ΔΣ::TORI - Copy TORUS modules locally for Hugging Face deployment

app.py

@@ -11,9 +11,6 @@ import io
 import base64
 
 # Импорт архитектуры TORUS
-import sys
-sys.path.append('./TORUS/toroidal_diffusion_complete_website/toroidal_diffusion_project/src')
-
 from central_singularity import SingularityCore, CognitiveFeedbackLoop
 from coherence_monitor import CoherenceMetrics, SelfReflectionModule
 from toroidal_topology import ToroidalLatentSpace

central_singularity.py

@@ -0,0 +1,517 @@
+"""
+Central Singularity Module
+
+This module implements the central singularity of the toroidal diffusion model,
+which acts as a self-reflective node of cognition - absorbing latent intent,
+transforming internal state, and emitting structured informational jets.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+from typing import Dict, List, Tuple, Optional
+from einops import rearrange, reduce, repeat
+
+
+class SingularityCore(nn.Module):
+    """
+    The core singularity that processes all information flowing through the torus center.
+    
+    This acts as the central cognitive node that:
+    1. Absorbs latent intent from the toroidal surface
+    2. Transforms and integrates internal state
+    3. Emits structured informational jets back to the surface
+    """
+    
+    def __init__(self, 
+                 latent_dim: int, 
+                 singularity_dim: int = 256,
+                 num_jets: int = 8,
+                 absorption_strength: float = 0.1,
+                 emission_strength: float = 0.1):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.singularity_dim = singularity_dim
+        self.num_jets = num_jets
+        self.absorption_strength = absorption_strength
+        self.emission_strength = emission_strength
+        
+        # Absorption network - processes incoming information
+        self.absorption_net = nn.Sequential(
+            nn.Linear(latent_dim, singularity_dim),
+            nn.LayerNorm(singularity_dim),
+            nn.SiLU(),
+            nn.Linear(singularity_dim, singularity_dim),
+            nn.LayerNorm(singularity_dim),
+            nn.SiLU()
+        )
+        
+        # Internal state transformation - the cognitive core
+        num_heads = min(8, singularity_dim // 64) if singularity_dim >= 64 else 1
+        self.cognitive_core = nn.ModuleList([
+            nn.MultiheadAttention(singularity_dim, num_heads=num_heads, batch_first=True),
+            nn.Sequential(
+                nn.Linear(singularity_dim, singularity_dim * 4),
+                nn.SiLU(),
+                nn.Linear(singularity_dim * 4, singularity_dim)
+            )
+        ])
+        
+        # Emission network - generates informational jets
+        self.emission_net = nn.Sequential(
+            nn.Linear(singularity_dim, singularity_dim * 2),
+            nn.SiLU(),
+            nn.Linear(singularity_dim * 2, num_jets * latent_dim),
+            nn.Tanh()
+        )
+        
+        # Learnable singularity state
+        self.singularity_state = nn.Parameter(torch.randn(1, singularity_dim) * 0.1)
+        
+        # Jet direction embeddings (learnable)
+        self.jet_directions = nn.Parameter(torch.randn(num_jets, 2) * 0.1)  # (theta, phi) for each jet
+        
+    def absorb_intent(self, toroidal_features: torch.Tensor) -> torch.Tensor:
+        """
+        Absorb latent intent from the toroidal surface into the singularity.
+        
+        Args:
+            toroidal_features: Features from the toroidal surface [B, C, H, W]
+            
+        Returns:
+            absorbed_intent: Absorbed and processed intent [B, singularity_dim]
+        """
+        batch_size, channels, height, width = toroidal_features.shape
+        
+        # Global average pooling to extract global intent
+        global_intent = F.adaptive_avg_pool2d(toroidal_features, 1).flatten(1)
+        
+        # Weighted absorption based on distance from center
+        center_h, center_w = height // 2, width // 2
+        y_coords, x_coords = torch.meshgrid(
+            torch.arange(height, device=toroidal_features.device),
+            torch.arange(width, device=toroidal_features.device),
+            indexing='ij'
+        )
+        
+        # Distance from center (inverted for absorption weight)
+        dist_from_center = torch.sqrt((y_coords - center_h)**2 + (x_coords - center_w)**2)
+        absorption_weight = 1.0 / (1.0 + dist_from_center)
+        absorption_weight = absorption_weight / absorption_weight.sum()
+        
+        # Weighted spatial pooling
+        weighted_features = toroidal_features * absorption_weight.unsqueeze(0).unsqueeze(0)
+        spatial_intent = weighted_features.sum(dim=[2, 3])
+        
+        # Combine global and spatial intent
+        combined_intent = global_intent + spatial_intent
+        
+        # Process through absorption network
+        absorbed_intent = self.absorption_net(combined_intent)
+        
+        return absorbed_intent
+    
+    def transform_state(self, absorbed_intent: torch.Tensor) -> torch.Tensor:
+        """
+        Transform the internal singularity state using absorbed intent.
+        
+        Args:
+            absorbed_intent: Absorbed intent from toroidal surface
+            
+        Returns:
+            transformed_state: New singularity state
+        """
+        batch_size = absorbed_intent.shape[0]
+        
+        # Expand singularity state for batch
+        current_state = self.singularity_state.expand(batch_size, -1)
+        
+        # Combine current state with absorbed intent
+        combined = torch.stack([current_state, absorbed_intent], dim=1)  # [B, 2, D]
+        
+        # Self-attention for cognitive processing
+        attn_layer, ffn_layer = self.cognitive_core
+        
+        # Self-attention
+        attended, _ = attn_layer(combined, combined, combined)
+        attended = attended + combined  # Residual connection
+        
+        # Feed-forward processing
+        transformed = ffn_layer(attended)
+        transformed = transformed + attended  # Residual connection
+        
+        # Extract the transformed singularity state
+        transformed_state = transformed[:, 0]  # Take the first token (singularity state)
+        
+        return transformed_state
+    
+    def emit_jets(self, transformed_state: torch.Tensor, target_shape: Tuple[int, int]) -> torch.Tensor:
+        """
+        Emit structured informational jets from the singularity to the toroidal surface.
+        
+        Args:
+            transformed_state: Transformed singularity state
+            target_shape: Target spatial shape (H, W) for the jets
+            
+        Returns:
+            emitted_jets: Informational jets projected onto toroidal surface [B, C, H, W]
+        """
+        batch_size = transformed_state.shape[0]
+        height, width = target_shape
+        
+        # Generate jet information
+        jet_info = self.emission_net(transformed_state)  # [B, num_jets * latent_dim]
+        jet_info = jet_info.view(batch_size, self.num_jets, self.latent_dim)
+        
+        # Create spatial grid
+        y_coords, x_coords = torch.meshgrid(
+            torch.linspace(-1, 1, height, device=transformed_state.device),
+            torch.linspace(-1, 1, width, device=transformed_state.device),
+            indexing='ij'
+        )
+        
+        # Convert to polar coordinates
+        theta = torch.atan2(y_coords, x_coords)
+        radius = torch.sqrt(x_coords**2 + y_coords**2)
+        
+        # Initialize emission field
+        emission_field = torch.zeros(batch_size, self.latent_dim, height, width, 
+                                   device=transformed_state.device)
+        
+        # Emit each jet
+        for jet_idx in range(self.num_jets):
+            # Jet direction
+            jet_theta = self.jet_directions[jet_idx, 0]
+            jet_phi = self.jet_directions[jet_idx, 1]
+            
+            # Compute jet influence based on angular distance
+            angular_dist = torch.abs(theta - jet_theta)
+            angular_dist = torch.min(angular_dist, 2 * math.pi - angular_dist)  # Wrap around
+            
+            # Jet strength decreases with angular distance and radius
+            jet_strength = torch.exp(-angular_dist**2 / 0.5) * torch.exp(-radius**2 / 2.0)
+            
+            # Apply jet information
+            jet_contribution = jet_info[:, jet_idx].unsqueeze(-1).unsqueeze(-1) * jet_strength.unsqueeze(0)
+            emission_field += jet_contribution
+        
+        return emission_field
+    
+    def forward(self, toroidal_features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Complete singularity processing cycle.
+        
+        Args:
+            toroidal_features: Input features from toroidal surface
+            
+        Returns:
+            result: Dictionary containing all singularity outputs
+        """
+        # Absorption phase
+        absorbed_intent = self.absorb_intent(toroidal_features)
+        
+        # Transformation phase
+        transformed_state = self.transform_state(absorbed_intent)
+        
+        # Emission phase
+        target_shape = toroidal_features.shape[2:]
+        emitted_jets = self.emit_jets(transformed_state, target_shape)
+        
+        return {
+            'absorbed_intent': absorbed_intent,
+            'transformed_state': transformed_state,
+            'emitted_jets': emitted_jets,
+            'singularity_influence': emitted_jets * self.emission_strength
+        }
+
+
+class SingularityToroidalCoupling(nn.Module):
+    """
+    Manages the coupling between the central singularity and the toroidal surface.
+    
+    This module handles the bidirectional information flow and ensures
+    proper integration of singularity effects with toroidal dynamics.
+    """
+    
+    def __init__(self, 
+                 latent_dim: int,
+                 singularity_dim: int = 256,
+                 coupling_strength: float = 0.1):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.singularity_dim = singularity_dim
+        self.coupling_strength = coupling_strength
+        
+        # Singularity core
+        self.singularity = SingularityCore(latent_dim, singularity_dim)
+        
+        # Coupling networks
+        num_groups = min(8, latent_dim) if latent_dim >= 8 else 1
+        self.surface_to_singularity = nn.Sequential(
+            nn.Conv2d(latent_dim, latent_dim, 3, padding=1),
+            nn.GroupNorm(num_groups, latent_dim),
+            nn.SiLU(),
+            nn.Conv2d(latent_dim, latent_dim, 1)
+        )
+        
+        self.singularity_to_surface = nn.Sequential(
+            nn.Conv2d(latent_dim, latent_dim, 3, padding=1),
+            nn.GroupNorm(num_groups, latent_dim),
+            nn.SiLU(),
+            nn.Conv2d(latent_dim, latent_dim, 1)
+        )
+        
+        # Adaptive coupling strength
+        self.coupling_modulator = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(latent_dim, max(1, latent_dim // 4), 1),
+            nn.SiLU(),
+            nn.Conv2d(max(1, latent_dim // 4), 1, 1),
+            nn.Sigmoid()
+        )
+    
+    def compute_coupling_strength(self, features: torch.Tensor) -> torch.Tensor:
+        """
+        Compute adaptive coupling strength based on feature characteristics.
+        
+        Args:
+            features: Input features
+            
+        Returns:
+            coupling_strength: Adaptive coupling strength
+        """
+        return self.coupling_modulator(features)
+    
+    def forward(self, toroidal_features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Process toroidal features through singularity coupling.
+        
+        Args:
+            toroidal_features: Features on the toroidal surface
+            
+        Returns:
+            result: Dictionary containing coupled features and singularity outputs
+        """
+        # Prepare features for singularity processing
+        prepared_features = self.surface_to_singularity(toroidal_features)
+        
+        # Process through singularity
+        singularity_result = self.singularity(prepared_features)
+        
+        # Process singularity output for surface integration
+        processed_jets = self.singularity_to_surface(singularity_result['emitted_jets'])
+        
+        # Compute adaptive coupling strength
+        coupling_strength = self.compute_coupling_strength(toroidal_features)
+        
+        # Apply coupling
+        coupled_influence = processed_jets * coupling_strength * self.coupling_strength
+        
+        # Integrate with original features
+        coupled_features = toroidal_features + coupled_influence
+        
+        return {
+            'coupled_features': coupled_features,
+            'original_features': toroidal_features,
+            'singularity_influence': coupled_influence,
+            'coupling_strength': coupling_strength,
+            **singularity_result
+        }
+
+
+class CognitiveFeedbackLoop(nn.Module):
+    """
+    Implements the cognitive feedback loop between observation, integration, and action.
+    
+    This creates a continuous cycle of self-reflection and adaptation.
+    """
+    
+    def __init__(self, latent_dim: int, memory_size: int = 10):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.memory_size = memory_size
+        
+        # Observation network
+        num_groups = min(8, latent_dim) if latent_dim >= 8 else 1
+        self.observer = nn.Sequential(
+            nn.Conv2d(latent_dim, latent_dim, 3, padding=1),
+            nn.GroupNorm(num_groups, latent_dim),
+            nn.SiLU(),
+            nn.Conv2d(latent_dim, latent_dim // 2, 1),
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.Linear(latent_dim // 2, latent_dim // 4)
+        )
+        
+        # Integration network (memory + current observation)
+        self.integrator = nn.Sequential(
+            nn.Linear(latent_dim // 4 * (memory_size + 1), latent_dim // 2),
+            nn.SiLU(),
+            nn.Linear(latent_dim // 2, latent_dim // 4)
+        )
+        
+        # Action network
+        self.actor = nn.Sequential(
+            nn.Linear(latent_dim // 4, latent_dim),
+            nn.SiLU(),
+            nn.Linear(latent_dim, latent_dim * 4),
+            nn.SiLU(),
+            nn.Linear(latent_dim * 4, latent_dim)
+        )
+        
+        # Memory buffer
+        self.register_buffer('memory', torch.zeros(memory_size, latent_dim // 4))
+        self.register_buffer('memory_ptr', torch.zeros(1, dtype=torch.long))
+    
+    def observe(self, features: torch.Tensor) -> torch.Tensor:
+        """
+        Observe current state and extract key information.
+        
+        Args:
+            features: Current features
+            
+        Returns:
+            observation: Compressed observation
+        """
+        return self.observer(features)
+    
+    def update_memory(self, observation: torch.Tensor):
+        """
+        Update memory with new observation.
+        
+        Args:
+            observation: New observation to store
+        """
+        batch_size = observation.shape[0]
+        
+        # Store observation in memory (simple circular buffer)
+        ptr = self.memory_ptr.item()
+        self.memory[ptr] = observation[0]  # Store first batch item
+        self.memory_ptr[0] = (ptr + 1) % self.memory_size
+    
+    def integrate(self, current_observation: torch.Tensor) -> torch.Tensor:
+        """
+        Integrate current observation with memory.
+        
+        Args:
+            current_observation: Current observation
+            
+        Returns:
+            integrated_state: Integrated cognitive state
+        """
+        batch_size = current_observation.shape[0]
+        
+        # Expand memory for batch
+        memory_expanded = self.memory.unsqueeze(0).expand(batch_size, -1, -1)
+        memory_flat = memory_expanded.flatten(1)
+        
+        # Combine with current observation
+        combined = torch.cat([current_observation, memory_flat], dim=1)
+        
+        # Integrate
+        integrated_state = self.integrator(combined)
+        
+        return integrated_state
+    
+    def act(self, integrated_state: torch.Tensor, original_shape: Tuple[int, int]) -> torch.Tensor:
+        """
+        Generate action based on integrated state.
+        
+        Args:
+            integrated_state: Integrated cognitive state
+            original_shape: Original spatial shape
+            
+        Returns:
+            action: Action to apply to features
+        """
+        # Generate action vector
+        action_vector = self.actor(integrated_state)
+        
+        # Reshape to spatial dimensions
+        height, width = original_shape
+        action = action_vector.unsqueeze(-1).unsqueeze(-1)
+        action = action.expand(-1, -1, height, width)
+        
+        return action
+    
+    def forward(self, features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Complete cognitive feedback loop.
+        
+        Args:
+            features: Input features
+            
+        Returns:
+            result: Dictionary containing cognitive processing results
+        """
+        # Observe
+        observation = self.observe(features)
+        
+        # Integrate with memory
+        integrated_state = self.integrate(observation)
+        
+        # Generate action
+        action = self.act(integrated_state, features.shape[2:])
+        
+        # Apply action
+        modified_features = features + action * 0.1  # Small action strength
+        
+        # Update memory
+        self.update_memory(observation)
+        
+        return {
+            'modified_features': modified_features,
+            'observation': observation,
+            'integrated_state': integrated_state,
+            'action': action,
+            'original_features': features
+        }
+
+
+def test_central_singularity():
+    """Test function for central singularity components."""
+    print("Testing Central Singularity Components...")
+    
+    # Test parameters
+    batch_size, latent_dim, height, width = 2, 64, 32, 32
+    test_features = torch.randn(batch_size, latent_dim, height, width)
+    
+    # Test SingularityCore
+    print("Testing SingularityCore...")
+    singularity_core = SingularityCore(latent_dim, singularity_dim=128, num_jets=8)
+    core_result = singularity_core(test_features)
+    
+    print(f"Absorbed intent shape: {core_result['absorbed_intent'].shape}")
+    print(f"Transformed state shape: {core_result['transformed_state'].shape}")
+    print(f"Emitted jets shape: {core_result['emitted_jets'].shape}")
+    print(f"Singularity influence shape: {core_result['singularity_influence'].shape}")
+    
+    # Test SingularityToroidalCoupling
+    print("\nTesting SingularityToroidalCoupling...")
+    coupling = SingularityToroidalCoupling(latent_dim, singularity_dim=128)
+    coupling_result = coupling(test_features)
+    
+    print(f"Coupled features shape: {coupling_result['coupled_features'].shape}")
+    print(f"Coupling strength shape: {coupling_result['coupling_strength'].shape}")
+    print(f"Coupling strength mean: {coupling_result['coupling_strength'].mean().item():.4f}")
+    
+    # Test CognitiveFeedbackLoop
+    print("\nTesting CognitiveFeedbackLoop...")
+    feedback_loop = CognitiveFeedbackLoop(latent_dim, memory_size=5)
+    
+    # Run multiple iterations to test memory
+    for i in range(3):
+        feedback_result = feedback_loop(test_features)
+        print(f"Iteration {i+1}:")
+        print(f"  Modified features shape: {feedback_result['modified_features'].shape}")
+        print(f"  Observation shape: {feedback_result['observation'].shape}")
+        print(f"  Action mean: {feedback_result['action'].mean().item():.4f}")
+    
+    print("\nAll central singularity tests passed!")
+
+
+if __name__ == "__main__":
+    test_central_singularity()
+

coherence_monitor.py

@@ -0,0 +1,409 @@
+"""
+Coherence Monitoring and Self-Reflection Module
+
+This module implements the coherence assessment and self-reflection mechanisms
+that are central to the toroidal diffusion model architecture.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from typing import Dict, List, Tuple, Optional
+from collections import deque
+
+
+class CoherenceMetrics:
+    """
+    Computes various coherence metrics for assessing generation quality.
+    """
+    
+    @staticmethod
+    def semantic_coherence(features: torch.Tensor, window_size: int = 3) -> torch.Tensor:
+        """
+        Compute semantic coherence based on local feature consistency.
+        
+        Args:
+            features: Feature tensor of shape (batch, channels, height, width)
+            window_size: Size of the local window for coherence computation
+            
+        Returns:
+            coherence: Semantic coherence score
+        """
+        batch_size, channels, height, width = features.shape
+        
+        # Compute local variance within windows
+        kernel = torch.ones(1, 1, window_size, window_size, device=features.device) / (window_size ** 2)
+        
+        # Mean within windows
+        local_mean = F.conv2d(features, kernel.repeat(channels, 1, 1, 1), 
+                             groups=channels, padding=window_size//2)
+        
+        # Variance within windows
+        local_var = F.conv2d((features - local_mean) ** 2, kernel.repeat(channels, 1, 1, 1),
+                            groups=channels, padding=window_size//2)
+        
+        # Coherence is inverse of variance (lower variance = higher coherence)
+        coherence = 1.0 / (1.0 + local_var.mean(dim=1, keepdim=True))
+        
+        return coherence
+    
+    @staticmethod
+    def structural_coherence(features: torch.Tensor) -> torch.Tensor:
+        """
+        Compute structural coherence based on gradient consistency.
+        
+        Args:
+            features: Feature tensor
+            
+        Returns:
+            coherence: Structural coherence score
+        """
+        # Compute gradients
+        grad_x = torch.diff(features, dim=3, prepend=features[:, :, :, -1:])
+        grad_y = torch.diff(features, dim=2, prepend=features[:, :, -1:, :])
+        
+        # Gradient magnitude
+        grad_mag = torch.sqrt(grad_x ** 2 + grad_y ** 2)
+        
+        # Coherence based on gradient smoothness
+        grad_smoothness = 1.0 / (1.0 + torch.std(grad_mag, dim=1, keepdim=True))
+        
+        return grad_smoothness
+    
+    @staticmethod
+    def temporal_coherence(features_sequence: List[torch.Tensor]) -> torch.Tensor:
+        """
+        Compute temporal coherence across a sequence of features.
+        
+        Args:
+            features_sequence: List of feature tensors from different timesteps
+            
+        Returns:
+            coherence: Temporal coherence score
+        """
+        if len(features_sequence) < 2:
+            return torch.ones_like(features_sequence[0][:, :1])
+        
+        # Compute frame-to-frame differences
+        temporal_diffs = []
+        for i in range(1, len(features_sequence)):
+            diff = torch.abs(features_sequence[i] - features_sequence[i-1])
+            temporal_diffs.append(diff.mean(dim=1, keepdim=True))
+        
+        # Average temporal difference
+        avg_temporal_diff = torch.stack(temporal_diffs).mean(dim=0)
+        
+        # Coherence is inverse of temporal variation
+        temporal_coherence = 1.0 / (1.0 + avg_temporal_diff)
+        
+        return temporal_coherence
+
+
+class SelfReflectionModule(nn.Module):
+    """
+    Implements self-reflection mechanisms for the toroidal diffusion model.
+    
+    This module analyzes the current generation state and provides feedback
+    for improving coherence and quality.
+    """
+    
+    def __init__(self, feature_dim: int, reflection_depth: int = 3):
+        super().__init__()
+        self.feature_dim = feature_dim
+        self.reflection_depth = reflection_depth
+        
+        # Reflection network layers
+        num_groups = min(8, feature_dim) if feature_dim >= 8 else 1
+        self.reflection_layers = nn.ModuleList([
+            nn.Sequential(
+                nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
+                nn.GroupNorm(num_groups, feature_dim),
+                nn.SiLU(),
+                nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
+                nn.GroupNorm(num_groups, feature_dim),
+                nn.SiLU()
+            ) for _ in range(reflection_depth)
+        ])
+        
+        # Coherence assessment head
+        self.coherence_head = nn.Sequential(
+            nn.Conv2d(feature_dim, feature_dim // 2, 1),
+            nn.SiLU(),
+            nn.Conv2d(feature_dim // 2, 1, 1),
+            nn.Sigmoid()
+        )
+        
+        # Correction suggestion head
+        self.correction_head = nn.Sequential(
+            nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
+            nn.GroupNorm(num_groups, feature_dim),
+            nn.SiLU(),
+            nn.Conv2d(feature_dim, feature_dim, 3, padding=1)
+        )
+        
+    def analyze_coherence(self, features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Analyze the coherence of current features.
+        
+        Args:
+            features: Input feature tensor
+            
+        Returns:
+            analysis: Dictionary containing coherence metrics
+        """
+        semantic_coh = CoherenceMetrics.semantic_coherence(features)
+        structural_coh = CoherenceMetrics.structural_coherence(features)
+        
+        # Overall coherence score
+        overall_coherence = self.coherence_head(features)
+        
+        return {
+            'semantic_coherence': semantic_coh,
+            'structural_coherence': structural_coh,
+            'overall_coherence': overall_coherence,
+            'mean_coherence': (semantic_coh + structural_coh + overall_coherence) / 3
+        }
+    
+    def generate_corrections(self, features: torch.Tensor, coherence_analysis: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """
+        Generate correction suggestions based on coherence analysis.
+        
+        Args:
+            features: Input feature tensor
+            coherence_analysis: Coherence analysis results
+            
+        Returns:
+            corrections: Suggested corrections to improve coherence
+        """
+        # Weight corrections by coherence deficiency
+        coherence_weight = 1.0 - coherence_analysis['mean_coherence']
+        
+        # Generate corrections
+        corrections = self.correction_head(features)
+        
+        # Apply coherence-weighted corrections
+        weighted_corrections = corrections * coherence_weight
+        
+        return weighted_corrections
+    
+    def reflect(self, features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Perform self-reflection on the current features.
+        
+        Args:
+            features: Input feature tensor
+            
+        Returns:
+            reflection_result: Dictionary containing analysis and corrections
+        """
+        # Multi-layer reflection
+        reflected_features = features
+        for layer in self.reflection_layers:
+            reflected_features = layer(reflected_features) + reflected_features  # Residual connection
+        
+        # Analyze coherence
+        coherence_analysis = self.analyze_coherence(reflected_features)
+        
+        # Generate corrections
+        corrections = self.generate_corrections(reflected_features, coherence_analysis)
+        
+        return {
+            'reflected_features': reflected_features,
+            'coherence_analysis': coherence_analysis,
+            'corrections': corrections,
+            'original_features': features
+        }
+    
+    def forward(self, features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass performing self-reflection.
+        
+        Args:
+            features: Input feature tensor
+            
+        Returns:
+            reflection_result: Self-reflection results
+        """
+        return self.reflect(features)
+
+
+class MultiPassRefinement(nn.Module):
+    """
+    Implements multi-pass refinement mechanism for iterative improvement.
+    
+    This module performs multiple passes of generation and refinement,
+    using self-reflection to guide the improvement process.
+    """
+    
+    def __init__(self, feature_dim: int, max_passes: int = 3, coherence_threshold: float = 0.8):
+        super().__init__()
+        self.feature_dim = feature_dim
+        self.max_passes = max_passes
+        self.coherence_threshold = coherence_threshold
+        
+        # Self-reflection module
+        self.reflection_module = SelfReflectionModule(feature_dim)
+        
+        # Refinement network
+        num_groups = min(8, feature_dim) if feature_dim >= 8 else 1
+        self.refinement_net = nn.Sequential(
+            nn.Conv2d(feature_dim * 2, feature_dim, 3, padding=1),  # features + corrections
+            nn.GroupNorm(num_groups, feature_dim),
+            nn.SiLU(),
+            nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
+            nn.GroupNorm(num_groups, feature_dim),
+            nn.SiLU(),
+            nn.Conv2d(feature_dim, feature_dim, 3, padding=1)
+        )
+        
+        # History tracking
+        self.coherence_history = deque(maxlen=max_passes)
+        
+    def should_continue_refinement(self, coherence_score: float, pass_num: int) -> bool:
+        """
+        Determine if refinement should continue.
+        
+        Args:
+            coherence_score: Current coherence score
+            pass_num: Current pass number
+            
+        Returns:
+            should_continue: Whether to continue refinement
+        """
+        # Stop if coherence threshold is reached
+        if coherence_score >= self.coherence_threshold:
+            return False
+        
+        # Stop if maximum passes reached
+        if pass_num >= self.max_passes:
+            return False
+        
+        # Stop if coherence is not improving
+        if len(self.coherence_history) >= 2:
+            recent_improvement = self.coherence_history[-1] - self.coherence_history[-2]
+            if recent_improvement < 0.01:  # Minimal improvement threshold
+                return False
+        
+        return True
+    
+    def refine_features(self, features: torch.Tensor, corrections: torch.Tensor) -> torch.Tensor:
+        """
+        Apply refinement to features using corrections.
+        
+        Args:
+            features: Input features
+            corrections: Correction suggestions
+            
+        Returns:
+            refined_features: Refined feature tensor
+        """
+        # Concatenate features and corrections
+        combined = torch.cat([features, corrections], dim=1)
+        
+        # Apply refinement network
+        refinement = self.refinement_net(combined)
+        
+        # Apply refinement with residual connection
+        refined_features = features + refinement
+        
+        return refined_features
+    
+    def forward(self, initial_features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Perform multi-pass refinement.
+        
+        Args:
+            initial_features: Initial feature tensor
+            
+        Returns:
+            refinement_result: Dictionary containing refinement results
+        """
+        current_features = initial_features
+        pass_num = 0
+        refinement_history = []
+        
+        # Clear history for new refinement session
+        self.coherence_history.clear()
+        
+        while True:
+            # Perform self-reflection
+            reflection_result = self.reflection_module(current_features)
+            
+            # Extract coherence score
+            coherence_score = reflection_result['coherence_analysis']['mean_coherence'].mean().item()
+            self.coherence_history.append(coherence_score)
+            
+            # Store history
+            refinement_history.append({
+                'pass': pass_num,
+                'features': current_features.clone(),
+                'coherence_score': coherence_score,
+                'reflection_result': reflection_result
+            })
+            
+            # Check if refinement should continue
+            if not self.should_continue_refinement(coherence_score, pass_num):
+                break
+            
+            # Apply refinement
+            corrections = reflection_result['corrections']
+            current_features = self.refine_features(current_features, corrections)
+            
+            pass_num += 1
+        
+        return {
+            'final_features': current_features,
+            'initial_features': initial_features,
+            'refinement_history': refinement_history,
+            'total_passes': pass_num + 1,
+            'final_coherence': coherence_score
+        }
+
+
+def test_coherence_monitoring():
+    """Test function for coherence monitoring components."""
+    print("Testing Coherence Monitoring and Self-Reflection...")
+    
+    # Create test features
+    batch_size, channels, height, width = 2, 64, 32, 32
+    test_features = torch.randn(batch_size, channels, height, width)
+    
+    # Test coherence metrics
+    semantic_coh = CoherenceMetrics.semantic_coherence(test_features)
+    structural_coh = CoherenceMetrics.structural_coherence(test_features)
+    
+    print(f"Semantic coherence shape: {semantic_coh.shape}")
+    print(f"Structural coherence shape: {structural_coh.shape}")
+    print(f"Semantic coherence mean: {semantic_coh.mean().item():.4f}")
+    print(f"Structural coherence mean: {structural_coh.mean().item():.4f}")
+    
+    # Test temporal coherence
+    feature_sequence = [torch.randn(batch_size, channels, height, width) for _ in range(5)]
+    temporal_coh = CoherenceMetrics.temporal_coherence(feature_sequence)
+    print(f"Temporal coherence shape: {temporal_coh.shape}")
+    print(f"Temporal coherence mean: {temporal_coh.mean().item():.4f}")
+    
+    # Test self-reflection module
+    reflection_module = SelfReflectionModule(channels)
+    reflection_result = reflection_module(test_features)
+    
+    print(f"Reflected features shape: {reflection_result['reflected_features'].shape}")
+    print(f"Corrections shape: {reflection_result['corrections'].shape}")
+    print(f"Overall coherence mean: {reflection_result['coherence_analysis']['overall_coherence'].mean().item():.4f}")
+    
+    # Test multi-pass refinement
+    refinement_module = MultiPassRefinement(channels, max_passes=3, coherence_threshold=0.9)
+    refinement_result = refinement_module(test_features)
+    
+    print(f"Final features shape: {refinement_result['final_features'].shape}")
+    print(f"Total passes: {refinement_result['total_passes']}")
+    print(f"Final coherence: {refinement_result['final_coherence']:.4f}")
+    print(f"Refinement history length: {len(refinement_result['refinement_history'])}")
+    
+    print("All coherence monitoring tests passed!")
+
+
+if __name__ == "__main__":
+    test_coherence_monitoring()
+

toroidal_topology.py

@@ -0,0 +1,339 @@
+"""
+Toroidal Topology Module for Diffusion Models
+
+This module implements toroidal topology functions for wrapping diffusion models
+in a toroidal latent space, enabling cyclic continuity and self-reflection.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+from typing import Tuple, Optional, Union
+
+
+class ToroidalCoordinates:
+    """
+    Handles coordinate transformations between Cartesian and toroidal spaces.
+    
+    The torus is parameterized by:
+    - Major radius R (distance from center to tube center)
+    - Minor radius r (tube radius)
+    - Angular coordinates (θ, φ) where θ ∈ [0, 2π], φ ∈ [0, 2π]
+    """
+    
+    def __init__(self, major_radius: float = 1.0, minor_radius: float = 0.3):
+        self.R = major_radius  # Major radius
+        self.r = minor_radius  # Minor radius
+        
+    def cartesian_to_toroidal(self, x: torch.Tensor, y: torch.Tensor, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Convert Cartesian coordinates to toroidal coordinates (θ, φ).
+        
+        Args:
+            x, y, z: Cartesian coordinates
+            
+        Returns:
+            theta, phi: Toroidal angular coordinates
+        """
+        # Distance from z-axis
+        rho = torch.sqrt(x**2 + y**2)
+        
+        # Major angle (around the main axis)
+        theta = torch.atan2(y, x)
+        
+        # Minor angle (around the tube)
+        # Distance from the major circle
+        d_major = rho - self.R
+        phi = torch.atan2(z, d_major)
+        
+        # Normalize to [0, 2π]
+        theta = (theta + 2 * math.pi) % (2 * math.pi)
+        phi = (phi + 2 * math.pi) % (2 * math.pi)
+        
+        return theta, phi
+    
+    def toroidal_to_cartesian(self, theta: torch.Tensor, phi: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Convert toroidal coordinates to Cartesian coordinates.
+        
+        Args:
+            theta, phi: Toroidal angular coordinates
+            
+        Returns:
+            x, y, z: Cartesian coordinates
+        """
+        x = (self.R + self.r * torch.cos(phi)) * torch.cos(theta)
+        y = (self.R + self.r * torch.cos(phi)) * torch.sin(theta)
+        z = self.r * torch.sin(phi)
+        
+        return x, y, z
+
+
+class ToroidalLatentSpace(nn.Module):
+    """
+    Implements toroidal latent space operations for diffusion models.
+    
+    This class wraps standard latent space operations to work on a torus,
+    providing cyclic continuity and enabling self-reflection mechanisms.
+    """
+    
+    def __init__(self, latent_dim: int, major_radius: float = 1.0, minor_radius: float = 0.3):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.coords = ToroidalCoordinates(major_radius, minor_radius)
+        
+        # Learnable parameters for toroidal embedding
+        self.embedding_scale = nn.Parameter(torch.ones(latent_dim))
+        self.embedding_offset = nn.Parameter(torch.zeros(latent_dim))
+        
+    def wrap_to_torus(self, latent: torch.Tensor) -> torch.Tensor:
+        """
+        Wrap latent vectors to toroidal space using periodic boundary conditions.
+        
+        Args:
+            latent: Input latent tensor of shape (batch, channels, height, width)
+            
+        Returns:
+            wrapped_latent: Latent tensor wrapped to toroidal space
+        """
+        # Apply learnable scaling and offset
+        scaled_latent = latent * self.embedding_scale.view(1, -1, 1, 1) + self.embedding_offset.view(1, -1, 1, 1)
+        
+        # Wrap to [0, 2π] using modular arithmetic
+        wrapped = torch.fmod(scaled_latent + 2 * math.pi, 2 * math.pi)
+        
+        return wrapped
+    
+    def toroidal_distance(self, latent1: torch.Tensor, latent2: torch.Tensor) -> torch.Tensor:
+        """
+        Compute distance between points on the torus.
+        
+        Args:
+            latent1, latent2: Latent tensors on the torus
+            
+        Returns:
+            distance: Toroidal distance tensor
+        """
+        # Wrap both latents to torus
+        wrapped1 = self.wrap_to_torus(latent1)
+        wrapped2 = self.wrap_to_torus(latent2)
+        
+        # Compute angular differences
+        diff = wrapped1 - wrapped2
+        
+        # Handle periodic boundary: choose shorter path around the circle
+        diff = torch.where(diff > math.pi, diff - 2 * math.pi, diff)
+        diff = torch.where(diff < -math.pi, diff + 2 * math.pi, diff)
+        
+        # Compute Euclidean distance on the torus surface
+        distance = torch.sqrt(torch.sum(diff**2, dim=1, keepdim=True))
+        
+        return distance
+    
+    def toroidal_interpolation(self, latent1: torch.Tensor, latent2: torch.Tensor, t: float) -> torch.Tensor:
+        """
+        Interpolate between two points on the torus along the shorter geodesic.
+        
+        Args:
+            latent1, latent2: Latent tensors on the torus
+            t: Interpolation parameter [0, 1]
+            
+        Returns:
+            interpolated: Interpolated latent tensor
+        """
+        wrapped1 = self.wrap_to_torus(latent1)
+        wrapped2 = self.wrap_to_torus(latent2)
+        
+        # Compute angular differences (shorter path)
+        diff = wrapped2 - wrapped1
+        diff = torch.where(diff > math.pi, diff - 2 * math.pi, diff)
+        diff = torch.where(diff < -math.pi, diff + 2 * math.pi, diff)
+        
+        # Linear interpolation along the shorter path
+        interpolated = wrapped1 + t * diff
+        
+        # Ensure result is wrapped to [0, 2π]
+        interpolated = torch.fmod(interpolated + 2 * math.pi, 2 * math.pi)
+        
+        return interpolated
+    
+    def compute_curvature(self, latent: torch.Tensor) -> torch.Tensor:
+        """
+        Compute local curvature of the latent space at given points.
+        
+        This is used for coherence assessment and self-reflection.
+        
+        Args:
+            latent: Input latent tensor
+            
+        Returns:
+            curvature: Local curvature tensor
+        """
+        wrapped = self.wrap_to_torus(latent)
+        
+        # Compute second derivatives (discrete approximation)
+        # This is a simplified curvature estimation
+        batch_size, channels, height, width = wrapped.shape
+        
+        # Compute gradients
+        grad_x = torch.diff(wrapped, dim=3, prepend=wrapped[:, :, :, -1:])
+        grad_y = torch.diff(wrapped, dim=2, prepend=wrapped[:, :, -1:, :])
+        
+        # Compute second derivatives
+        grad_xx = torch.diff(grad_x, dim=3, prepend=grad_x[:, :, :, -1:])
+        grad_yy = torch.diff(grad_y, dim=2, prepend=grad_y[:, :, -1:, :])
+        
+        # Gaussian curvature approximation
+        curvature = torch.abs(grad_xx + grad_yy)
+        
+        return curvature
+    
+    def forward(self, latent: torch.Tensor) -> dict:
+        """
+        Forward pass that computes toroidal properties.
+        
+        Args:
+            latent: Input latent tensor
+            
+        Returns:
+            dict: Dictionary containing wrapped latent and computed properties
+        """
+        wrapped_latent = self.wrap_to_torus(latent)
+        curvature = self.compute_curvature(latent)
+        
+        return {
+            'wrapped_latent': wrapped_latent,
+            'curvature': curvature,
+            'original_latent': latent
+        }
+
+
+class ToroidalFlow(nn.Module):
+    """
+    Implements flow dynamics on the toroidal manifold.
+    
+    This class handles the flow of information and energy across the torus,
+    enabling the self-stabilizing properties of the toroidal diffusion model.
+    """
+    
+    def __init__(self, channels: int, flow_strength: float = 0.1):
+        super().__init__()
+        self.channels = channels
+        self.flow_strength = flow_strength
+        
+        # Learnable flow parameters
+        self.flow_weights = nn.Parameter(torch.randn(channels, channels) * 0.1)
+        self.flow_bias = nn.Parameter(torch.zeros(channels))
+        
+    def compute_flow_field(self, latent: torch.Tensor) -> torch.Tensor:
+        """
+        Compute the flow field on the toroidal surface.
+        
+        Args:
+            latent: Input latent tensor on the torus
+            
+        Returns:
+            flow_field: Vector field representing flow directions
+        """
+        batch_size, channels, height, width = latent.shape
+        
+        # Compute gradients for flow direction
+        grad_x = torch.diff(latent, dim=3, prepend=latent[:, :, :, -1:])
+        grad_y = torch.diff(latent, dim=2, prepend=latent[:, :, -1:, :])
+        
+        # Apply learnable transformation
+        flow_x = torch.einsum('bchw,cd->bdhw', grad_x, self.flow_weights) + self.flow_bias.view(1, -1, 1, 1)
+        flow_y = torch.einsum('bchw,cd->bdhw', grad_y, self.flow_weights) + self.flow_bias.view(1, -1, 1, 1)
+        
+        # Combine into flow field
+        flow_field = torch.stack([flow_x, flow_y], dim=-1)
+        
+        return flow_field
+    
+    def apply_flow(self, latent: torch.Tensor, flow_field: torch.Tensor, dt: float = 0.01) -> torch.Tensor:
+        """
+        Apply flow dynamics to the latent tensor.
+        
+        Args:
+            latent: Input latent tensor
+            flow_field: Flow field tensor
+            dt: Time step for flow integration
+            
+        Returns:
+            flowed_latent: Latent tensor after applying flow
+        """
+        # Simple Euler integration
+        flow_x, flow_y = flow_field[..., 0], flow_field[..., 1]
+        
+        # Apply flow with periodic boundary conditions
+        flowed_latent = latent + dt * self.flow_strength * (flow_x + flow_y)
+        
+        return flowed_latent
+    
+    def forward(self, latent: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass applying toroidal flow dynamics.
+        
+        Args:
+            latent: Input latent tensor
+            
+        Returns:
+            flowed_latent: Latent tensor after flow application
+        """
+        flow_field = self.compute_flow_field(latent)
+        flowed_latent = self.apply_flow(latent, flow_field)
+        
+        return flowed_latent
+
+
+def test_toroidal_operations():
+    """Test function for toroidal operations."""
+    print("Testing Toroidal Topology Operations...")
+    
+    # Test coordinate transformations
+    coords = ToroidalCoordinates()
+    
+    # Test points
+    theta = torch.tensor([0.0, math.pi/2, math.pi, 3*math.pi/2])
+    phi = torch.tensor([0.0, math.pi/4, math.pi/2, math.pi])
+    
+    # Convert to Cartesian and back
+    x, y, z = coords.toroidal_to_cartesian(theta, phi)
+    theta_back, phi_back = coords.cartesian_to_toroidal(x, y, z)
+    
+    print(f"Original theta: {theta}")
+    print(f"Recovered theta: {theta_back}")
+    print(f"Original phi: {phi}")
+    print(f"Recovered phi: {phi_back}")
+    
+    # Test toroidal latent space
+    latent_space = ToroidalLatentSpace(latent_dim=4)
+    
+    # Create test latent
+    test_latent = torch.randn(2, 4, 8, 8)
+    
+    # Test wrapping
+    result = latent_space(test_latent)
+    print(f"Input latent shape: {test_latent.shape}")
+    print(f"Wrapped latent shape: {result['wrapped_latent'].shape}")
+    print(f"Curvature shape: {result['curvature'].shape}")
+    
+    # Test distance computation
+    latent1 = torch.randn(1, 4, 8, 8)
+    latent2 = torch.randn(1, 4, 8, 8)
+    distance = latent_space.toroidal_distance(latent1, latent2)
+    print(f"Toroidal distance shape: {distance.shape}")
+    
+    # Test flow dynamics
+    flow = ToroidalFlow(channels=4)
+    flowed = flow(test_latent)
+    print(f"Flowed latent shape: {flowed.shape}")
+    
+    print("All tests passed!")
+
+
+if __name__ == "__main__":
+    test_toroidal_operations()
+