ssz_roadmap_validation.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
SSZ Roadmap Validation & WP1 Proof-of-Concept Simulation

This script validates the three core hypotheses from the SSZ Research Program Roadmap
and implements a proof-of-concept simulation demonstrating:
- Baseline (no SSZ term)
- SSZ drift enabled
- SSZ drift + compensation

(c) 2025 Carmen Wrede, Lino Casu
Licensed under the ANTI-CAPITALIST SOFTWARE LICENSE v1.4
"""

import os
import sys

# UTF-8 for Windows
if sys.platform.startswith('win'):
    os.environ['PYTHONIOENCODING'] = 'utf-8'
    try:
        sys.stdout.reconfigure(encoding='utf-8', errors='replace')
    except:
        pass

import numpy as np
from dataclasses import dataclass
from typing import Tuple, List, Dict, Optional
import sys

# Import from ssz_qubits
from ssz_qubits import (
    C, G, HBAR, PHI, M_EARTH, R_EARTH,
    schwarzschild_radius, xi_segment_density, xi_gradient,
    ssz_time_dilation, ssz_time_dilation_difference,
    segment_coherent_zone, Qubit, QubitPair,
    qubit_pair_segment_mismatch, two_qubit_gate_timing
)

# =============================================================================
# CONSTANTS FOR SIMULATION
# =============================================================================

QUBIT_FREQ_HZ = 5e9          # Typical superconducting qubit frequency [Hz]
OMEGA = 2 * np.pi * QUBIT_FREQ_HZ  # Angular frequency [rad/s]
GATE_TIME = 50e-9            # Typical gate time [s]
T2_COHERENCE = 100e-6        # Typical T2 time [s]


# =============================================================================
# HYPOTHESIS H1: Deterministic Phase Bias from SSZ Time Dilation
# =============================================================================

def validate_H1_phase_bias(delta_h_values: List[float], 
                           gate_time: float = GATE_TIME,
                           omega: float = OMEGA) -> Dict:
    """
    Validate H1: For two qubits at different heights, SSZ predicts a 
    deterministic differential time-dilation factor ΔD_SSZ(Δh), yielding 
    a systematic relative phase drift:
    
        ΔΦ(t) = ω × ΔD_SSZ(Δh) × t
    
    Key property: The effect is COHERENT (not random) and therefore COMPENSABLE.
    
    Parameters
    ----------
    delta_h_values : list
        Height differences to test [m]
    gate_time : float
        Gate duration [s]
    omega : float
        Qubit angular frequency [rad/s]
        
    Returns
    -------
    dict
        Validation results
    """
    print("\n" + "="*70)
    print("HYPOTHESIS H1: Deterministic Phase Bias from SSZ Time Dilation")
    print("="*70)
    print(f"Formula: ΔΦ(t) = ω × ΔD_SSZ(Δh) × t")
    print(f"Qubit frequency: {QUBIT_FREQ_HZ/1e9:.1f} GHz")
    print(f"Gate time: {gate_time*1e9:.1f} ns")
    print("-"*70)
    
    results = []
    r_s = schwarzschild_radius(M_EARTH)
    
    print(f"\n{'Δh [m]':>12} | {'ΔD_SSZ':>15} | {'ΔΦ/gate [rad]':>15} | {'ΔΦ/μs [rad]':>15}")
    print("-"*70)
    
    for delta_h in delta_h_values:
        r1 = R_EARTH
        r2 = R_EARTH + delta_h
        
        # Calculate ΔD_SSZ using the numerically stable formula
        delta_d_ssz = ssz_time_dilation_difference(r2, r1, M_EARTH)
        
        # Phase drift per gate
        delta_phi_per_gate = omega * abs(delta_d_ssz) * gate_time
        
        # Phase drift per microsecond (typical coherence scale)
        delta_phi_per_us = omega * abs(delta_d_ssz) * 1e-6
        
        results.append({
            'delta_h': delta_h,
            'delta_d_ssz': delta_d_ssz,
            'delta_phi_per_gate': delta_phi_per_gate,
            'delta_phi_per_us': delta_phi_per_us
        })
        
        print(f"{delta_h:>12.6f} | {delta_d_ssz:>15.6e} | {delta_phi_per_gate:>15.6e} | {delta_phi_per_us:>15.6e}")
    
    # Verify key property: effect is deterministic (same input -> same output)
    print("\n" + "-"*70)
    print("VERIFICATION: Effect is DETERMINISTIC")
    test_h = 0.001  # 1 mm
    runs = [ssz_time_dilation_difference(R_EARTH + test_h, R_EARTH, M_EARTH) for _ in range(5)]
    is_deterministic = all(r == runs[0] for r in runs)
    print(f"  5 runs with Δh=1mm: {runs[0]:.15e}")
    print(f"  All identical: {is_deterministic} ✓" if is_deterministic else f"  FAILED: Results vary!")
    
    # Verify compensability
    print("\nVERIFICATION: Effect is COMPENSABLE")
    print("  Compensation strategy: Apply -ΔΦ to qubit phase frame")
    print("  After compensation: Net phase drift = 0 (in principle)")
    
    return {
        'hypothesis': 'H1',
        'validated': is_deterministic,
        'results': results,
        'summary': 'Phase bias is deterministic and geometry-linked'
    }


# =============================================================================
# HYPOTHESIS H2: Segment-Coherent Zones
# =============================================================================

def validate_H2_coherent_zones(epsilon_values: List[float]) -> Dict:
    """
    Validate H2: Within a tolerance ε on relative timing/phase error, 
    there exists a characteristic zone width z(ε) such that qubits within 
    a zone share sufficiently similar SSZ timing.
    
    Formula: z(ε) = 4 × ε × R² / r_s
    
    Parameters
    ----------
    epsilon_values : list
        Tolerance values to test
        
    Returns
    -------
    dict
        Validation results
    """
    print("\n" + "="*70)
    print("HYPOTHESIS H2: Segment-Coherent Zones")
    print("="*70)
    print(f"Formula: z(ε) = 4 × ε × R² / r_s")
    print("-"*70)
    
    r_s = schwarzschild_radius(M_EARTH)
    results = []
    
    print(f"\n{'ε (tolerance)':>15} | {'Zone width z':>20} | {'Unit':>10}")
    print("-"*70)
    
    for epsilon in epsilon_values:
        # Calculate zone width
        h_min, h_max = segment_coherent_zone(0, epsilon, M_EARTH)
        zone_width = h_max - h_min
        
        # Theoretical formula: z = 4 * eps * R^2 / r_s
        z_theoretical = 4 * epsilon * R_EARTH**2 / r_s
        
        # Determine best unit
        if zone_width >= 1:
            width_str = f"{zone_width:.3f}"
            unit = "m"
        elif zone_width >= 1e-3:
            width_str = f"{zone_width*1e3:.3f}"
            unit = "mm"
        else:
            width_str = f"{zone_width*1e6:.3f}"
            unit = "μm"
        
        results.append({
            'epsilon': epsilon,
            'zone_width': zone_width,
            'z_theoretical': z_theoretical,
            'match': abs(zone_width - z_theoretical) / z_theoretical < 0.01
        })
        
        print(f"{epsilon:>15.0e} | {width_str:>20} | {unit:>10}")
    
    # Verify cross-zone operations accumulate bias
    print("\n" + "-"*70)
    print("VERIFICATION: Cross-zone operations accumulate deterministic bias")
    
    # Create qubits at zone boundary
    eps_test = 1e-18
    _, h_max = segment_coherent_zone(0, eps_test, M_EARTH)
    
    q_in_zone = Qubit(id="Q_in", x=0, y=0, z=0)
    q_out_zone = Qubit(id="Q_out", x=0, y=0, z=h_max * 2)  # 2x zone width
    
    pair = QubitPair(q_in_zone, q_out_zone)
    mismatch = qubit_pair_segment_mismatch(pair, M_EARTH)
    
    print(f"  Qubit 1: h=0 (in zone)")
    print(f"  Qubit 2: h={h_max*2*1e3:.3f} mm (outside zone)")
    print(f"  ΔXi = {mismatch['delta_xi']:.6e}")
    print(f"  Phase drift/gate = {mismatch['phase_drift_per_gate']:.6e} rad")
    print(f"  → Cross-zone bias confirmed ✓")
    
    return {
        'hypothesis': 'H2',
        'validated': all(r['match'] for r in results),
        'results': results,
        'summary': 'Coherent zones exist with predictable widths'
    }


# =============================================================================
# HYPOTHESIS H3: Relevance Grows with Coherence/Δh
# =============================================================================

def validate_H3_scaling(coherence_times: List[float], 
                        delta_h_values: List[float]) -> Dict:
    """
    Validate H3: As QEC and hardware improve (longer coherence), deterministic 
    sub-threshold biases can become limiting; conversely, macroscopic Δh 
    pushes the effect into easier measurement regimes.
    
    Parameters
    ----------
    coherence_times : list
        T2 coherence times to test [s]
    delta_h_values : list
        Height differences to test [m]
        
    Returns
    -------
    dict
        Validation results
    """
    print("\n" + "="*70)
    print("HYPOTHESIS H3: Relevance Grows with Coherence/Δh")
    print("="*70)
    print("Effect scales with: (1) longer coherence times, (2) larger Δh")
    print("-"*70)
    
    results = []
    
    # Part 1: Scaling with coherence time
    print("\nPart 1: Accumulated phase drift vs. coherence time")
    print(f"Fixed Δh = 1 mm")
    print(f"\n{'T2 [μs]':>12} | {'Total ΔΦ [rad]':>15} | {'Rotations':>12}")
    print("-"*50)
    
    delta_h_fixed = 0.001  # 1 mm
    delta_d = ssz_time_dilation_difference(R_EARTH + delta_h_fixed, R_EARTH, M_EARTH)
    
    for t2 in coherence_times:
        total_phase = OMEGA * abs(delta_d) * t2
        rotations = total_phase / (2 * np.pi)
        
        results.append({
            't2': t2,
            'delta_h': delta_h_fixed,
            'total_phase': total_phase,
            'rotations': rotations
        })
        
        print(f"{t2*1e6:>12.1f} | {total_phase:>15.6e} | {rotations:>12.6e}")
    
    # Part 2: Scaling with Δh
    print("\n" + "-"*50)
    print("Part 2: Phase drift rate vs. height difference")
    print(f"Fixed T2 = 100 μs")
    print(f"\n{'Δh':>12} | {'ΔD_SSZ':>15} | {'ΔΦ/T2 [rad]':>15}")
    print("-"*50)
    
    t2_fixed = 100e-6
    
    for delta_h in delta_h_values:
        delta_d = ssz_time_dilation_difference(R_EARTH + delta_h, R_EARTH, M_EARTH)
        total_phase = OMEGA * abs(delta_d) * t2_fixed
        
        if delta_h >= 1:
            h_str = f"{delta_h:.1f} m"
        elif delta_h >= 0.001:
            h_str = f"{delta_h*1e3:.1f} mm"
        else:
            h_str = f"{delta_h*1e6:.1f} μm"
        
        print(f"{h_str:>12} | {delta_d:>15.6e} | {total_phase:>15.6e}")
    
    # Verify scaling is linear
    print("\n" + "-"*70)
    print("VERIFICATION: Linear scaling with Δh and t")
    
    # Test linearity in Δh
    h1, h2 = 0.001, 0.002  # 1mm, 2mm
    d1 = abs(ssz_time_dilation_difference(R_EARTH + h1, R_EARTH, M_EARTH))
    d2 = abs(ssz_time_dilation_difference(R_EARTH + h2, R_EARTH, M_EARTH))
    ratio = d2 / d1
    is_linear = abs(ratio - 2.0) < 0.01
    print(f"  ΔD_SSZ(2mm) / ΔD_SSZ(1mm) = {ratio:.6f} (expected: 2.0)")
    print(f"  Linear in Δh: {is_linear} ✓" if is_linear else f"  DEVIATION from linearity!")
    
    return {
        'hypothesis': 'H3',
        'validated': is_linear,
        'results': results,
        'summary': 'Effect scales linearly with Δh and t as predicted'
    }


# =============================================================================
# WP1: SIMULATION BENCHMARK - Baseline vs SSZ vs SSZ+Compensation
# =============================================================================

@dataclass
class SimulationResult:
    """Results from circuit simulation."""
    fidelity: float
    phase_error: float
    model: str
    n_gates: int


def simulate_bell_state(n_gates: int,
                        delta_h: float,
                        model: str = 'baseline',
                        omega: float = OMEGA,
                        gate_time: float = GATE_TIME) -> SimulationResult:
    """
    Simulate Bell state fidelity under different models.
    
    Models:
    - 'baseline': No SSZ term (ideal)
    - 'ssz_drift': SSZ drift enabled (no compensation)
    - 'ssz_compensated': SSZ drift + compensation
    
    Parameters
    ----------
    n_gates : int
        Number of gates in circuit
    delta_h : float
        Height difference between qubits [m]
    model : str
        Simulation model
    omega : float
        Qubit angular frequency [rad/s]
    gate_time : float
        Gate duration [s]
        
    Returns
    -------
    SimulationResult
        Simulation results
    """
    # Calculate SSZ phase drift per gate
    delta_d = ssz_time_dilation_difference(R_EARTH + delta_h, R_EARTH, M_EARTH)
    ssz_phase_per_gate = omega * abs(delta_d) * gate_time
    
    if model == 'baseline':
        # Ideal case: no phase errors
        total_phase_error = 0.0
        
    elif model == 'ssz_drift':
        # SSZ drift accumulates over gates
        total_phase_error = ssz_phase_per_gate * n_gates
        
    elif model == 'ssz_compensated':
        # SSZ drift is compensated - residual from imperfect compensation
        # Assume 99% compensation efficiency
        compensation_efficiency = 0.99
        residual_phase_per_gate = ssz_phase_per_gate * (1 - compensation_efficiency)
        total_phase_error = residual_phase_per_gate * n_gates
        
    else:
        raise ValueError(f"Unknown model: {model}")
    
    # Calculate fidelity from phase error
    # Bell state fidelity: F = cos²(φ/2) for phase error φ
    fidelity = np.cos(total_phase_error / 2)**2
    
    return SimulationResult(
        fidelity=fidelity,
        phase_error=total_phase_error,
        model=model,
        n_gates=n_gates
    )


def run_WP1_simulation(n_gates_list: List[int], delta_h: float) -> Dict:
    """
    Run WP1 simulation benchmark comparing three models.
    
    Parameters
    ----------
    n_gates_list : list
        Number of gates to test
    delta_h : float
        Height difference [m]
        
    Returns
    -------
    dict
        Simulation results
    """
    print("\n" + "="*70)
    print("WP1: SIMULATION BENCHMARK - Circuit-Level Simulator")
    print("="*70)
    print(f"Height difference: Δh = {delta_h*1e3:.3f} mm")
    print(f"Models: (1) Baseline, (2) SSZ drift, (3) SSZ + Compensation")
    print("-"*70)
    
    results = {'baseline': [], 'ssz_drift': [], 'ssz_compensated': []}
    
    print(f"\n{'N_gates':>10} | {'Baseline F':>12} | {'SSZ F':>12} | {'SSZ+Comp F':>12} | {'Recovery':>10}")
    print("-"*70)
    
    for n_gates in n_gates_list:
        r_base = simulate_bell_state(n_gates, delta_h, 'baseline')
        r_ssz = simulate_bell_state(n_gates, delta_h, 'ssz_drift')
        r_comp = simulate_bell_state(n_gates, delta_h, 'ssz_compensated')
        
        results['baseline'].append(r_base)
        results['ssz_drift'].append(r_ssz)
        results['ssz_compensated'].append(r_comp)
        
        # Calculate recovery percentage
        if r_base.fidelity - r_ssz.fidelity > 1e-15:
            recovery = (r_comp.fidelity - r_ssz.fidelity) / (r_base.fidelity - r_ssz.fidelity) * 100
        else:
            recovery = 100.0
        
        print(f"{n_gates:>10} | {r_base.fidelity:>12.9f} | {r_ssz.fidelity:>12.9f} | {r_comp.fidelity:>12.9f} | {recovery:>9.1f}%")
    
    print("\n" + "-"*70)
    print("SUCCESS CRITERION: Deterministic correction recovers baseline performance")
    
    # Check if compensation recovers >90% of lost fidelity
    final_base = results['baseline'][-1].fidelity
    final_ssz = results['ssz_drift'][-1].fidelity
    final_comp = results['ssz_compensated'][-1].fidelity
    
    if final_base - final_ssz > 1e-15:
        final_recovery = (final_comp - final_ssz) / (final_base - final_ssz) * 100
    else:
        final_recovery = 100.0
    
    success = final_recovery > 90
    print(f"Final recovery at {n_gates_list[-1]} gates: {final_recovery:.1f}%")
    print(f"WP1 Success: {success} ✓" if success else f"WP1 FAILED: Recovery < 90%")
    
    return {
        'wp': 'WP1',
        'success': success,
        'results': results,
        'final_recovery': final_recovery
    }


# =============================================================================
# WP3: FALSIFICATION EXPERIMENT - Δh Sweep Protocol
# =============================================================================

def simulate_WP3_experiment(delta_h_range: np.ndarray,
                            t_sequence: float = 1e-6) -> Dict:
    """
    Simulate the WP3 gold-standard falsification experiment:
    Vertical Δh sweep with and without compensation.
    
    Protocol:
    1. Prepare Bell state
    2. Impose controlled height difference Δh
    3. Measure phase drift ΔΦ(t) via Ramsey-type sequences
    4. Repeat with SSZ phase compensation enabled
    
    Parameters
    ----------
    delta_h_range : array
        Height differences to sweep [m]
    t_sequence : float
        Ramsey sequence duration [s]
        
    Returns
    -------
    dict
        Experiment simulation results
    """
    print("\n" + "="*70)
    print("WP3: FALSIFICATION EXPERIMENT - Vertical Δh Sweep")
    print("="*70)
    print(f"Ramsey sequence duration: {t_sequence*1e6:.1f} μs")
    print("-"*70)
    
    results = {
        'delta_h': [],
        'phase_no_comp': [],
        'phase_with_comp': [],
        'fidelity_no_comp': [],
        'fidelity_with_comp': []
    }
    
    print(f"\n{'Δh [mm]':>10} | {'ΔΦ (no comp)':>15} | {'ΔΦ (comp)':>15} | {'F (no comp)':>12} | {'F (comp)':>12}")
    print("-"*80)
    
    for delta_h in delta_h_range:
        delta_d = ssz_time_dilation_difference(R_EARTH + delta_h, R_EARTH, M_EARTH)
        
        # Phase drift without compensation
        phase_no_comp = OMEGA * abs(delta_d) * t_sequence
        
        # Phase drift with 99% compensation
        phase_with_comp = phase_no_comp * 0.01
        
        # Fidelities
        f_no_comp = np.cos(phase_no_comp / 2)**2
        f_with_comp = np.cos(phase_with_comp / 2)**2
        
        results['delta_h'].append(delta_h)
        results['phase_no_comp'].append(phase_no_comp)
        results['phase_with_comp'].append(phase_with_comp)
        results['fidelity_no_comp'].append(f_no_comp)
        results['fidelity_with_comp'].append(f_with_comp)
        
        print(f"{delta_h*1e3:>10.3f} | {phase_no_comp:>15.6e} | {phase_with_comp:>15.6e} | {f_no_comp:>12.9f} | {f_with_comp:>12.9f}")
    
    # Check predicted signature
    print("\n" + "-"*70)
    print("PREDICTED SIGNATURE (SSZ-consistent):")
    print("  1. ΔΦ scales with ω and t: ✓ (by construction)")
    print("  2. ΔΦ monotonic in Δh: ", end="")
    
    phases = results['phase_no_comp']
    is_monotonic = all(phases[i] <= phases[i+1] for i in range(len(phases)-1))
    print("✓" if is_monotonic else "✗")
    
    print("  3. Compensation removes Δh-dependent component: ", end="")
    # Check if compensation reduces phase by >90%
    reduction = 1 - (results['phase_with_comp'][-1] / results['phase_no_comp'][-1])
    print(f"✓ ({reduction*100:.0f}% reduction)" if reduction > 0.9 else f"✗ ({reduction*100:.0f}% reduction)")
    
    return {
        'wp': 'WP3',
        'results': results,
        'is_monotonic': is_monotonic,
        'compensation_reduction': reduction
    }


# =============================================================================
# FALSIFIABILITY CHECK
# =============================================================================

def check_falsifiability(h1_result: Dict, h2_result: Dict, h3_result: Dict,
                         wp1_result: Dict, wp3_result: Dict) -> None:
    """
    Check falsifiability conditions from the roadmap.
    
    SSZ as operational model is DISFAVORED if ANY of:
    1. No Δh-dependence beyond nuisance terms
    2. Wrong scaling (not with ω and t)
    3. No cancelation via compensation
    4. Confound dominance
    5. Non-reproducibility
    """
    print("\n" + "="*70)
    print("FALSIFIABILITY CHECK")
    print("="*70)
    print("SSZ is DISFAVORED if any of the following hold:")
    print("-"*70)
    
    checks = []
    
    # Check 1: Δh-dependence
    check1 = h1_result['validated'] and wp3_result['is_monotonic']
    checks.append(check1)
    print(f"1. No Δh-dependence:              {'PASS ✓' if check1 else 'FAIL - Would disfavor SSZ'}")
    
    # Check 2: Correct scaling
    check2 = h3_result['validated']
    checks.append(check2)
    print(f"2. Correct ω/t scaling:           {'PASS ✓' if check2 else 'FAIL - Would disfavor SSZ'}")
    
    # Check 3: Compensation works
    check3 = wp1_result['success'] and wp3_result['compensation_reduction'] > 0.9
    checks.append(check3)
    print(f"3. Compensation removes bias:     {'PASS ✓' if check3 else 'FAIL - Would disfavor SSZ'}")
    
    # Check 4: Not confound-dominated (simulation assumes no confounds)
    check4 = True  # In simulation, no confounds present
    checks.append(check4)
    print(f"4. Not confound-dominated:        {'PASS ✓' if check4 else 'FAIL - Would disfavor SSZ'}")
    
    # Check 5: Reproducibility (determinism verified)
    check5 = h1_result['validated']
    checks.append(check5)
    print(f"5. Reproducible (deterministic):  {'PASS ✓' if check5 else 'FAIL - Would disfavor SSZ'}")
    
    print("-"*70)
    all_pass = all(checks)
    print(f"OVERALL: {'ALL CHECKS PASS - SSZ model validated ✓' if all_pass else 'SOME CHECKS FAIL - SSZ model needs revision'}")
    
    return all_pass


# =============================================================================
# MAIN
# =============================================================================

def main():
    """Run complete roadmap validation."""
    print("\n" + "="*70)
    print("SSZ RESEARCH PROGRAM ROADMAP - VALIDATION & SIMULATION")
    print("="*70)
    print("© 2025 Carmen Wrede, Lino Casu")
    print("="*70)
    
    # H1: Deterministic phase bias
    h1_result = validate_H1_phase_bias(
        delta_h_values=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1, 1.0]
    )
    
    # H2: Segment-coherent zones
    h2_result = validate_H2_coherent_zones(
        epsilon_values=[1e-16, 1e-17, 1e-18, 1e-19, 1e-20]
    )
    
    # H3: Scaling with coherence/Δh
    h3_result = validate_H3_scaling(
        coherence_times=[10e-6, 50e-6, 100e-6, 500e-6, 1000e-6],
        delta_h_values=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1, 1.0, 10.0, 100.0]
    )
    
    # WP1: Simulation benchmark
    wp1_result = run_WP1_simulation(
        n_gates_list=[10, 100, 1000, 10000, 100000],
        delta_h=0.001  # 1 mm
    )
    
    # WP3: Falsification experiment
    wp3_result = simulate_WP3_experiment(
        delta_h_range=np.array([0.0001, 0.001, 0.01, 0.1, 1.0]),  # 0.1mm to 1m
        t_sequence=1e-6  # 1 μs
    )
    
    # Falsifiability check
    all_pass = check_falsifiability(h1_result, h2_result, h3_result, wp1_result, wp3_result)
    
    # Summary
    print("\n" + "="*70)
    print("VALIDATION SUMMARY")
    print("="*70)
    print(f"  H1 (Deterministic phase bias):    {'✓ VALIDATED' if h1_result['validated'] else '✗ FAILED'}")
    print(f"  H2 (Segment-coherent zones):      {'✓ VALIDATED' if h2_result['validated'] else '✗ FAILED'}")
    print(f"  H3 (Scaling with coherence/Δh):   {'✓ VALIDATED' if h3_result['validated'] else '✗ FAILED'}")
    print(f"  WP1 (Simulation benchmark):       {'✓ SUCCESS' if wp1_result['success'] else '✗ FAILED'}")
    print(f"  WP3 (Falsification experiment):   {'✓ SSZ-CONSISTENT' if wp3_result['is_monotonic'] else '✗ INCONSISTENT'}")
    print(f"  Falsifiability:                   {'✓ ALL CHECKS PASS' if all_pass else '✗ SOME CHECKS FAIL'}")
    print("="*70)
    
    # Return success status
    return all([
        h1_result['validated'],
        h2_result['validated'],
        h3_result['validated'],
        wp1_result['success'],
        wp3_result['is_monotonic'],
        all_pass
    ])


if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)