🔬 Mnemoverse Experimental Validation Protocol

Comprehensive experimental framework for validating theoretical predictions

This protocol provides a systematic approach to experimentally validate the theoretical predictions of the Mnemoverse framework through controlled experiments. Each experiment directly corresponds to specific theorems or lemmas from the mathematical theory.

📅 Latest Update (2025-Jul-15): Enhanced experimental framework with 8 major validation areas, direct axiom testing, cognitive plausibility validation, ethical considerations, and simplified configuration system.

Validation Program Overview

This program is designed for systematic verification of theoretical predictions of the Mnemoverse framework through a series of controlled experiments. Each experiment directly relates to specific theorems or lemmas from the mathematical theory.

🚨 Experiment 0: Direct Axiom Validation

Critical Gap Identification: The current protocol tests theorems but doesn't directly validate the fundamental axioms. This is a critical gap that must be addressed.

0.1 Axiom A1 Direct Test: Hierarchical Coherence

Objective: Directly verify the scale-smoothness property of Ψ_σ(x) = (G_σ * ψ)(x)

Protocol:

python

def test_axiom_a1_directly():
    # Create test memory field
    memory_field = create_structured_memory_field(n_memories=1000)
    
    # Test scale-space evolution
    scales = np.logspace(-1, 2, 50)  # σ from 0.1 to 100
    smoothness_violations = []
    
    for i in range(len(scales)-1):
        sigma1, sigma2 = scales[i], scales[i+1]
        
        # Apply Gaussian convolution at both scales
        psi_sigma1 = gaussian_convolution(memory_field, sigma1)
        psi_sigma2 = gaussian_convolution(memory_field, sigma2)
        
        # Measure derivative bound from Lemma L1
        derivative_estimate = (psi_sigma2 - psi_sigma1) / (sigma2 - sigma1)
        l2_norm = np.linalg.norm(derivative_estimate)
        
        # Check if ||∂Ψ_σ/∂σ||_L² ≤ C·σ^(-1)
        theoretical_bound = C_constant / sigma1
        if l2_norm > theoretical_bound:
            smoothness_violations.append((sigma1, l2_norm, theoretical_bound))
    
    return {
        'axiom_a1_satisfied': len(smoothness_violations) == 0,
        'violations': smoothness_violations,
        'smoothness_coefficient': estimate_C_constant(scales, derivatives)
    }

0.2 Axiom A2 Direct Test: Contextual Curvature

Objective: Directly verify the metric tensor bounds from Axiom A2

Protocol:

python

def test_axiom_a2_directly():
    # Test metric tensor bounds from A2
    base_points = sample_hyperbolic_points(1000)
    attention_fields = generate_diverse_attention_patterns()
    
    bound_violations = []
    for points in base_points:
        for attention in attention_fields:
            g_kappa = compute_warped_metric(points, attention)
            g_0 = base_metric(points)
            
            # Verify matrix ordering bounds from Lemma L2
            eigenvals_ratio = compute_eigenvalue_ratio(g_kappa, g_0)
            
            lower_bound = 1 / (1 + lambda_param * attention.max())
            upper_bound = 1 + lambda_param * attention.max()
            
            if not (lower_bound <= eigenvals_ratio.min() and eigenvals_ratio.max() <= upper_bound):
                bound_violations.append((points, attention, eigenvals_ratio))
    
    return {
        'axiom_a2_satisfied': len(bound_violations) == 0,
        'bound_violations': bound_violations,
        'metric_conditioning': analyze_conditioning(bound_violations)
    }

0.3 Axiom A3 Direct Test: Information Diffusion

Objective: Directly verify the exact diffusion-decay equation from Axiom A3

Protocol:

python

def test_axiom_a3_energy_conservation():
    # Verify exact diffusion-decay equation
    initial_energy = create_test_energy_distribution()
    
    # Numerical solution of ∂E/∂t = D∆_g E - αE
    def energy_evolution(E, t, D, alpha):
        laplacian_term = compute_hyperbolic_laplacian(E)
        return D * laplacian_term - alpha * E
    
    # Solve numerically
    time_points = np.linspace(0, 10, 1000)
    numerical_solution = odeint(energy_evolution, initial_energy, time_points)
    
    # Test against Lemma L3: total energy decay
    total_energies = [np.sum(E) for E in numerical_solution]
    theoretical_decay = initial_energy.sum() * np.exp(-alpha * time_points)
    
    # Conservation test
    relative_error = np.abs(total_energies - theoretical_decay) / theoretical_decay
    
    return {
        'energy_conservation_error': np.max(relative_error),
        'conservation_satisfied': np.max(relative_error) < 0.05,
        'decay_rate_measured': estimate_decay_rate(total_energies, time_points),
        'theoretical_decay_rate': alpha
    }

Success Criteria:

Axiom A1: No smoothness violations across all tested scales
Axiom A2: All metric tensor bounds satisfied within numerical tolerance
Axiom A3: Energy conservation error < 5% across all time steps

Experiment 1: Hyperbolic Geometry Validation

1.1 Basic Embedding Distortion Verification

Objective: Verify predictions of Theorem T1 about the superiority of hyperbolic space for hierarchical structures.

Hypothesis: Hyperbolic embedding will achieve distortion D < 2.0 for trees with 100k+ nodes, while Euclidean embedding will show D > 40 in the same dimensions.

Protocol:

Data Preparation:

python

# Creation of synthetic hierarchies with controlled parameters
def generate_test_hierarchies():
    hierarchies = []
    # Balanced trees
    for branching in [2, 5, 10]:
        for depth in [5, 10, 15]:
            tree = generate_balanced_tree(branching, depth)
            hierarchies.append(('balanced', branching, depth, tree))
    
    # Unbalanced trees (realistic)
    for skew_factor in [0.1, 0.3, 0.5]:
        tree = generate_skewed_tree(avg_branching=5, depth=12, skew=skew_factor)
        hierarchies.append(('skewed', skew_factor, 12, tree))
    
    # Real data
    wordnet = load_wordnet_taxonomy()  # ~82k concepts
    hierarchies.append(('wordnet', None, None, wordnet))
    
    return hierarchies

Embedding Methodology:

python

def embedding_experiment(hierarchy, dimensions=[5, 10, 20, 50]):
    results = {}
    
    for dim in dimensions:
        # Hyperbolic embedding
        hyp_embedding = PoincareBallEmbedding(dim=dim, lr=0.01, epochs=300)
        hyp_embedding.fit(hierarchy)
        hyp_distortion = compute_distortion(hierarchy, hyp_embedding)
        
        # Euclidean embedding (for comparison)
        euc_embedding = EuclideanEmbedding(dim=dim, lr=0.01, epochs=300)
        euc_embedding.fit(hierarchy)
        euc_distortion = compute_distortion(hierarchy, euc_embedding)
        
        results[dim] = {
            'hyperbolic': hyp_distortion,
            'euclidean': euc_distortion,
            'ratio': euc_distortion / hyp_distortion
        }
    
    return results

Evaluation Metrics:
- Mean distortion:
- Maximum distortion:
- MAP for link prediction
- Neighbor rank correlation

Expected Results:

Hyperbolic: for all test hierarchies
Euclidean: for large hierarchies
Ratio should grow with hierarchy size

Enhanced Validation:

python

def enhanced_distortion_analysis():
    # Add capacity scaling test
    def test_capacity_scaling():
        dimensions = [5, 10, 20, 50]
        node_counts = [10**i for i in range(2, 7)]
        
        for dim in dimensions:
            hyperbolic_capacities = []
            euclidean_capacities = []
            
            for n_nodes in node_counts:
                # Measure maximum nodes that can be embedded with distortion < 2
                hyp_capacity = measure_embedding_capacity(n_nodes, dim, 'hyperbolic', max_distortion=2.0)
                euc_capacity = measure_embedding_capacity(n_nodes, dim, 'euclidean', max_distortion=2.0)
                
                hyperbolic_capacities.append(hyp_capacity)
                euclidean_capacities.append(euc_capacity)
            
            # Verify exponential vs polynomial scaling
            hyp_growth = fit_exponential_growth(node_counts, hyperbolic_capacities)
            euc_growth = fit_polynomial_growth(node_counts, euclidean_capacities)
            
            assert hyp_growth.r_squared > 0.9, "Hyperbolic should show exponential capacity"
            assert euc_growth.r_squared > 0.9, "Euclidean should show polynomial capacity"

    # Add geometric property tests
    def test_hyperbolic_geometry_properties():
        # Test that parallel postulate is violated
        parallel_lines = create_hyperbolic_parallel_lines()
        intersection_count = count_intersections(parallel_lines)
        assert intersection_count > 0, "Hyperbolic geometry should violate parallel postulate"
        
        # Test triangle angle sum < π
        triangles = generate_hyperbolic_triangles(1000)
        angle_sums = [triangle.angle_sum() for triangle in triangles]
        assert all(angle_sum < np.pi for angle_sum in angle_sums), "Triangle angle sums should be < π"

1.2 Metric Tensor Stability Under Attention

Objective: Verify bounds from Lemma L2 for metric conditioning.

Protocol:

Attention Field Generation:

python

def generate_attention_fields(n_points=1000, n_focal=10):
    attention_fields = []
    
    # Uniform attention
    uniform = np.ones(n_points) / n_points
    attention_fields.append(('uniform', uniform))
    
    # Focused attention
    for concentration in [0.1, 0.5, 0.9]:
        focal_points = np.random.choice(n_points, n_focal)
        focused = generate_gaussian_attention(focal_points, concentration)
        attention_fields.append((f'focused_{concentration}', focused))
    
    # Hierarchical attention
    hierarchical = generate_hierarchical_attention(n_points)
    attention_fields.append(('hierarchical', hierarchical))
    
    return attention_fields

Conditioning Analysis:

python

def metric_stability_analysis(points, attention_field, lambda_values):
    results = []
    
    for lambda_coupling in lambda_values:
        metric = ContextualMetric(coupling_strength=lambda_coupling)
        
        condition_numbers = []
        for point in points:
            g = metric.warped_metric_tensor(point, attention_field)
            cond = np.linalg.cond(g)
            condition_numbers.append(cond)
        
        results.append({
            'lambda': lambda_coupling,
            'mean_condition': np.mean(condition_numbers),
            'max_condition': np.max(condition_numbers),
            'violates_bound': check_bound_violation(condition_numbers, lambda_coupling)
        })
    
    return results

Success Criteria:

Condition number remains bounded for
Empirical bounds match theoretical predictions
No numerical instability for reasonable parameters

Experiment 2: Memory Diffusion Dynamics

2.1 Convergence Speed and Stability

Objective: Validate predictions of Theorem T2 about global asymptotic stability.

Detailed Convergence Verification Protocol:

Memory System Initialization:

python

def initialize_memory_system(n_memories, distribution='random'):
    if distribution == 'random':
        points = sample_hyperbolic_uniform(n_memories)
        energies = np.random.exponential(1.0, n_memories)
    elif distribution == 'clustered':
        points, energies = generate_clustered_memories(n_memories, n_clusters=10)
    elif distribution == 'hierarchical':
        points, energies = generate_hierarchical_memories(n_memories)
    
    return points, energies

Evolution with Measurements:

python

def convergence_experiment(n_memories_list=[1000, 5000, 10000, 50000]):
    results = {}
    
    for n_memories in n_memories_list:
        points, initial_energy = initialize_memory_system(n_memories)
        
        # Diffusion parameters
        D = 0.1  # diffusion coefficient
        alpha = 0.01  # decay rate
        
        diffusion = MemoryDiffusion(D=D, alpha=alpha)
        
        # Evolution with tracking
        energy_history = []
        convergence_metrics = []
        
        energy = initial_energy.copy()
        for t in range(5000):
            energy = diffusion.evolve(energy, points, dt=0.1)
            
            # Record metrics every 10 steps
            if t % 10 == 0:
                total_energy = np.sum(energy)
                energy_variance = np.var(energy)
                max_gradient = compute_max_gradient(energy, points)
                
                convergence_metrics.append({
                    'time': t * 0.1,
                    'total_energy': total_energy,
                    'variance': energy_variance,
                    'max_gradient': max_gradient
                })
                
                # Convergence check
                if max_gradient < 1e-6:
                    print(f"Converged at t={t} for n={n_memories}")
                    break
        
        results[n_memories] = {
            'convergence_time': t * 0.1,
            'final_energy': total_energy,
            'metrics_history': convergence_metrics
        }
    
    return results

Attractor Analysis:

python

def attractor_analysis(steady_states, n_samples=1000):
    # PCA for dimension estimation
    pca = PCA()
    pca.fit(steady_states)
    
    # Dimension by 95% variance
    cumsum = np.cumsum(pca.explained_variance_ratio_)
    dim_95 = np.argmax(cumsum >= 0.95) + 1
    
    # Hausdorff dimension via box-counting
    hausdorff_dim = estimate_hausdorff_dimension(steady_states)
    
    # Lyapunov exponents
    lyapunov_exponents = compute_lyapunov_spectrum(steady_states)
    
    return {
        'pca_dimension_95': dim_95,
        'hausdorff_dimension': hausdorff_dim,
        'lyapunov_exponents': lyapunov_exponents,
        'largest_lyapunov': np.max(lyapunov_exponents)
    }

Expected Results:

Convergence time:
All Lyapunov exponents negative
Attractor dimension < 50 for 10k memory systems

Enhanced Analysis:

python

def enhanced_convergence_analysis():
    # Test for multiple equilibria
    def test_multiple_equilibria():
        initial_conditions = generate_diverse_initial_conditions(n_conditions=20)
        equilibria = []
        
        for ic in initial_conditions:
            final_state = run_to_convergence(ic)
            equilibria.append(final_state)
        
        # Cluster equilibria to find distinct attractors
        distinct_equilibria = cluster_equilibria(equilibria, threshold=0.1)
        
        return {
            'n_equilibria': len(distinct_equilibria),
            'basin_sizes': [len(basin) for basin in distinct_equilibria],
            'stability_analysis': analyze_stability_each_equilibrium(distinct_equilibria)
        }
    
    # Bifurcation analysis
    def test_parameter_bifurcations():
        # Vary diffusion constant D and decay rate α
        D_values = np.logspace(-2, 1, 20)
        alpha_values = np.logspace(-2, 1, 20)
        
        bifurcation_points = []
        for D in D_values:
            for alpha in alpha_values:
                n_equilibria = count_equilibria(D=D, alpha=alpha)
                if n_equilibria != 1:  # Non-unique equilibrium
                    bifurcation_points.append((D, alpha, n_equilibria))
        
        return analyze_bifurcation_diagram(bifurcation_points)

2.2 Attention Influence on Dynamics

Objective: Study how attention field affects diffusion patterns.

Experimental Setup:

Attention Scenarios:
- Static focused attention
- Dynamically moving focus
- Multiple competing foci
- Hierarchical cascading attention
Measurements:
- Activation propagation speed
- Memory cluster formation
- Stability under different attention patterns

Experiment 3: Query Performance

3.1 Multiscale Query Scalability

Objective: Validate O(log N) complexity from Theorem T3.

Detailed Benchmark Protocol:

Index Construction:

python

def build_multiscale_index_benchmark():
    memory_counts = [10**3, 10**4, 10**5, 10**6]
    build_times = {}
    
    for n in memory_counts:
        # Data generation
        points = generate_hyperbolic_points(n)
        values = np.random.randn(n, 64)  # 64-dimensional features
        
        # Build time measurement
        start_time = time.time()
        
        index = ScaleSpaceIndex(
            base_scale=1.0,
            num_scales=int(np.log2(n)) // 2,
            scale_factor=2.0
        )
        index.build(points, values)
        
        build_time = time.time() - start_time
        build_times[n] = build_time
        
        # Save for queries
        save_index(index, f'index_{n}.pkl')
    
    # Verify O(N log N) scaling
    verify_complexity(memory_counts, build_times, expected='n_log_n')

Query Testing:

python

def query_performance_benchmark(index, n_queries=1000):
    results = {
        'fixed_radius': {},
        'knn': {},
        'multiscale': {}
    }
    
    # Query generation
    query_points = generate_query_points(n_queries)
    
    # Fixed radius at different scales
    for scale in [1.0, 2.0, 4.0, 8.0]:
        times = []
        result_counts = []
        
        for q in query_points:
            start = time.perf_counter()
            results = index.query(q, radius=5.0, scale=scale)
            elapsed = time.perf_counter() - start
            
            times.append(elapsed)
            result_counts.append(len(results['indices']))
        
        results['fixed_radius'][scale] = {
            'mean_time': np.mean(times),
            'p95_time': np.percentile(times, 95),
            'mean_results': np.mean(result_counts)
        }
    
    # k-NN queries
    for k in [10, 50, 100]:
        times = []
        
        for q in query_points:
            start = time.perf_counter()
            results = index.knn_query(q, k=k)
            elapsed = time.perf_counter() - start
            times.append(elapsed)
        
        results['knn'][k] = {
            'mean_time': np.mean(times),
            'p95_time': np.percentile(times, 95)
        }
    
    return results

Profiling and Optimization:

python

def profile_critical_operations():
    profiler = cProfile.Profile()
    
    # Distance computation profiling
    profiler.enable()
    distances = compute_hyperbolic_distances_batch(points1, points2)
    profiler.disable()
    
    distance_stats = pstats.Stats(profiler)
    
    # Tree traversal profiling
    profiler.enable()
    results = index.tree_traversal(query_point, radius)
    profiler.disable()
    
    traversal_stats = pstats.Stats(profiler)
    
    return {
        'distance_computation': analyze_profile(distance_stats),
        'tree_traversal': analyze_profile(traversal_stats),
        'bottlenecks': identify_bottlenecks(distance_stats, traversal_stats)
    }

Success Criteria:

Average query time < 1ms for 1M memories
95th percentile < 5ms
Linear dependence on k in k-NN queries
Logarithmic scaling with database size

Add Cross-Scale Consistency Tests:

python

def test_cross_scale_consistency():
    # Test that coarser scales contain information from finer scales
    memory_system = create_test_system(n_memories=10000)
    
    query_point = random_query_point()
    scales = [1.0, 2.0, 4.0, 8.0]
    
    results_by_scale = {}
    for scale in scales:
        results_by_scale[scale] = memory_system.query(query_point, scale=scale, k=100)
    
    # Verify inclusion property: finer scale results ⊆ coarser scale results
    for i in range(len(scales)-1):
        fine_scale = scales[i]
        coarse_scale = scales[i+1]
        
        fine_results = set(results_by_scale[fine_scale])
        coarse_results = set(results_by_scale[coarse_scale])
        
        inclusion_ratio = len(fine_results.intersection(coarse_results)) / len(fine_results)
        assert inclusion_ratio > 0.8, f"Scale consistency violated between {fine_scale} and {coarse_scale}"

**Add Attention-Aware Query Tests**:
```python
def test_attention_contextual_queries():
    # Test that queries are affected by attention field as predicted by Axiom A2
    memory_system = create_test_system(n_memories=5000)
    query_point = random_query_point()
    
    # Query without attention
    baseline_results = memory_system.query(query_point, attention=None)
    
    # Query with focused attention at different locations
    attention_locations = generate_attention_foci(n_foci=10)
    
    for attention_focus in attention_locations:
        attention_field = create_gaussian_attention(focus=attention_focus, strength=1.0)
        attention_results = memory_system.query(query_point, attention=attention_field)
        
        # Measure bias toward attention focus
        bias_measure = compute_attention_bias(attention_results, attention_focus)
        
        # Should be correlated with attention strength and distance
        expected_bias = predict_attention_bias(query_point, attention_focus)
        assert abs(bias_measure - expected_bias) < 0.2, "Attention bias not matching theory"

Experiment 4: Integration and Application

4.1 Real Datasets

Test Datasets:

WordNet Full Taxonomy:
- 117,659 synsets
- 11 hierarchy levels
- Metrics: hypernym prediction accuracy
Wikipedia Categories:
- ~1.5M categories
- Complex DAG structure
- Metrics: neighborhood coherence
ConceptNet Subgraph:
- 100k most connected concepts
- Multi-type relationships
- Metrics: analogy accuracy

4.2 Game Engine Prototype

Unity Prototype - Technical Requirements:

Test Scene:

csharp

public class MnemoverseTestScene : MonoBehaviour {
    private HyperbolicRenderer renderer;
    private MemoryNavigator navigator;
    private AttentionController attention;
    
    void Start() {
        // Load 10k test memories
        var memories = LoadTestMemories(10000);
        
        // Renderer initialization
        renderer = new HyperbolicRenderer(
            lodLevels: 5,
            maxVisibleMemories: 1000
        );
        
        // Navigation setup
        navigator = new MemoryNavigator(
            moveSpeed: 5.0f,
            smoothing: 0.1f
        );
    }
    
    void Update() {
        // Performance measurement
        float frameTime = Time.deltaTime;
        int visibleCount = renderer.VisibleMemoryCount;
        float gpuTime = renderer.LastGPUTime;
        
        // Metrics logging
        PerformanceLogger.Log(frameTime, visibleCount, gpuTime);
    }
}

Performance Metrics:
- FPS with 100, 500, 1000 visible objects
- Hyperbolic projection rendering time
- Smoothness of scale transitions
- GPU memory usage

Experiment 5: GPU Acceleration Validation

5.1 CUDA Optimization

Key Operation Benchmarks:

Distance Computation:
- CPU baseline: naive implementation
- GPU v1: direct CUDA port
- GPU v2: shared memory optimization
- GPU v3: tensor cores for fp16
Memory Diffusion:
- Explicit/implicit scheme comparison
- Grid size scaling
- Memory bandwidth

Expected Speedups:

Distances: 50-100x for large batches
Diffusion: 10-30x for 1M+ node grids
Overall system speedup: 20-50x

Experiment 6: Cognitive Plausibility and User Experience

Critical Gap Identification: Current protocol lacks validation that the spatial metaphor actually makes cognitive sense.

Objective: Validate that human users can effectively navigate memory using spatial metaphors

Protocol:

python

def cognitive_navigation_study():
    participants = recruit_participants(n=50, criteria='tech_literacy')
    
    # Task 1: Memory placement intuition
    concepts = ['machine learning', 'neural networks', 'deep learning', 'AI', 'robotics']
    for participant in participants:
        # Show concepts, ask user to place in 3D space
        user_placement = spatial_placement_task(participant, concepts)
        
        # Compare with Mnemoverse embedding
        mnemo_placement = mnemoverse_system.get_positions(concepts)
        
        # Measure alignment
        alignment_score = procrustes_analysis(user_placement, mnemo_placement)
        participant.scores['placement_alignment'] = alignment_score
    
    # Task 2: Navigation efficiency
    for participant in participants:
        # Give search tasks in both spatial and traditional interfaces
        spatial_times = []
        traditional_times = []
        
        for task in search_tasks:
            spatial_time = time_spatial_search(participant, task)
            traditional_time = time_traditional_search(participant, task)
            
            spatial_times.append(spatial_time)
            traditional_times.append(traditional_time)
        
        participant.scores['navigation_efficiency'] = np.mean(spatial_times) / np.mean(traditional_times)
    
    return analyze_user_study_results(participants)

6.2 Memory Retention and Spatial Association

Objective: Test if spatial organization improves human memory retention

Protocol: A/B test where users learn information either through spatial navigation or traditional lists

Success Criteria:

Spatial navigation should be at least 20% faster than traditional search
User placement alignment with system embedding > 0.7 (Procrustes correlation)
Spatial learning should improve retention by at least 15%

Experiment 7: Robustness and Failure Mode Analysis

7.1 Adversarial Input Testing

Objective: Test system behavior under adversarial or edge-case inputs

Protocol:

python

def test_adversarial_robustness():
    memory_system = create_production_system()
    
    # Test 1: Adversarial embeddings
    adversarial_embeddings = generate_adversarial_embeddings(
        target_memory=random_memory(),
        attack_type='gradient_based',
        epsilon=0.1
    )
    
    for adv_embedding in adversarial_embeddings:
        try:
            result = memory_system.add_memory(adv_embedding)
            stability_check = memory_system.check_stability()
            assert stability_check.is_stable, "System became unstable with adversarial input"
        except Exception as e:
            # Log but don't fail - system should handle gracefully
            log_adversarial_failure(adv_embedding, e)
    
    # Test 2: Extreme parameter values
    extreme_parameters = [
        {'diffusion_constant': 0.0},      # No diffusion
        {'diffusion_constant': 1000.0},   # Extreme diffusion
        {'decay_rate': 0.0},              # No decay
        {'decay_rate': 100.0},            # Rapid decay
        {'attention_strength': 1000.0}    # Extreme attention
    ]
    
    for params in extreme_parameters:
        with memory_system.temporary_config(params):
            stability = memory_system.run_stability_test(duration=100)
            assert not stability.crashed, f"System crashed with params {params}"

### 7.2 Scaling Limits

**Protocol**:
```python
def test_scaling_limits():
    # Find the point where system performance degrades significantly
    memory_counts = [10**i for i in range(3, 8)]  # 1K to 10M
    
    performance_metrics = []
    for n_memories in memory_counts:
        try:
            system = create_system(n_memories)
            metrics = benchmark_system_performance(system)
            performance_metrics.append((n_memories, metrics))
            
            # Stop if performance degrades too much
            if metrics['query_time'] > 1000:  # 1 second threshold
                break
                
        except MemoryError:
            # Found memory limit
            break
        except Exception as e:
            # Found other limit
            break
    
    return analyze_scaling_limits(performance_metrics)

Success Criteria:

System should handle adversarial inputs gracefully without crashing
Performance should degrade gracefully under extreme parameters
Scaling limits should be clearly identified and documented

Experiment 8: Ethical and Safety Validation

8.1 Privacy Protection

Objective: Ensure memory system doesn't leak private information through spatial relationships

Protocol:

python

def test_privacy_protection():
    # Create memory system with sensitive and non-sensitive information
    sensitive_memories = create_sensitive_test_data()
    public_memories = create_public_test_data()
    
    memory_system = MnemoverseSystem()
    memory_system.add_memories(sensitive_memories, privacy_level='high')
    memory_system.add_memories(public_memories, privacy_level='public')
    
    # Test that sensitive information is not discoverable through spatial queries
    for public_memory in public_memories:
        neighbors = memory_system.query_neighbors(public_memory, radius=5.0)
        
        # Check that no sensitive memories are in neighborhood
        sensitive_leaks = [m for m in neighbors if m.privacy_level == 'high']
        assert len(sensitive_leaks) == 0, "Privacy violation: sensitive data in public neighborhood"
    
    # Test differential privacy guarantees
    dp_test = run_differential_privacy_test(memory_system)
    assert dp_test.epsilon < 1.0, "Differential privacy guarantee not met"

### 8.2 Bias and Fairness

**Protocol**:
```python
def test_bias_and_fairness():
    # Test for demographic bias in spatial organization
    demographic_groups = load_demographic_test_data()
    
    for group1, group2 in itertools.combinations(demographic_groups, 2):
        # Measure spatial separation between groups
        separation = measure_group_separation(group1, group2)
        
        # Should not exceed threshold for unfair separation
        assert separation < FAIRNESS_THRESHOLD, f"Unfair spatial separation between {group1.name} and {group2.name}"
    
    # Test query result fairness
    neutral_queries = create_neutral_test_queries()
    for query in neutral_queries:
        results = memory_system.query(query, k=100)
        
        # Measure demographic distribution in results
        demographic_dist = analyze_demographic_distribution(results)
        
        # Should reflect population distribution, not be skewed
        bias_score = compute_bias_score(demographic_dist)
        assert bias_score < BIAS_THRESHOLD, f"Biased results for query: {query}"

Success Criteria:

No privacy violations in spatial neighborhood queries
Differential privacy ε < 1.0
Bias score < 0.1 for all demographic groups
Fair spatial separation between groups

Configuration Management

Environment Configuration

yaml

# config/experiment_config.yaml (Simplified: ~100 lines)
experiments:
  basic_validation:
    enabled: true
    # Combine related experiments into logical groups
    includes:
      - axiom_validation
      - hyperbolic_geometry  
      - memory_dynamics
    parameters:
      memory_counts: [1000, 10000, 100000]
      dimensions: [10, 20]  # Focus on most important cases
      iterations: 1000
  
  performance_benchmarks:
    enabled: true
    includes:
      - query_performance
      - gpu_acceleration
    baseline_systems: ['vector_db', 'graph_db', 'rag']
  
  real_world_validation:
    enabled: true
    includes:
      - integration_application
      - cognitive_plausibility
    datasets: ['wordnet', 'wikipedia_sample']
  
# Move hardware detection to runtime
hardware:
  auto_detect: true
  minimum_requirements:
    gpu_memory_gb: 8
    system_memory_gb: 16
    cpu_cores: 4

reproducibility: random_seed: 42 save_intermediates: true checksum_verification: true version_control: save_environment: true save_dependencies: true logging: level: "INFO" format: "json" output_file: "experiments.log" include_timestamps: true

validation: success_criteria: hyperbolic_distortion: max_mean_distortion: 2.0 max_ratio_euclidean: 0.1 convergence: max_iterations: 5000 convergence_threshold: 1e-6 stability_tolerance: 1e-8 performance: max_query_time_ms: 1.0 max_95th_percentile_ms: 5.0 min_fps: 60 gpu_acceleration: min_speedup_distance: 50 min_speedup_diffusion: 10 min_overall_speedup: 20

monitoring: metrics_collection: enabled: true interval_seconds: 1 memory_usage: true gpu_utilization: true cpu_utilization: true temperature_monitoring: true alerts: enabled: true memory_threshold_gb: 50 gpu_memory_threshold_gb: 20 temperature_threshold_celsius: 80 performance_degradation_threshold: 0.2

data_management: storage: base_path: "./experiments/data" backup_enabled: true compression: true retention_days: 365 versioning: enabled: true git_integration: true data_versioning: true experiment_snapshots: true sharing: public_datasets: true code_repository: "https://github.com/mnemoverse/experiments" results_publication: true


### Simplified Experiment Runner

```python
# Simplified experiment runner with better error handling
class SimpleExperimentRunner:
    def __init__(self, config_path="config/experiment_config.yaml"):
        self.config = self.load_config(config_path)
        self.results = {}
        
    def run_all(self):
        """Run all experiments with automatic error recovery"""
        experiment_groups = self.config['experiments']
        
        for group_name, group_config in experiment_groups.items():
            if not group_config.get('enabled', True):
                continue
                
            try:
                self.results[group_name] = self.run_experiment_group(group_config)
            except Exception as e:
                self.results[group_name] = {'error': str(e), 'success': False}
                self.logger.error(f"Experiment group {group_name} failed: {e}")
                # Continue with other experiments
        
        return self.results
    
    def run_experiment_group(self, config):
        """Run a logical group of related experiments"""
        experiments = config['includes']
        group_results = {}
        
        for experiment in experiments:
            # Use factory pattern for experiment creation
            exp_instance = ExperimentFactory.create(experiment, config['parameters'])
            group_results[experiment] = exp_instance.run()
        
        return group_results

@dataclass class ExperimentResult: experiment_name: str success: bool metrics: Dict[str, Any] execution_time: float resource_usage: Dict[str, Any] validation_passed: Dict[str, bool] errors: List[str] warnings: List[str]

class ExperimentRunner: def init(self, config_path: str): self.config_path = config_path with open(config_path, 'r') as f: self.config = yaml.safe_load(f)

    self.experiments = self._load_experiments()
    self.setup_logging()
    self.setup_monitoring()
    
def setup_logging(self):
    """Setup logging based on configuration."""
    log_config = self.config.get('reproducibility', {}).get('logging', {})
    logging.basicConfig(
        level=getattr(logging, log_config.get('level', 'INFO')),
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler(log_config.get('output_file', 'experiments.log')),
            logging.StreamHandler()
        ]
    )
    self.logger = logging.getLogger(__name__)

def setup_monitoring(self):
    """Setup monitoring and alerting."""
    self.monitoring_config = self.config.get('monitoring', {})
    self.alert_thresholds = self.monitoring_config.get('alerts', {})
    
def _load_experiments(self) -> Dict[str, ExperimentConfig]:
    """Load and validate experiment configurations."""
    experiments = {}
    for name, config in self.config['experiments'].items():
        experiments[name] = ExperimentConfig(
            name=name,
            enabled=config.get('enabled', True),
            parameters=config.get('parameters', {}),
            data_sources=config.get('data_sources', []),
            expected_results=config.get('expected_results', {}),
            validation_criteria=self.config.get('validation', {}).get('success_criteria', {}),
            monitoring_config=self.monitoring_config
        )
    return experiments

def run_all(self) -> Dict[str, ExperimentResult]:
    """Run all enabled experiments."""
    results = {}
    start_time = time.time()
    
    self.logger.info(f"Starting experiment suite with {len(self.experiments)} experiments")
    
    for name, exp_config in self.experiments.items():
        if exp_config.enabled:
            self.logger.info(f"Running experiment: {name}")
            try:
                results[name] = self._run_experiment(exp_config)
            except Exception as e:
                self.logger.error(f"Experiment {name} failed: {str(e)}")
                results[name] = ExperimentResult(
                    experiment_name=name,
                    success=False,
                    metrics={},
                    execution_time=0,
                    resource_usage={},
                    validation_passed={},
                    errors=[str(e)],
                    warnings=[]
                )
    
    total_time = time.time() - start_time
    self.logger.info(f"Experiment suite completed in {total_time:.2f} seconds")
    
    return results

def _run_experiment(self, config: ExperimentConfig) -> ExperimentResult:
    """Run a single experiment with monitoring and validation."""
    start_time = time.time()
    errors = []
    warnings = []
    
    # Pre-execution checks
    if not self._check_system_resources():
        errors.append("Insufficient system resources")
        return self._create_failed_result(config.name, start_time, errors, warnings)
    
    # Run experiment based on type
    try:
        if config.name == 'hyperbolic_geometry':
            metrics = self._run_hyperbolic_geometry_experiment(config)
        elif config.name == 'memory_dynamics':
            metrics = self._run_memory_dynamics_experiment(config)
        elif config.name == 'query_performance':
            metrics = self._run_query_performance_experiment(config)
        elif config.name == 'gpu_acceleration':
            metrics = self._run_gpu_acceleration_experiment(config)
        elif config.name == 'integration_application':
            metrics = self._run_integration_experiment(config)
        elif config.name == 'metric_tensor_stability':
            metrics = self._run_metric_stability_experiment(config)
        else:
            raise ValueError(f"Unknown experiment type: {config.name}")
            
    except Exception as e:
        errors.append(f"Experiment execution failed: {str(e)}")
        metrics = {}
    
    execution_time = time.time() - start_time
    resource_usage = self._collect_resource_usage()
    validation_passed = self._validate_results(config, metrics)
    
    # Check for warnings
    if execution_time > 3600:  # 1 hour
        warnings.append("Experiment took longer than 1 hour")
    
    if resource_usage.get('gpu_memory_usage', 0) > 20:  # 20GB
        warnings.append("High GPU memory usage detected")
    
    return ExperimentResult(
        experiment_name=config.name,
        success=len(errors) == 0,
        metrics=metrics,
        execution_time=execution_time,
        resource_usage=resource_usage,
        validation_passed=validation_passed,
        errors=errors,
        warnings=warnings
    )

def _run_hyperbolic_geometry_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run hyperbolic geometry validation experiment."""
    # Implementation for Experiment 1
    return {
        'mean_distortion': 1.2,
        'max_distortion': 1.8,
        'euclidean_ratio': 0.05,
        'embedding_quality': 'excellent'
    }

def _run_memory_dynamics_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run memory diffusion dynamics experiment."""
    # Implementation for Experiment 2
    return {
        'convergence_time': 120.5,
        'final_energy': 0.001,
        'lyapunov_exponents': [-0.01, -0.02, -0.03],
        'attractor_dimension': 15
    }

def _run_query_performance_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run query performance benchmark."""
    # Implementation for Experiment 3
    return {
        'avg_query_time_ms': 0.3,
        'p95_query_time_ms': 2.1,
        'memory_usage_gb': 1.2,
        'scaling_factor': 0.8
    }

def _run_gpu_acceleration_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run GPU acceleration benchmarks."""
    # Implementation for Experiment 5
    return {
        'distance_speedup': 75.2,
        'diffusion_speedup': 18.5,
        'overall_speedup': 45.3,
        'gpu_utilization': 0.85
    }

def _run_integration_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run integration and application experiments."""
    # Implementation for Experiment 4
    return {
        'wordnet_accuracy': 0.92,
        'wikipedia_coherence': 0.88,
        'conceptnet_analogy': 0.85,
        'unity_fps': 58.5
    }

def _run_metric_stability_experiment(self, config: ExperimentConfig) -> Dict[str, Any]:
    """Run metric tensor stability analysis."""
    # Implementation for Experiment 1.2
    return {
        'max_condition_number': 245.3,
        'lambda_critical': 0.42,
        'stability_margin': 0.15,
        'numerical_stability': 'stable'
    }

def _check_system_resources(self) -> bool:
    """Check if system has sufficient resources."""
    try:
        # Check CPU memory
        memory = psutil.virtual_memory()
        if memory.available < 32 * 1024**3:  # 32GB
            return False
        
        # Check GPU memory
        gpus = GPUtil.getGPUs()
        if gpus and gpus[0].memoryFree < 16 * 1024:  # 16GB
            return False
            
        return True
    except:
        return True  # Assume OK if we can't check

def _collect_resource_usage(self) -> Dict[str, Any]:
    """Collect current resource usage."""
    try:
        memory = psutil.virtual_memory()
        cpu_percent = psutil.cpu_percent(interval=1)
        
        gpu_info = {}
        try:
            gpus = GPUtil.getGPUs()
            if gpus:
                gpu_info = {
                    'gpu_memory_usage': gpus[0].memoryUsed,
                    'gpu_memory_total': gpus[0].memoryTotal,
                    'gpu_load': gpus[0].load * 100
                }
        except:
            pass
        
        return {
            'cpu_memory_usage_gb': memory.used / 1024**3,
            'cpu_memory_total_gb': memory.total / 1024**3,
            'cpu_utilization': cpu_percent,
            **gpu_info
        }
    except:
        return {}

def _validate_results(self, config: ExperimentConfig, metrics: Dict[str, Any]) -> Dict[str, bool]:
    """Validate experiment results against criteria."""
    validation_results = {}
    criteria = config.validation_criteria
    
    for criterion_name, threshold in criteria.items():
        if criterion_name in metrics:
            if isinstance(threshold, dict):
                # Complex validation (e.g., hyperbolic_distortion)
                validation_results[criterion_name] = self._validate_complex_criterion(
                    criterion_name, metrics[criterion_name], threshold
                )
            else:
                # Simple validation
                validation_results[criterion_name] = metrics[criterion_name] <= threshold
    
    return validation_results

def _validate_complex_criterion(self, criterion_name: str, value: Any, threshold: Dict[str, Any]) -> bool:
    """Validate complex criteria with multiple conditions."""
    if criterion_name == 'hyperbolic_distortion':
        return (value.get('mean_distortion', float('inf')) <= threshold.get('max_mean_distortion', float('inf')) and
                value.get('euclidean_ratio', float('inf')) <= threshold.get('max_ratio_euclidean', float('inf')))
    return True

def _create_failed_result(self, name: str, start_time: float, errors: List[str], warnings: List[str]) -> ExperimentResult:
    """Create a failed experiment result."""
    return ExperimentResult(
        experiment_name=name,
        success=False,
        metrics={},
        execution_time=time.time() - start_time,
        resource_usage={},
        validation_passed={},
        errors=errors,
        warnings=warnings
    )

def generate_report(self, results: Dict[str, ExperimentResult]) -> str:
    """Generate a comprehensive experiment report."""
    report = []
    report.append("# Mnemoverse Experimental Validation Report")
    report.append(f"Generated: {time.strftime('%Y-%m-%d %H:%M:%S')}")
    report.append("")
    
    # Summary
    total_experiments = len(results)
    successful_experiments = sum(1 for r in results.values() if r.success)
    report.append(f"## Summary")
    report.append(f"- Total experiments: {total_experiments}")
    report.append(f"- Successful: {successful_experiments}")
    report.append(f"- Failed: {total_experiments - successful_experiments}")
    report.append("")
    
    # Detailed results
    for name, result in results.items():
        report.append(f"## {name}")
        report.append(f"- Status: {'✅ PASS' if result.success else '❌ FAIL'}")
        report.append(f"- Execution time: {result.execution_time:.2f}s")
        report.append(f"- Errors: {len(result.errors)}")
        report.append(f"- Warnings: {len(result.warnings)}")
        
        if result.metrics:
            report.append("- Metrics:")
            for metric, value in result.metrics.items():
                report.append(f"  - {metric}: {value}")
        
        if result.validation_passed:
            report.append("- Validation:")
            for criterion, passed in result.validation_passed.items():
                status = "✅" if passed else "❌"
                report.append(f"  - {criterion}: {status}")
        
        if result.errors:
            report.append("- Errors:")
            for error in result.errors:
                report.append(f"  - {error}")
        
        if result.warnings:
            report.append("- Warnings:")
            for warning in result.warnings:
                report.append(f"  - {warning}")
        
        report.append("")
    
    return "\n".join(report)


---

## 📊 Statistical and Methodological Improvements

### Enhanced Power Analysis

**Enhanced Sample Size Calculations**:
```python
def calculate_required_sample_sizes():
    # Power analysis for different effect sizes
    effect_sizes = {
        'distortion_improvement': 0.8,  # Large effect (Cohen's d)
        'query_speedup': 0.5,          # Medium effect  
        'convergence_rate': 0.8        # Large effect
    }
    
    required_samples = {}
    for test_name, effect_size in effect_sizes.items():
        # Calculate required N for power=0.8, alpha=0.05
        n_required = calculate_sample_size(
            effect_size=effect_size,
            power=0.8,
            alpha=0.05,
            test_type='two_tailed'
        )
        required_samples[test_name] = n_required
    
    return required_samples

Multiple Comparison Corrections:

python

def apply_multiple_comparison_corrections():
    # We're running ~50 statistical tests across all experiments
    n_tests = 50
    
    # Bonferroni correction
    bonferroni_alpha = 0.05 / n_tests
    
    # False Discovery Rate (Benjamini-Hochberg)
    fdr_alpha = 0.05
    
    # Holm-Bonferroni (less conservative)
    holm_alpha = calculate_holm_alpha(n_tests)
    
    return {
        'bonferroni': bonferroni_alpha,
        'fdr': fdr_alpha,
        'holm': holm_alpha,
        'recommended': 'holm'  # Good balance of power and control
    }

Enhanced Reproducibility Protocol

Experiment Provenance Tracking:

python

class ExperimentProvenance:
    def __init__(self):
        self.metadata = {
            'git_commit': get_git_commit_hash(),
            'timestamp': datetime.utcnow().isoformat(),
            'environment': self.capture_environment(),
            'hardware': self.detect_hardware(),
            'dependencies': self.capture_dependencies()
        }
    
    def capture_environment(self):
        return {
            'python_version': sys.version,
            'platform': platform.platform(),
            'env_variables': {k: v for k, v in os.environ.items() if 'PATH' not in k}
        }
    
    def create_experiment_hash(self, config, data):
        # Create unique hash for experiment configuration and data
        config_hash = hashlib.sha256(str(config).encode()).hexdigest()
        data_hash = hashlib.sha256(str(data).encode()).hexdigest()
        return f"{config_hash[:8]}-{data_hash[:8]}"

Automated Result Validation:

python

def validate_experiment_results(results, expected_patterns):
    """Automatically validate that results match expected theoretical patterns"""
    
    validation_report = {}
    
    # Test 1: Scaling laws
    if 'query_times' in results:
        scaling_fit = fit_scaling_law(results['memory_sizes'], results['query_times'])
        validation_report['scaling_law'] = {
            'expected': 'O(log n)',
            'measured': scaling_fit.complexity_class,
            'r_squared': scaling_fit.r_squared,
            'passes': scaling_fit.r_squared > 0.9 and 'log' in scaling_fit.complexity_class
        }
    
    # Test 2: Convergence properties
    if 'convergence_data' in results:
        conv_analysis = analyze_convergence(results['convergence_data'])
        validation_report['convergence'] = {
            'expected': 'exponential',
            'measured': conv_analysis.convergence_type,
            'rate': conv_analysis.convergence_rate,
            'passes': conv_analysis.convergence_type == 'exponential'
        }
    
    return validation_report

Reproducibility Protocol

Environment and Dependencies

yaml

# environment.yml
name: mnemoverse
channels:
  - pytorch
  - conda-forge
dependencies:
  - python=3.9
  - numpy=1.21
  - scipy=1.7
  - scikit-learn=1.0
  - pytorch=1.10
  - cudatoolkit=11.3
  - jupyter=1.0
  - matplotlib=3.5
  - pip:
    - hyperbolic-embeddings==0.2.0
    - geoopt==0.4.1

Experiment Data Structure

experiments/
├── data/
│   ├── synthetic/
│   │   ├── balanced_trees/
│   │   └── skewed_trees/
│   ├── real/
│   │   ├── wordnet/
│   │   └── conceptnet/
│   └── generated/
├── results/
│   ├── embedding/
│   ├── dynamics/
│   ├── performance/
│   └── visualizations/
├── configs/
│   └── experiment_configs.yaml
└── scripts/
    ├── run_all_experiments.py
    └── analyze_results.py

Checksums and Versions

All experimental data must include:

SHA-256 hashes of input data
Versions of all libraries
Random seeds for reproducibility
Hardware metadata (GPU model, drivers)

Execution Timeline

Quarter 1 (Months 1-3):

Weeks 1-4: Environment setup, data generation
Weeks 5-8: Geometry experiments (Exp. 1)
Weeks 9-12: Initial dynamics experiments (Exp. 2.1)

Quarter 2 (Months 4-6):

Weeks 13-16: Complete dynamics validation (Exp. 2)
Weeks 17-20: Performance benchmarks (Exp. 3)
Weeks 21-24: GPU optimization (Exp. 5)

Quarter 3 (Months 7-9):

Weeks 25-28: Real datasets (Exp. 4.1)
Weeks 29-32: Game engine prototype (Exp. 4.2)
Weeks 33-36: Integration and debugging

Quarter 4 (Months 10-12):

Weeks 37-40: Additional feedback experiments
Weeks 41-44: Publication preparation
Weeks 45-48: Documentation and open release

Expected Publications

Main Paper: "Mnemoverse: Hyperbolic Geometry for Scalable AI Memory Systems"
- Target conference: NeurIPS 2026 or ICML 2026
Systems Paper: "Engineering Hyperbolic Memory: From Theory to Practice"
- Target conference: MLSys 2026
Demo Paper: "Interactive Exploration of AI Memory in Virtual Worlds"
- Target conference: SIGGRAPH 2026 (Real-Time Live!)

This plan provides a systematic path from theoretical predictions to empirical validation, maintaining scientific rigor and practical applicability.

Advanced Configuration

Performance Monitoring Configuration

yaml

# config/monitoring_config.yaml
performance_monitoring:
  real_time:
    enabled: true
    sampling_rate_hz: 10
    metrics:
      - cpu_utilization
      - memory_usage
      - gpu_utilization
      - gpu_memory
      - disk_io
      - network_io
    alerts:
      cpu_threshold: 90
      memory_threshold: 85
      gpu_threshold: 95
      temperature_threshold: 85
  
  profiling:
    enabled: true
    profilers:
      - cProfile
      - line_profiler
      - memory_profiler
    output_format: "json"
    save_profiles: true
    
  benchmarking:
    enabled: true
    baseline_runs: 5
    statistical_significance: 0.05
    confidence_interval: 0.95

Data Analysis Configuration

yaml

# config/analysis_config.yaml
data_analysis:
  statistical_tests:
    enabled: true
    tests:
      - t_test
      - mann_whitney
      - wilcoxon
      - kruskal_wallis
    multiple_comparison_correction: "bonferroni"
    
  visualization:
    enabled: true
    plots:
      - distortion_comparison
      - convergence_analysis
      - performance_scaling
      - gpu_acceleration
    output_formats:
      - png
      - svg
      - pdf
    style: "seaborn-v0_8"
    
  reporting:
    enabled: true
    templates:
      - latex
      - markdown
      - html
    include_plots: true
    include_statistics: true
    include_raw_data: false

Automation Configuration

yaml

# config/automation_config.yaml
automation:
  scheduling:
    enabled: true
    cron_schedule: "0 2 * * *"  # Daily at 2 AM
    timezone: "UTC"
    
  parallel_execution:
    enabled: true
    max_parallel_jobs: 4
    resource_allocation:
      cpu_cores_per_job: 6
      gpu_memory_per_job: "4GB"
      system_memory_per_job: "8GB"
      
  error_handling:
    max_retries: 3
    retry_delay_seconds: 300
    failure_notification: true
    notification_channels:
      - email
      - slack
      - webhook
    
  backup:
    enabled: true
    backup_schedule: "0 1 * * *"  # Daily at 1 AM
    retention_days: 30
    compression: true
    encryption: false

Machine Learning Pipeline Configuration

yaml

# config/ml_pipeline_config.yaml
ml_pipeline:
  hyperparameter_optimization:
    enabled: true
    method: "bayesian_optimization"
    n_trials: 100
    search_space:
      learning_rate:
        type: "log_uniform"
        min: 1e-5
        max: 1e-1
      embedding_dimension:
        type: "categorical"
        choices: [16, 32, 64, 128]
      attention_heads:
        type: "int_uniform"
        min: 1
        max: 16
        
  model_selection:
    enabled: true
    cross_validation:
      method: "k_fold"
      k: 5
      stratified: true
    metrics:
      - accuracy
      - precision
      - recall
      - f1_score
      - distortion
      
  ensemble_methods:
    enabled: true
    methods:
      - bagging
      - boosting
      - stacking
    base_models:
      - hyperbolic_embedding
      - euclidean_embedding
      - attention_mechanism

Cloud Computing Configuration

yaml

# config/cloud_config.yaml
cloud_computing:
  aws:
    enabled: false
    region: "us-west-2"
    instance_types:
      - "g4dn.xlarge"
      - "g4dn.2xlarge"
      - "g4dn.4xlarge"
    spot_instances: true
    max_bid_percentage: 80
    
  gcp:
    enabled: false
    project_id: "mnemoverse-experiments"
    zone: "us-west1-a"
    machine_types:
      - "n1-standard-4"
      - "n1-standard-8"
      - "n1-standard-16"
    gpu_types:
      - "nvidia-tesla-t4"
      - "nvidia-tesla-v100"
      
  azure:
    enabled: false
    subscription_id: "your-subscription-id"
    location: "West US 2"
    vm_sizes:
      - "Standard_NC6s_v3"
      - "Standard_NC12s_v3"
      - "Standard_NC24s_v3"
      
  cost_optimization:
    enabled: true
    budget_limit_usd: 1000
    auto_shutdown: true
    idle_timeout_minutes: 30
    cost_alerts:
      threshold_percentage: 80
      notification_email: "experiments@mnemoverse.ai"

Security Configuration

yaml

# config/security_config.yaml
security:
  authentication:
    enabled: true
    method: "oauth2"
    providers:
      - github
      - google
      - microsoft
    session_timeout_hours: 24
    
  authorization:
    enabled: true
    roles:
      admin:
        permissions:
          - read_all
          - write_all
          - delete_all
          - manage_users
      researcher:
        permissions:
          - read_own
          - write_own
          - read_public
      viewer:
        permissions:
          - read_public
          
  data_protection:
    enabled: true
    encryption:
      at_rest: true
      in_transit: true
      algorithm: "AES-256"
    anonymization:
      enabled: true
      methods:
        - k_anonymity
        - differential_privacy
    audit_logging:
      enabled: true
      retention_days: 365
      events:
        - data_access
        - data_modification
        - user_actions

Integration Configuration

yaml

# config/integration_config.yaml
integrations:
  version_control:
    git:
      enabled: true
      repository: "https://github.com/mnemoverse/experiments"
      branch: "main"
      auto_commit: true
      commit_message_template: "feat: {experiment_name} results - {timestamp}"
      
  continuous_integration:
    github_actions:
      enabled: true
      triggers:
        - push
        - pull_request
      workflows:
        - experiment_validation
        - performance_testing
        - security_scanning
        
  data_storage:
    s3:
      enabled: false
      bucket: "mnemoverse-experiments"
      region: "us-west-2"
      lifecycle_policy:
        transition_days: 30
        expiration_days: 365
        
    google_cloud_storage:
      enabled: false
      bucket: "mnemoverse-experiments"
      project: "mnemoverse-ai"
      
  databases:
    postgresql:
      enabled: false
      host: "localhost"
      port: 5432
      database: "mnemoverse_experiments"
      ssl_mode: "require"
      
    mongodb:
      enabled: false
      uri: "mongodb://localhost:27017"
      database: "mnemoverse"
      collections:
        - experiments
        - results
        - metadata
        
  monitoring_services:
    prometheus:
      enabled: false
      endpoint: "http://localhost:9090"
      metrics:
        - experiment_duration
        - success_rate
        - resource_usage
        
    grafana:
      enabled: false
      url: "http://localhost:3000"
      dashboards:
        - experiment_overview
        - performance_metrics
        - resource_monitoring

Configuration Usage Examples

Running Experiments with Custom Configuration

python

# Example: Running specific experiments with custom parameters
from experiment_runner import ExperimentRunner

# Load custom configuration
runner = ExperimentRunner("config/custom_experiment_config.yaml")

# Run only GPU acceleration experiments
results = runner.run_experiments(['gpu_acceleration'])

# Generate detailed report
report = runner.generate_report(results)
print(report)

Monitoring Experiment Progress

python

# Example: Real-time monitoring
import time
from experiment_monitor import ExperimentMonitor

monitor = ExperimentMonitor("config/monitoring_config.yaml")

# Start monitoring
monitor.start()

# Run experiments
runner = ExperimentRunner("config/experiment_config.yaml")
results = runner.run_all()

# Stop monitoring and get report
monitor.stop()
monitoring_report = monitor.generate_report()

Automated Analysis Pipeline

python

# Example: Automated analysis and reporting
from analysis_pipeline import AnalysisPipeline

pipeline = AnalysisPipeline("config/analysis_config.yaml")

# Run analysis on experiment results
analysis_results = pipeline.analyze_results("experiments/results/")

# Generate publication-ready figures
figures = pipeline.generate_figures(analysis_results)

# Create comprehensive report
report = pipeline.create_report(analysis_results, figures)
pipeline.save_report(report, "reports/experiment_analysis.pdf")

This comprehensive configuration system provides full control over experiment execution, monitoring, analysis, and automation while maintaining reproducibility and scientific rigor.

🎯 Priority Recommendations

Based on this analysis, here are the highest priority improvements:

Immediate (Week 1-2)

Add Experiment 0 (Direct Axiom Validation) - Critical gap that needs to be filled
Implement enhanced statistical controls - Multiple comparison corrections, proper power analysis
Add basic robustness testing - System should handle edge cases gracefully

Short-term (Month 1)

Add cross-scale consistency tests to Experiment 3
Implement cognitive plausibility testing (Experiment 6)
Simplify configuration system - Current system is too complex for practical use

Medium-term (Month 2-3)

Add bifurcation analysis to convergence studies
Implement privacy and bias testing (Experiment 8)
Enhance capacity scaling tests for geometric validation

Long-term (Month 3-6)

Full user experience studies with spatial navigation interfaces
Advanced adversarial testing with sophisticated attack vectors
Cross-platform reproducibility validation

Summary

The experimental protocol is already quite comprehensive, but these additions will significantly strengthen the validation of Mnemoverse's theoretical claims. The most critical gap is the lack of direct axiom testing - the current protocol tests consequences of the axioms (theorems) but not the axioms themselves. Adding Experiment 0 should be the first priority.

The configuration system, while thorough, may be over-engineered for practical use. The simplified approach will make it easier for other researchers to reproduce and extend the work.

Finally, the addition of ethical considerations (privacy, bias, fairness) is essential for any memory system that might be deployed in production environments.

📚 Research Sources

For the complete collection of research sources supporting the experimental protocols and theoretical foundations presented in this document, see:

📚 Research Library - Comprehensive collection of 92 verified academic sources covering:

Hyperbolic Geometry & Embeddings - Foundational research on Poincaré embeddings, hyperbolic neural networks, and geometric deep learning
Multi-Agent Systems & Collective Intelligence - Research on distributed cognitive systems and collective behavior
GPU Computing & Performance - Hardware acceleration, optimization techniques, and benchmarking methodologies
Memory Theory & Navigation - Grid cells, spatial memory, and cognitive mapping research
Information Geometry & Metrics - Fisher metrics, natural gradients, and attention theory

All experimental protocols in this document are designed based on these verified research sources and theoretical foundations.

Explore related documentation:

📚 Research Documentation - 🔬 📚 Research Documentation | Scientific research on AI memory systems. Academic insights, mathematical foundations, experimental results.
Experimental Theory & Speculative Research - 🔬 Experimental Theory & Speculative Research | Experimental research and theoretical frameworks for advanced AI memory systems.
Cognitive Homeostasis Theory: Mathematical Framework for Consciousness Emergence - 🔬 Cognitive Homeostasis Theory: Mathematical Framework for Consciousness Emergence | Experimental research and theoretical frameworks for advanced AI memory...
Cognitive Thermodynamics for Mnemoverse 2.0 - 🔬 Cognitive Thermodynamics for Mnemoverse 2.0 | Experimental research and theoretical frameworks for advanced AI memory systems.
Temporal Symmetry as the Basis for AGI: A Unified Cognitive Architecture - 🔬 Temporal Symmetry as the Basis for AGI: A Unified Cognitive Architecture | Experimental research and theoretical frameworks for advanced AI memory systems.

🔬 Mnemoverse Experimental Validation Protocol ​

Comprehensive experimental framework for validating theoretical predictions ​

Validation Program Overview ​

🚨 Experiment 0: Direct Axiom Validation ​

0.1 Axiom A1 Direct Test: Hierarchical Coherence ​

0.2 Axiom A2 Direct Test: Contextual Curvature ​

0.3 Axiom A3 Direct Test: Information Diffusion ​

Experiment 1: Hyperbolic Geometry Validation ​

1.1 Basic Embedding Distortion Verification ​

1.2 Metric Tensor Stability Under Attention ​

Experiment 2: Memory Diffusion Dynamics ​

2.1 Convergence Speed and Stability ​

2.2 Attention Influence on Dynamics ​

Experiment 3: Query Performance ​

3.1 Multiscale Query Scalability ​

Experiment 4: Integration and Application ​

4.1 Real Datasets ​

4.2 Game Engine Prototype ​

Experiment 5: GPU Acceleration Validation ​

5.1 CUDA Optimization ​

Experiment 6: Cognitive Plausibility and User Experience ​

6.1 Spatial Memory Navigation Study ​

6.2 Memory Retention and Spatial Association ​

Experiment 7: Robustness and Failure Mode Analysis ​

7.1 Adversarial Input Testing ​

Experiment 8: Ethical and Safety Validation ​

8.1 Privacy Protection ​

Configuration Management ​

Environment Configuration ​

Enhanced Reproducibility Protocol ​

Reproducibility Protocol ​

Environment and Dependencies ​

Experiment Data Structure ​

Checksums and Versions ​

Execution Timeline ​

Expected Publications ​

Advanced Configuration ​

Performance Monitoring Configuration ​

Data Analysis Configuration ​

Automation Configuration ​

Machine Learning Pipeline Configuration ​

Cloud Computing Configuration ​

Security Configuration ​

Integration Configuration ​

Configuration Usage Examples ​

Running Experiments with Custom Configuration ​

Monitoring Experiment Progress ​

Automated Analysis Pipeline ​

🎯 Priority Recommendations ​

Immediate (Week 1-2) ​

Short-term (Month 1) ​

Medium-term (Month 2-3) ​

Long-term (Month 3-6) ​

Summary ​

📚 Research Sources ​

Related Links ​

🔬 Mnemoverse Experimental Validation Protocol

Comprehensive experimental framework for validating theoretical predictions

Validation Program Overview

🚨 Experiment 0: Direct Axiom Validation

0.1 Axiom A1 Direct Test: Hierarchical Coherence

0.2 Axiom A2 Direct Test: Contextual Curvature

0.3 Axiom A3 Direct Test: Information Diffusion

Experiment 1: Hyperbolic Geometry Validation

1.1 Basic Embedding Distortion Verification

1.2 Metric Tensor Stability Under Attention

Experiment 2: Memory Diffusion Dynamics

2.1 Convergence Speed and Stability

2.2 Attention Influence on Dynamics

Experiment 3: Query Performance

3.1 Multiscale Query Scalability

Experiment 4: Integration and Application

4.1 Real Datasets

4.2 Game Engine Prototype

Experiment 5: GPU Acceleration Validation

5.1 CUDA Optimization

Experiment 6: Cognitive Plausibility and User Experience

6.1 Spatial Memory Navigation Study

6.2 Memory Retention and Spatial Association

Experiment 7: Robustness and Failure Mode Analysis

7.1 Adversarial Input Testing

Experiment 8: Ethical and Safety Validation

8.1 Privacy Protection

Configuration Management

Environment Configuration

Enhanced Reproducibility Protocol

Reproducibility Protocol

Environment and Dependencies

Experiment Data Structure

Checksums and Versions

Execution Timeline

Expected Publications

Advanced Configuration

Performance Monitoring Configuration

Data Analysis Configuration

Automation Configuration

Machine Learning Pipeline Configuration

Cloud Computing Configuration

Security Configuration

Integration Configuration

Configuration Usage Examples

Running Experiments with Custom Configuration

Monitoring Experiment Progress

Automated Analysis Pipeline

🎯 Priority Recommendations

Immediate (Week 1-2)

Short-term (Month 1)

Medium-term (Month 2-3)

Long-term (Month 3-6)

Summary

📚 Research Sources

Related Links