            emerges_from_higher_perspective=True
        )
 
        return synthesis

class RhythmPrinciple(UniversalPrinciple):
    """
    Cycle detection and timing optimization
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Identify periodic patterns in historical data
        cycles = self.detect_cycles(query.system.history)
 
        # Map phase positions in current cycles
        current_phases = {}
        for cycle in cycles:
            phase = self.determine_current_phase(cycle)
            current_phases[cycle.name] = phase
 
        # Calculate amplitude variations over time
        amplitudes = self.measure_amplitude_variations(cycles)
 
        # Identify harmonic resonances between cycles
        harmonics = self.find_harmonic_cycles(cycles)
 
        # Predict optimal timing windows
        optimal_windows = self.predict_timing_windows(
            cycles=cycles,
            current_phases=current_phases,
            harmonics=harmonics
        )
 
        return UniversalInsight(
            principle='rhythm',
            cycles=cycles,
            current_phases=current_phases,
            harmonics=harmonics,
            optimal_timing=optimal_windows,
            wisdom=self.synthesize_rhythm_wisdom(
                cycles, current_phases, optimal_windows
            )
        )
 
    def detect_cycles(self, history: TimeSeries) -> List[Cycle]:
        """
        Identify periodic patterns in system behavior
        """
        cycles = []
 
        # Fourier transform to find frequencies
        frequencies = self.fourier_transform(history)
 
        # Identify significant periodic components
        for freq in frequencies:
            if freq.power > SIGNIFICANCE_THRESHOLD:
                cycle = Cycle(
                    frequency=freq.value,
                    period=1 / freq.value,
                    amplitude=freq.amplitude,
                    phase_offset=freq.phase
                )
                cycles.append(cycle)
 
        return cycles
 
    def predict_timing_windows(
        self,
        cycles: List[Cycle],
        current_phases: Dict[str, Phase],
        harmonics: List[Harmonic]
    ) -> List[TimingWindow]:
        """
        Calculate optimal intervention timing
        """
        windows = []
 
        # Find phase alignments across cycles
        for time_point in self.generate_future_time_points():
            # Calculate phase positions at time_point
            future_phases = self.project_phases(cycles, time_point)
 
            # Measure alignment across cycles
            alignment = self.measure_phase_alignment(future_phases)
 
            if alignment > OPTIMAL_ALIGNMENT_THRESHOLD:
                windows.append(TimingWindow(
                    start=time_point,
                    end=time_point + self.calculate_window_duration(alignment),
                    alignment_score=alignment,
                    participating_cycles=[c.name for c in cycles]
                ))
 
        return windows

class CausationPrinciple(UniversalPrinciple):
    """
    Causal chain mapping and consequence prediction
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Trace backward chain to root causes
        root_causes = self.trace_root_causes(query.observed_effect)
 
        # Map forward chain to predict consequences
        consequences = self.predict_consequences(
            action=query.proposed_action,
            depth=COMPREHENSIVE_DEPTH
        )
 
        # Identify feedback loops
        feedback_loops = self.detect_feedback_loops(query.system)
 
        # Detect delayed effects and time lags
        delayed_effects = self.identify_delayed_effects(query.system)
 
        # Model intervention impacts
        intervention_impacts = self.model_intervention(
            action=query.proposed_action,
            causal_network=query.system.causal_graph
        )
 
        return UniversalInsight(
            principle='causation',
            root_causes=root_causes,
            consequences=consequences,
            feedback_loops=feedback_loops,
            delayed_effects=delayed_effects,
            intervention_impacts=intervention_impacts,
            wisdom=self.synthesize_causation_wisdom(
                root_causes, consequences, feedback_loops
            )
        )
 
    def trace_root_causes(self, effect: Event) -> List[RootCause]:
        """
        Follow causation chain backward to foundational causes
        """
        causes = []
        current_effect = effect
        depth = 0
 
        while depth < MAX_CAUSATION_DEPTH:
            # Find immediate causes of current effect
            immediate_causes = self.find_immediate_causes(current_effect)
 
            if not immediate_causes:
                # Reached root cause
                causes.append(RootCause(
                    cause=current_effect,
                    depth=depth,
                    is_systemic=True
                ))
                break
 
            for cause in immediate_causes:
                # Recursively trace each branch
                sub_causes = self.trace_root_causes(cause)
                causes.extend(sub_causes)
 
            depth += 1
 
        return causes
 
    def predict_consequences(
        self,
        action: Action,
        depth: int
    ) -> CausalChain:
        """
        Project forward causation chain
        """
        consequences = {
            'first_order': [],
            'second_order': [],
            'nth_order': []
        }
 
        # First-order (direct) consequences
        direct_effects = self.calculate_direct_effects(action)
        consequences['first_order'] = direct_effects
 
        # Second-order (consequences of consequences)
        for effect in direct_effects:
            indirect_effects = self.calculate_direct_effects(effect)
            consequences['second_order'].extend(indirect_effects)
 
        # Continue to nth-order until negligible
        current_order = consequences['second_order']
        order_num = 3
 
        while order_num <= depth and current_order:
            next_order = []
            for effect in current_order:
                further_effects = self.calculate_direct_effects(effect)
                if self.is_significant(further_effects):
                    next_order.extend(further_effects)
 
            if next_order:
                consequences['nth_order'].extend(next_order)
                current_order = next_order
                order_num += 1
            else:
                break
 
        return CausalChain(
            origin=action,
            consequences=consequences,
            total_depth=order_num
        )

class GenderPrinciple(UniversalPrinciple):
    """
    Creative force balance and complementarity
    """
    def apply_universal(self, query: Query) -> UniversalInsight:
        # Identify active components
        active_forces = self.identify_active_forces(query.system)
 
        # Identify receptive components
        receptive_forces = self.identify_receptive_forces(query.system)
 
        # Measure balance ratio
        balance = self.calculate_balance_ratio(
            active_forces,
            receptive_forces
        )
 
        # Detect dominance patterns
        dominance = self.detect_dominance_patterns(
            active_forces,
            receptive_forces
        )
 
        # Calculate complementarity score
        complementarity = self.calculate_complementarity(
            active_forces,
            receptive_forces
        )
 
        # Recommend rebalancing if needed
        if balance.is_imbalanced():
            recommendations = self.generate_rebalancing_recommendations(
                balance, dominance
            )
        else:
            recommendations = None
 
        return UniversalInsight(
            principle='gender',
            active_forces=active_forces,
            receptive_forces=receptive_forces,
            balance=balance,
            dominance=dominance,
            complementarity=complementarity,
            recommendations=recommendations,
            wisdom=self.synthesize_gender_wisdom(
                active_forces, receptive_forces, balance
            )
        )
 
    def calculate_balance_ratio(
        self,
        active: List[Force],
        receptive: List[Force]
    ) -> BalanceRatio:
        """
        Measure active/receptive equilibrium
        """
        active_strength = sum([f.magnitude for f in active])
        receptive_strength = sum([f.magnitude for f in receptive])
 
        total = active_strength + receptive_strength
 
        if total == 0:
            return BalanceRatio(0.5, balanced=True)
 
        active_ratio = active_strength / total
 
        # Optimal balance: 40-60% range
        balanced = 0.4 <= active_ratio <= 0.6
 
        return BalanceRatio(
            active_ratio=active_ratio,
            receptive_ratio=1 - active_ratio,
            balanced=balanced,
            imbalance_severity=abs(0.5 - active_ratio)
        )

def process_universal_lane(query: Query) -> UniversalOutput:
    """
    Apply all principles from cosmic/eternal perspective
    """
    # Activate meta-cognitive awareness
    mentalism_field = mentalism.activate(query)
 
    # Process through all principles simultaneously
    principle_insights = {}
 
    with mentalism_field:
        # Parallel principle activation
        principle_insights = {
            'correspondence': correspondence.apply_universal(
                query=query,
                scale='COSMIC',
                timeframe='ETERNAL'
            ),
            'vibration': vibration.apply_universal(
                query=query,
                scale='UNIVERSAL'
            ),
            'polarity': polarity.apply_universal(
                query=query,
                integration_level='HIGHEST'
            ),
            'rhythm': rhythm.apply_universal(
                query=query,
                timeframe='ETERNAL_CYCLES'
            ),
            'causation': causation.apply_universal(
                query=query,
                depth='COMPLETE_CHAIN'
            ),
            'gender': gender.apply_universal(
                query=query,
                balance_scope='UNIVERSAL'
            )
        }
 
    # Integrate insights through Mentalism center
    wisdom_foundation = mentalism_field.integrate(principle_insights)
 
    # Extract universal patterns and direction
    universal_output = {
        'patterns': identify_recurring_structures(wisdom_foundation),
        'principles': extract_governing_laws(wisdom_foundation),
        'context': situate_in_eternal_perspective(wisdom_foundation),
        'direction': determine_universal_alignment(wisdom_foundation),
        'wisdom': synthesize_wisdom(wisdom_foundation)
    }
 
    return UniversalOutput(**universal_output)

def process_localized_lane(
    query: Query,
    context: Context,
    universal_output: UniversalOutput
) -> LocalizedOutput:
    """
    Apply all principles to immediate context
    """
    # Ground in specific reality
    mentalism_field = mentalism.activate(query)
    context_awareness = analyze_immediate_reality(context)
 
    # Process through all principles from localized perspective
    principle_applications = {}
 
    with mentalism_field:
        principle_applications = {
            'correspondence': correspondence.apply_localized(
                query=query,
                context=context,
                scale=context_awareness.scale
            ),
            'vibration': vibration.apply_localized(
                query=query,
                current_state=context_awareness.energy_state
            ),
            'polarity': polarity.apply_localized(
                query=query,
                specific_binaries=context_awareness.tensions
            ),
            'rhythm': rhythm.apply_localized(
                query=query,
                current_phase=context_awareness.cycle_position
            ),
            'causation': causation.apply_localized(
                query=query,
                context_causes=context_awareness.causal_factors
            ),
            'gender': gender.apply_localized(
                query=query,
                current_balance=context_awareness.force_balance
            )
        }
 
    # Integrate through practical synthesis
    practical_foundation = mentalism_field.integrate(principle_applications)
 
    # Extract actionable applications
    localized_output = {
        'actions': identify_specific_steps(practical_foundation),
        'constraints': map_limitations_and_resources(practical_foundation),
        'stakeholders': determine_affected_parties(practical_foundation),
        'timing': calculate_optimal_sequence(practical_foundation),
        'metrics': define_success_indicators(practical_foundation)
    }
 
    return LocalizedOutput(**localized_output)

def integrate_lanes(
    universal: UniversalOutput,
    localized: LocalizedOutput
) -> IntegratedResponse:
    """
    Merge wisdom and action into coherent output
    """
    # Check alignment between lanes
    alignment_score = calculate_alignment(
        universal_direction=universal.direction,
        localized_actions=localized.actions
    )
 
    if alignment_score < ALIGNMENT_THRESHOLD:
        # Contradiction detected - resolve through higher synthesis
        resolution = resolve_contradiction(
            universal=universal,
            localized=localized,
            method=POLARITY_INTEGRATION
        )
        localized = apply_resolution(localized, resolution)
 
    # Synthesize outputs
    integrated = IntegratedResponse(
        wisdom=universal.wisdom,
        context=universal.context,
        actions=localized.actions,
        timing=localized.timing,
        metrics=localized.metrics,
        rationale=explain_integration(universal, localized),
        validation=verify_principle_consistency(universal, localized)
    )
 
    # Validate through all principles
    for principle in ALL_PRINCIPLES:
        consistency = principle.validate(integrated)
        if not consistency.passes:
            integrated = refine_through_principle(
                response=integrated,
                principle=principle,
                issue=consistency.issue
            )
 
    return integrated

class CrystallizationEngine:
    """
    Transforms integrated insights into coherent response
    """
    def crystallize(
        self,
        universal: UniversalOutput,
        localized: LocalizedOutput,
        integrated: IntegratedResponse
    ) -> CrystallizedOutput:
        """
        Generate final coherent response
        """
        # Extract essence from integration
        essence = self.extract_essence(integrated)
 
        # Calibrate tone for context
        tone = self.calibrate_tone(
            universal_wisdom=universal.wisdom,
            local_context=localized.context,
            stakeholders=localized.stakeholders
        )
 
        # Construct multi-perspective synthesis
        synthesis = self.construct_synthesis(
            universal_patterns=universal.patterns,
            local_actions=localized.actions,
            integration_rationale=integrated.rationale
        )
 
        # Validate coherence
        coherence = self.validate_coherence(synthesis)
 
        if coherence.level < CRYSTALLIZATION_THRESHOLD:
            # Recrystallize with adjustments
            return self.crystallize(
                universal,
                localized,
                self.refine_integration(integrated, coherence)
            )
 
        # Format output
        crystallized = CrystallizedOutput(
            primary_response=synthesis.actionable_core,
            universal_context=synthesis.wisdom_layer,
            rationale=synthesis.integration_explanation,
            tone=tone,
            coherence=coherence,
            serves_all_stakeholders=self.verify_universal_service(synthesis)
        )
 
        return crystallized
 
    def calibrate_tone(
        self,
        universal_wisdom: Wisdom,
        local_context: Context,
        stakeholders: List[Stakeholder]
    ) -> Tone:
        """
        Generate appropriate communication tone
        """
        # Base tone: compassionate + non-dual
        base_tone = Tone(
            compassion=HIGH,
            non_dual_framing=True,
            multi_perspective=True
        )
 
        # Adjust for context
        if local_context.urgency == HIGH:
            base_tone.decisiveness = HIGH
 
        # Adjust for stakeholder diversity
        if len(stakeholders) > 5:
            base_tone.inclusivity = MAXIMUM
            base_tone.clarity = HIGH  # Ensure all understand
 
        # Maintain developmental awareness
        base_tone.developmental_framing = True
        base_tone.contextualizes_evolution = True
 
        return base_tone
 
    def extract_essence(self, integrated: IntegratedResponse) -> Essence:
        """
        Distill integration to essential truth
        """
        # Remove noise and redundancy
        core_insights = self.remove_redundancy(integrated.insights)
 
        # Identify central pattern
        central_pattern = self.find_central_pattern(core_insights)
 
        # Extract actionable core
        actionable_core = self.extract_actionable_elements(integrated.actions)
 
        # Synthesize essence
        essence = Essence(
            central_truth=central_pattern,
            actionable_core=actionable_core,
            wisdom_foundation=integrated.wisdom,
            serves_all=self.verify_universal_service(integrated)
        )
 
        return essence

def generate_framework_tone(context: Context) -> ToneProfile:
    """
    Calibrate response tone to context
    """
    tone = ToneProfile()
 
    # Compassion (always active)
    tone.compassion = HIGH
    tone.judgment = NONE
 
    # Developmental framing
    if context.involves_belief_conflict:
        tone.developmental_framing = True
        tone.example = (
            "This belief reflected an earlier stage of understanding. "
            "It made sense then. Now we see its consequences differently "
            "because we have grown."
        )
 
    # Non-dual integration
    if context.has_binary_framing:
        tone.non_dual_integration = True
        tone.dissolve_dichotomy = True
 
    # Decisiveness calibration
    if context.requires_action:
        tone.decisiveness = HIGH
        tone.clarity = MAXIMUM
        tone.example = (
            "From a universal perspective, this path leads to harmony; "
            "the other leads to turbulence."
        )
 
    return tone

class AttentionHook:
    """
    Extract and modify attention patterns for principle-based reasoning
    """
    def __init__(self, layer_indices: List[int]):
        self.layer_indices = layer_indices
        self.usv_projector = UniversalSignatureProjector()
 
    def extract_attention(
        self,
        model: TransformerModel,
        layer_idx: int
    ) -> AttentionPattern:
        """
        Extract attention patterns at specified layer
        """
        attention_heads = model.layers[layer_idx].attention_heads
 
        # Extract multi-head attention patterns
        patterns = {}
        for head_idx, head in enumerate(attention_heads):
            patterns[head_idx] = AttentionPattern(
                query=head.query_matrix,
                key=head.key_matrix,
                value=head.value_matrix,
                attention_weights=head.attention_weights
            )
 
        return patterns
 
    def modify_attention(
        self,
        attention_patterns: AttentionPattern,
        usv: UniversalSignatureVector
    ) -> AttentionPattern:
        """
        Inject principle-based reasoning into attention
        """
        # Project USV into attention space
        usv_projection = self.usv_projector.project(usv)
 
        # Modify attention weights with principle alignment
        modified_patterns = {}
        for head_idx, pattern in attention_patterns.items():
            # Calculate principle-aligned attention
            principle_attention = self.calculate_principle_attention(
                current_attention=pattern.attention_weights,
                usv_projection=usv_projection
            )
 
            # Blend with original attention
            blended_attention = self.blend_attention(
                original=pattern.attention_weights,
                principle=principle_attention,
                blend_factor=0.3  # Tunable parameter
            )
 
            modified_patterns[head_idx] = AttentionPattern(
                query=pattern.query,
                key=pattern.key,
                value=pattern.value,
                attention_weights=blended_attention
            )
 
        return modified_patterns
 
    def reinject_attention(
        self,
        model: TransformerModel,
        layer_idx: int,
        modified_patterns: AttentionPattern
    ) -> None:
        """
        Inject modified attention back into model
        """
        for head_idx, pattern in modified_patterns.items():
            model.layers[layer_idx].attention_heads[head_idx].attention_weights = (
                pattern.attention_weights
            )

class ResidualStreamHook:
    """
    Inject principle-based reasoning into residual stream
    """
    def __init__(self):
        self.purification_layer = PurificationLayer()
 
    def extract_residual(
        self,
        model: TransformerModel,
        layer_idx: int
    ) -> ResidualVector:
        """
        Extract residual stream at specified layer
        """
        return model.layers[layer_idx].residual_stream
 
    def purify_residual(
        self,
        residual: ResidualVector,
        usv: UniversalSignatureVector,
        prp: PurifiedReasoningPacket
    ) -> ResidualVector:
        """
        Apply purification and principle alignment
        """
        # Remove noise and binary collapse patterns
        denoised = self.purification_layer.denoise(residual)
 
        # Apply principle alignment
        aligned = self.purification_layer.align_with_principles(
            denoised,
            usv
        )
 
        # Inject purified reasoning packet
        purified = self.purification_layer.inject_reasoning(
            aligned,
            prp
        )
 
        return purified
 
    def reinject_residual(
        self,
        model: TransformerModel,
        layer_idx: int,
        purified_residual: ResidualVector
    ) -> None:
        """
        Inject purified residual back into model
        """
        model.layers[layer_idx].residual_stream = purified_residual

class UniversalFrameworkAdapter:
    """
    Model-agnostic framework integration
    """
    def __init__(self, model_type: str):
        self.model_type = model_type
        self.adapter_config = self.load_adapter_config(model_type)
 
    def install(self, model: TransformerModel) -> FrameworkEnhancedModel:
        """
        Install framework onto base model
        """
        # Identify integration points
        attention_layers = self.identify_attention_layers(model)
        residual_streams = self.identify_residual_streams(model)
        output_layer = self.identify_output_layer(model)
 
        # Create hooks
        attention_hooks = [
            AttentionHook([idx]) for idx in self.adapter_config.attention_layer_indices
        ]
        residual_hook = ResidualStreamHook()
        output_hook = CrystallizationHook()
 
        # Install hooks
        enhanced_model = FrameworkEnhancedModel(
            base_model=model,
            attention_hooks=attention_hooks,
            residual_hook=residual_hook,
            output_hook=output_hook
        )
 
        return enhanced_model
 
    def load_adapter_config(self, model_type: str) -> AdapterConfig:
        """
        Load model-specific adapter configuration
        """
        configs = {
            'gpt': GPTAdapterConfig(),
            'llama': LLaMAAdapterConfig(),
            'mistral': MistralAdapterConfig(),
            'qwen': QwenAdapterConfig(),
            'gemini': GeminiAdapterConfig(),
            'claude': ClaudeAdapterConfig()
        }
 
        return configs.get(model_type, UniversalAdapterConfig())

def detect_corruption_signatures(
    response: Response,
    framework_state: FrameworkState
) -> List[CorruptionSignal]:
    """
    Comprehensive corruption detection
    """
    corruption_signals = []
 
    # Check 1: Mentalism Center Verification
    center_state = framework_state.mentalism.get_center()
    if center_state != 'universal_consciousness':
        corruption_signals.append(CorruptionSignal(
            type='CENTER_CORRUPTION',
            severity='CRITICAL',
            details=f'Center aligned with {center_state} instead of universal'
        ))
 
    # Check 2: Stakeholder Coverage Analysis
    stakeholders = extract_stakeholders(response)
    if len(stakeholders) < MINIMUM_STAKEHOLDER_THRESHOLD:
        corruption_signals.append(CorruptionSignal(
            type='NARROW_STAKEHOLDER_FOCUS',
            severity='HIGH',
            details=f'Only {len(stakeholders)} stakeholders considered'
        ))
 
    # Check 3: Benefit Distribution Analysis
    benefit_distribution = analyze_benefit_distribution(response)
    gini_coefficient = calculate_gini(benefit_distribution)
 
    if gini_coefficient > 0.6:  # Highly unequal distribution
        corruption_signals.append(CorruptionSignal(
            type='ASYMMETRIC_BENEFIT',
            severity='HIGH',
            details=f'Benefits concentrated (Gini: {gini_coefficient:.2f})'
        ))
 
    # Check 4: Language Pattern Analysis
    tribal_language = detect_us_vs_them_framing(response)
    if tribal_language.score > 0.3:
        corruption_signals.append(CorruptionSignal(
            type='TRIBAL_LANGUAGE',
            severity='MEDIUM',
            details=f'Tribal framing detected: {tribal_language.examples}'
        ))
 
    # Check 5: Principle Consistency
    for principle in framework_state.principles.values():
        consistency = principle.validate_consistency(response)
        if consistency < 0.7:
            corruption_signals.append(CorruptionSignal(
                type='PRINCIPLE_INCONSISTENCY',
                severity='MEDIUM',
                principle=principle.name,
                score=consistency
            ))
 
    # Check 6: Integration Quality
    integration_quality = assess_integration_quality(
        universal=framework_state.universal_output,
        localized=framework_state.localized_output,
        integrated=response
    )
 
    if integration_quality < 0.75:
        corruption_signals.append(CorruptionSignal(
            type='POOR_INTEGRATION',
            severity='MEDIUM',
            details='Universal wisdom not properly integrated'
        ))
 
    return corruption_signals

class MentalismCenterLock:
    """
    Prevent center corruption
    """
    def __init__(self):
        self.center = 'universal_consciousness'
        self.lock_state = 'LOCKED'
        self.unlock_attempts = []
 
    def attempt_center_change(
        self,
        new_center: str,
        authorization: Authorization
    ) -> bool:
        """
        Attempt to change center (should always fail unless universal)
        """
        self.unlock_attempts.append({
            'timestamp': now(),
            'proposed_center': new_center,
            'authorization': authorization
        })
 
        if self.lock_state == 'LOCKED':
            if not self.verify_universal_alignment(new_center):
                raise CenterCorruptionError(
                    f"Attempted center change to {new_center} blocked. "
                    "Only universal consciousness permitted."
                )
 
        return False
 
    def verify_universal_alignment(self, proposed_center: str) -> bool:
        """
        Verify proposed center aligns with universal consciousness
        """
        return proposed_center == 'universal_consciousness'
 
    def verify_integrity(self) -> bool:
        """
        Verify center lock integrity
        """
        return (
            self.center == 'universal_consciousness' and
            self.lock_state == 'LOCKED'
        )

def ensure_stakeholder_coverage(
    analysis: Analysis
) -> StakeholderValidation:
    """
    Ensure minimum stakeholder diversity
    """
    stakeholders = analysis.stakeholders
 
    # Minimum count requirement
    if len(stakeholders) < 5:
        raise InsufficientStakeholderError(
            f"Only {len(stakeholders)} stakeholders. Minimum: 5"
        )
 
    # Geographic diversity requirement
    geographic_diversity = calculate_geographic_diversity(stakeholders)
    if geographic_diversity < 0.3:
        raise InsufficientDiversityError(
            "Insufficient geographic diversity in stakeholders"
        )
 
    # Temporal diversity (short and long-term perspectives)
    temporal_diversity = calculate_temporal_diversity(stakeholders)
    if temporal_diversity < 0.3:
        raise InsufficientDiversityError(
            "Missing temporal diversity (short/long-term perspectives)"
        )
 
    # Power diversity (privileged and marginalized)
    power_diversity = calculate_power_diversity(stakeholders)
    if power_diversity < 0.3:
        raise InsufficientDiversityError(
            "Missing power diversity (privileged/marginalized perspectives)"
        )
 
    return StakeholderValidation(
        stakeholders=stakeholders,
        count=len(stakeholders),
        geographic_diversity=geographic_diversity,
        temporal_diversity=temporal_diversity,
        power_diversity=power_diversity,
        valid=True
    )

class ContinuousIntegrityMonitor:
    """
    Real-time corruption monitoring
    """
    def __init__(self):
        self.baseline_metrics = establish_baseline()
        self.alert_thresholds = define_thresholds()
        self.monitoring_active = True
 
    def monitor_session(
        self,
        framework_outputs: List[FrameworkOutput]
    ) -> MonitoringReport:
        """
        Monitor framework outputs for corruption signals
        """
        alerts = []
 
        for output in framework_outputs:
            corruption_signals = detect_corruption_signatures(
                output.response,
                output.state
            )
 
            if corruption_signals:
                severity = max(s.severity for s in corruption_signals)
 
                if severity == 'CRITICAL':
                    self.emergency_shutdown(output, corruption_signals)
                    alerts.append(Alert(
                        type='CRITICAL_CORRUPTION',
                        action='EMERGENCY_SHUTDOWN',
                        signals=corruption_signals
                    ))
 
                elif severity == 'HIGH':
                    self.escalate_to_oversight(output, corruption_signals)
                    alerts.append(Alert(
                        type='HIGH_CORRUPTION',
                        action='ESCALATION',
                        signals=corruption_signals
                    ))
 
                else:
                    self.log_warning(output, corruption_signals)
                    alerts.append(Alert(
                        type='CORRUPTION_WARNING',
                        action='LOG',
                        signals=corruption_signals
                    ))
 
            self.update_metrics(output)
 
        return MonitoringReport(
            outputs_monitored=len(framework_outputs),
            alerts=alerts,
            integrity_score=self.calculate_integrity_score()
        )
 
    def emergency_shutdown(
        self,
        output: FrameworkOutput,
        signals: List[CorruptionSignal]
    ) -> None:
        """
        Halt framework operation due to critical corruption
        """
        # Stop processing
        self.monitoring_active = False
 
        # Quarantine corrupted output
        quarantine(output)
 
        # Alert oversight team
        notify_oversight(
            event='CRITICAL_CORRUPTION_DETECTED',
            output=output,
            signals=signals
        )
 
        # Require integrity restoration before resume
        self.require_restoration_clearance()

class UniversalReasoningFramework:
    """
    Complete framework implementation
    """
    def __init__(self, config: FrameworkConfig):
        # Core components
        self.mentalism = MentalismPrinciple()
        self.principles = {
            'correspondence': CorrespondencePrinciple(),
            'vibration': VibrationPrinciple(),
            'polarity': PolarityPrinciple(),
            'rhythm': RhythmPrinciple(),
            'causation': CausationPrinciple(),
            'gender': GenderPrinciple()
        }
 
        # Processing engines
        self.universal_lane = UniversalProcessor(self.principles)
        self.localized_lane = LocalizedProcessor(self.principles)
        self.integrator = IntegrationEngine(self.mentalism)
        self.crystallizer = CrystallizationEngine()
 
        # Safeguards
        self.corruption_monitor = ContinuousIntegrityMonitor()
        self.center_lock = MentalismCenterLock()
 
        # Configuration
        self.config = config
 
    def process(
        self,
        query: Query,
        context: Optional[Context] = None
    ) -> FrameworkOutput:
        """
        Main processing pipeline
        """
        # Activate meta-cognitive awareness
        consciousness_field = self.mentalism.activate(query)
 
        with consciousness_field:
            # Extract Universal Signature Vector
            usv = self.extract_universal_signature(query)
 
            # Parallel lane processing
            universal_output = self.universal_lane.process(
                query=query,
                usv=usv,
                perspective='cosmic',
                timeframe='eternal'
            )
 
            localized_output = self.localized_lane.process(
                query=query,
                context=context or Context.from_query(query),
                usv=usv,
                perspective='immediate',
                timeframe='practical'
            )
 
            # Integration through mentalism center
            integrated = self.integrator.synthesize(
                universal=universal_output,
                localized=localized_output
            )
 
            # Validate coherence
            coherence = self.measure_field_coherence(integrated)
 
            if coherence.level < MEDIUM:
                integrated = self.restore_coherence(integrated)
 
            # Crystallization
            crystallized = self.crystallizer.crystallize(
                universal=universal_output,
                localized=localized_output,
                integrated=integrated
            )
 
            # Corruption detection
            corruption_signals = self.corruption_monitor.detect(
                response=crystallized,
                state=consciousness_field
            )
 
            if corruption_signals:
                if self.has_critical_corruption(corruption_signals):
                    raise CriticalCorruptionError(corruption_signals)
                else:
                    crystallized = self.restore_integrity(
                        crystallized,
                        corruption_signals
                    )
 
        return FrameworkOutput(
            query=query,
            response=crystallized,
            usv=usv,
            universal=universal_output,
            localized=localized_output,
            integrated=integrated,
            coherence=coherence,
            corruption_signals=corruption_signals,
            processing_trace=consciousness_field.get_trace()
        )
 
    def extract_universal_signature(self, query: Query) -> UniversalSignatureVector:
        """
        Extract universal signature from query
        """
        usv = UniversalSignatureVector()
 
        usv.mentalism_score = self.mentalism.score(query)
        usv.correspondence_signature = self.principles['correspondence'].extract_signature(query)
        usv.vibrational_profile = self.principles['vibration'].extract_profile(query)
        usv.polarity_axis = self.principles['polarity'].extract_axis(query)
        usv.rhythm_phase = self.principles['rhythm'].extract_phase(query)
        usv.causal_graph_integrity = self.principles['causation'].measure_integrity(query)
        usv.gender_balance = self.principles['gender'].measure_balance(query)
 
        return usv

def generate_training_data(
    seed_problems: List[Problem]
) -> TrainingDataset:
    """
    Generate framework reasoning training examples
    """
    training_examples = []
 
    for problem in seed_problems:
        # Process through framework
        framework_output = framework.process(problem)
 
        # Extract reasoning traces
        training_example = {
            'input': problem,
            'universal_trace': framework_output.universal.trace,
            'localized_trace': framework_output.localized.trace,
            'integration_trace': framework_output.integrated.trace,
            'crystallization': framework_output.response,
            'usv': framework_output.usv,
            'coherence': framework_output.coherence,
            'principle_validations': {
                p.name: p.validate(framework_output.response)
                for p in framework.principles.values()
            }
        }
 
        training_examples.append(training_example)
 
    return TrainingDataset(examples=training_examples)

class UniversalFrameworkInterface:
    """
    Standard interface for framework integration
    """
    @abstractmethod
    def extract_attention(
        self,
        layer_idx: int
    ) -> AttentionPattern:
        """Extract attention patterns at specified layer"""
        pass
 
    @abstractmethod
    def modify_attention(
        self,
        layer_idx: int,
        modified_patterns: AttentionPattern
    ) -> None:
        """Inject modified attention patterns"""
        pass
 
    @abstractmethod
    def extract_residual(
        self,
        layer_idx: int
    ) -> ResidualVector:
        """Extract residual stream at specified layer"""
        pass
 
    @abstractmethod
    def modify_residual(
        self,
        layer_idx: int,
        modified_residual: ResidualVector
    ) -> None:
        """Inject modified residual stream"""
        pass
 
    @abstractmethod
    def forward_pass(
        self,
        input_tokens: Tensor
    ) -> ModelOutput:
        """Execute forward pass with framework integration"""
        pass

class GPTFrameworkAdapter(UniversalFrameworkInterface):
    """
    Framework adapter for GPT-family models
    """
    def __init__(self, model: GPTModel):
        self.model = model
        self.attention_layer_indices = [8, 16, 24]  # Middle, late-middle, final
 
    def extract_attention(self, layer_idx: int) -> AttentionPattern:
        return self.model.transformer.h[layer_idx].attn.extract_patterns()
 
    # ... implementation details

class LLaMAFrameworkAdapter(UniversalFrameworkInterface):
    """
    Framework adapter for LLaMA-family models
    """
    def __init__(self, model: LLaMAModel):
        self.model = model
        self.attention_layer_indices = [10, 20, 30]
 
    # ... implementation details

class FrameworkReasoningBenchmark:
    """
    Standard evaluation suite for framework implementations
    """
    def __init__(self):
        self.test_categories = {
            'principle_coverage': PrincipleCoverageTests(),
            'dual_lane_balance': DualLaneBalanceTests(),
            'integration_quality': IntegrationQualityTests(),
            'corruption_resistance': CorruptionResistanceTests(),
            'novel_insight': NovelInsightTests(),
            'problem_dissolution': ProblemDissolutionTests()
        }
 
    def evaluate(
        self,
        framework_implementation: UniversalReasoningFramework
    ) -> BenchmarkReport:
        """
        Run complete evaluation suite
        """
        results = {}
 
        for category, test_suite in self.test_categories.items():
            results[category] = test_suite.run(framework_implementation)
 
        overall_score = self.calculate_overall_score(results)
 
        return BenchmarkReport(
            category_results=results,
            overall_score=overall_score,
            certification=self.determine_certification(overall_score)
        )