Update src/components/biokinetic_mesh.py

src/components/biokinetic_mesh.py

@@ -1,426 +1,426 @@
-"""
-Biokinetic Neural Mesh - A biomimetic neural routing system
-Combines biological neural patterns with kinetic state processing for ultra-fast routing
-"""
-
-try:
-    import numpy as np
-except Exception:
-    np = None
-
-try:
-    import torch
-except Exception:
-    torch = None
-
-from typing import Dict, List, Tuple, Optional, Set, Any
-from dataclasses import dataclass
-import logging
-from pathlib import Path
-import json
-from components.quantum_spiderweb import QuantumSpiderweb
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class SynapticNode:
-    """Represents a node in the biokinetic mesh"""
-    id: str
-    energy: float = 1.0
-    connections: Dict[str, float] = None
-    activation_pattern: 'np.ndarray' = None
-    kinetic_state: float = 0.0
-    
-    def __post_init__(self):
-        self.connections = self.connections or {}
-        self.activation_pattern = self.activation_pattern or np.random.rand(128)
-
-class BioKineticMesh:
-    """
-    Biokinetic Neural Mesh - A biomimetic routing system
-    
-    Features:
-    - Ultra-fast pattern recognition (<0.3ms)
-    - Self-evolving neural pathways
-    - Energy-based routing
-    - Synaptic pruning for optimization
-    - Fractal memory patterns
-    - Quantum state integration
-    - Multi-perspective resonance
-    - Adaptive pathway evolution
-    """
-    
-    def __init__(self, 
-                 initial_nodes: int = 512,
-                 energy_threshold: float = 0.3,
-                 learning_rate: float = 0.01,
-                 prune_threshold: float = 0.1,
-                 quantum_influence: float = 0.3,
-                 perspective_resonance: float = 0.2):
-        self.nodes: Dict[str, SynapticNode] = {}
-        self.energy_threshold = energy_threshold
-        self.learning_rate = learning_rate
-        self.prune_threshold = prune_threshold
-        self.quantum_influence = quantum_influence
-        self.perspective_resonance = perspective_resonance
-        
-        # Kinetic state tensors
-        if torch is not None:
-            self.kinetic_matrix = torch.zeros((initial_nodes, initial_nodes))
-            self.energy_gradients = torch.zeros(initial_nodes)
-        else:
-            self.kinetic_matrix = None
-            self.energy_gradients = [0.0] * initial_nodes
-        
-        # Pattern recognition layers
-        if np is not None:
-            self.pattern_embeddings = np.random.rand(initial_nodes, 128)
-        else:
-            self.pattern_embeddings = [[0.0]*128 for _ in range(initial_nodes)]
-        
-        # Activation history
-        self.activation_history: List['np.ndarray'] = []
-        
-        # Integration components
-        self.quantum_resonance: Dict[str, float] = {}  # Quantum state influence
-        self.perspective_weights: Dict[str, Dict[str, float]] = {}  # Per-node perspective weights
-        self.active_pathways: Set[Tuple[str, str]] = set()  # Currently active neural pathways
-        
-        # Initialize mesh
-        # Initialize mesh
-        try:
-            self._initialize_mesh(initial_nodes)
-        except Exception as e:
-            logger.warning(f"Failed to fully initialize mesh: {e}")
-        
-    def _initialize_mesh(self, node_count: int):
-        """Initialize the biokinetic mesh with initial nodes"""
-        for i in range(node_count):
-            node_id = f"BK_{i}"
-            self.nodes[node_id] = SynapticNode(
-                id=node_id,
-                energy=1.0,
-                activation_pattern=np.random.rand(128)
-            )
-            
-        # Create initial connections (sparse)
-        for node in self.nodes.values():
-            connection_count = np.random.randint(5, 15)
-            target_nodes = np.random.choice(
-                list(self.nodes.keys()), 
-                size=connection_count, 
-                replace=False
-            )
-            node.connections = {
-                target: np.random.rand() 
-                for target in target_nodes 
-                if target != node.id
-            }
-
-    def route_intent(self, 
-                    input_pattern: np.ndarray, 
-                    context: Optional[Dict] = None) -> Tuple[str, float]:
-        """
-        Route an input pattern through the mesh to determine intent
-        Returns in under 0.3ms
-        """
-        # Convert input to energy pattern
-        energy_pattern = self._compute_energy_pattern(input_pattern)
-        
-        # Fast activation: fall back to python loop if torch missing
-        activations = []
-        for node in self.nodes.values():
-            try:
-                act = self._compute_node_activation(node, energy_pattern, context)
-            except Exception:
-                act = 0.0
-            activations.append(act)
-
-        # Find highest energy path
-        max_idx = int(max(range(len(activations)), key=lambda i: activations[i]))
-        node_id = list(self.nodes.keys())[max_idx]
-        confidence = float(activations[max_idx])
-        
-        # Update kinetic state
-        self._update_kinetic_state(node_id, confidence)
-        
-        return node_id, confidence
-
-    def _compute_energy_pattern(self, input_pattern: np.ndarray) -> torch.Tensor:
-        """Convert input pattern to energy distribution"""
-        # Normalize input
-        if np is not None:
-            input_norm = input_pattern / (np.linalg.norm(input_pattern) + 1e-12)
-        else:
-            # Simple python normalization
-            mag = sum(x*x for x in input_pattern) ** 0.5
-            input_norm = [x / (mag + 1e-12) for x in input_pattern]
-
-        # Create energy tensor if torch available
-        if torch is not None and np is not None:
-            energy = torch.from_numpy(input_norm).float()
-            energy = self._apply_kinetic_transform(energy)
-            return energy
-        else:
-            return input_norm
-
-    def _compute_node_activation(self, 
-                               node: SynapticNode, 
-                               energy_pattern: torch.Tensor,
-                               context: Optional[Dict]) -> float:
-        """Compute node activation based on energy pattern and context"""
-        # Base activation from pattern match (torch optional)
-        if torch is not None:
-            base_activation = torch.cosine_similarity(
-                energy_pattern,
-                torch.from_numpy(node.activation_pattern).float().unsqueeze(0),
-                dim=1
-            )
-            base_val = base_activation.item()
-        else:
-            # fallback cosine similarity
-            a = energy_pattern if isinstance(energy_pattern, (list, tuple)) else energy_pattern.tolist()
-            b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern)
-            dot = sum(x*y for x,y in zip(a,b))
-            norm_a = sum(x*x for x in a) ** 0.5
-            norm_b = sum(x*x for x in b) ** 0.5
-            base_val = dot / (norm_a * norm_b + 1e-12)
-        
-        # Apply kinetic state
-        kinetic_boost = node.kinetic_state * self.learning_rate
-        
-        # Context influence
-        context_factor = 1.0
-        if context:
-            context_pattern = self._context_to_pattern(context)
-            if torch is not None:
-                context_match = torch.cosine_similarity(
-                    torch.from_numpy(context_pattern).float().unsqueeze(0),
-                    torch.from_numpy(node.activation_pattern).float().unsqueeze(0),
-                    dim=1
-                )
-                context_factor = 1.0 + (context_match.item() * 0.5)
-            else:
-                # simple fallback dot match
-                a = context_pattern
-                b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern)
-                dot = sum(x*y for x,y in zip(a,b))
-                norm_a = sum(x*x for x in a) ** 0.5
-                norm_b = sum(x*x for x in b) ** 0.5
-                match = dot / (norm_a * norm_b + 1e-12)
-                context_factor = 1.0 + (match * 0.5)
-        
-        return (base_val + kinetic_boost) * context_factor
-
-    def _apply_kinetic_transform(self, energy: torch.Tensor) -> torch.Tensor:
-        """Apply kinetic transformation to energy pattern"""
-        if torch is not None:
-            # Create momentum factor
-            momentum = torch.sigmoid(self.energy_gradients.mean())
-            
-            # Apply momentum to energy
-            energy = energy * (1.0 + momentum)
-            
-            # Normalize
-            energy = energy / energy.norm()
-            
-            return energy
-        else:
-            mean_grad = sum(self.energy_gradients)/len(self.energy_gradients) if self.energy_gradients else 0.0
-            momentum = 1.0 / (1.0 + (2.718281828 ** (-mean_grad)))
-            energy = [e * (1.0 + momentum) for e in energy]
-            mag = sum(x*x for x in energy) ** 0.5
-            energy = [x / (mag + 1e-12) for x in energy]
-            return energy
-
-    def _update_kinetic_state(self, node_id: str, activation: float):
-        """Update kinetic state of the network"""
-        # Update node energy
-        node = self.nodes[node_id]
-        node.kinetic_state += self.learning_rate * (activation - node.kinetic_state)
-        
-        # Update connected nodes
-        for target_id, weight in node.connections.items():
-            if target_id in self.nodes:
-                target = self.nodes[target_id]
-                target.kinetic_state += (
-                    self.learning_rate * weight * (activation - target.kinetic_state)
-                )
-
-    def _context_to_pattern(self, context: Dict) -> np.ndarray:
-        """Convert context dictionary to pattern vector"""
-        # Create empty pattern
-        if np is not None:
-            pattern = np.zeros(128)
-        else:
-            pattern = [0.0]*128
-        
-        # Add context influences
-        if "mode" in context:
-            pattern += self.pattern_embeddings[
-                hash(context["mode"]) % len(self.pattern_embeddings)
-            ]
-        
-        if "priority" in context:
-            priority_factor = float(context["priority"]) / 10.0
-            pattern *= (1.0 + priority_factor)
-            
-        # Normalize
-        if np is not None:
-            pattern = pattern / (np.linalg.norm(pattern) + 1e-8)
-        else:
-            mag = sum(x*x for x in pattern) ** 0.5
-            pattern = [x / (mag + 1e-8) for x in pattern]
-        
-        return pattern
-
-    def prune_connections(self):
-        """Remove weak or unused connections"""
-        for node in self.nodes.values():
-            # Find weak connections
-            weak_connections = [
-                target_id
-                for target_id, weight in node.connections.items()
-                if weight < self.prune_threshold
-            ]
-            
-            # Remove weak connections
-            for target_id in weak_connections:
-                del node.connections[target_id]
-                
-            # Normalize remaining connections
-            if node.connections:
-                total_weight = sum(node.connections.values())
-                for target_id in node.connections:
-                    node.connections[target_id] /= total_weight
-
-    def integrate_quantum_state(self, quantum_web: QuantumSpiderweb, node_id: str):
-        """Integrate quantum web state with biokinetic mesh"""
-        # Get quantum state for this node
-        quantum_state = quantum_web.get_node_state(node_id)
-        
-        if quantum_state:
-            # Update quantum resonance
-            self.quantum_resonance[node_id] = quantum_state["coherence"]
-            
-            # Influence node connections based on quantum state
-            node = self.nodes.get(node_id)
-            if node:
-                quantum_boost = quantum_state["coherence"] * self.quantum_influence
-                for target_id in node.connections:
-                    node.connections[target_id] *= (1.0 + quantum_boost)
-                    
-                # Update node's kinetic state
-                node.kinetic_state += quantum_boost
-                
-    def integrate_perspective_results(self, 
-                                   node_id: str,
-                                   perspective_results: Dict[str, Dict[str, Any]]):
-        """Integrate perspective processing results into the mesh"""
-        if node_id not in self.perspective_weights:
-            self.perspective_weights[node_id] = {}
-            
-        # Update perspective weights based on confidence
-        total_confidence = 0.0
-        for perspective, result in perspective_results.items():
-            if "confidence" in result:
-                confidence = result["confidence"]
-                self.perspective_weights[node_id][perspective] = confidence
-                total_confidence += confidence
-        
-        if total_confidence > 0:
-            # Normalize weights
-            for perspective in self.perspective_weights[node_id]:
-                self.perspective_weights[node_id][perspective] /= total_confidence
-            
-            # Apply perspective resonance to node
-            node = self.nodes.get(node_id)
-            if node:
-                resonance = sum(
-                    weight * self.perspective_resonance 
-                    for weight in self.perspective_weights[node_id].values()
-                )
-                node.kinetic_state *= (1.0 + resonance)
-                
-    def strengthen_pathway(self, node_sequence: List[str], reward: float):
-        """Strengthen a successful pathway with integrated effects"""
-        for i in range(len(node_sequence) - 1):
-            current_id = node_sequence[i]
-            next_id = node_sequence[i + 1]
-            
-            if current_id in self.nodes and next_id in self.nodes:
-                current_node = self.nodes[current_id]
-                
-                # Add path to active pathways
-                self.active_pathways.add((current_id, next_id))
-                
-                # Calculate integrated boost
-                quantum_boost = self.quantum_resonance.get(current_id, 0.0)
-                perspective_boost = sum(
-                    self.perspective_weights.get(current_id, {}).values()
-                ) / max(len(self.perspective_weights.get(current_id, {})), 1)
-                
-                total_boost = (
-                    1.0 +
-                    quantum_boost * self.quantum_influence +
-                    perspective_boost * self.perspective_resonance
-                )
-                
-                # Strengthen connection with integrated boost
-                if next_id in current_node.connections:
-                    current_node.connections[next_id] += (
-                        self.learning_rate * reward * total_boost
-                    )
-                else:
-                    current_node.connections[next_id] = (
-                        self.learning_rate * reward * total_boost
-                    )
-                    
-                # Update kinetic state
-                current_node.kinetic_state += (
-                    self.learning_rate * reward * total_boost
-                )
-
-    def save_state(self, path: Path):
-        """Save mesh state to file"""
-        state = {
-            "nodes": {
-                node_id: {
-                    "energy": node.energy,
-                    "connections": node.connections,
-                    "kinetic_state": node.kinetic_state,
-                    "activation_pattern": node.activation_pattern.tolist()
-                }
-                for node_id, node in self.nodes.items()
-            },
-            "params": {
-                "energy_threshold": self.energy_threshold,
-                "learning_rate": self.learning_rate,
-                "prune_threshold": self.prune_threshold
-            }
-        }
-        
-        with open(path, 'w') as f:
-            json.dump(state, f)
-
-    def load_state(self, path: Path):
-        """Load mesh state from file"""
-        with open(path, 'r') as f:
-            state = json.load(f)
-            
-        # Restore nodes
-        self.nodes = {
-            node_id: SynapticNode(
-                id=node_id,
-                energy=data["energy"],
-                connections=data["connections"],
-                activation_pattern=np.array(data["activation_pattern"]),
-                kinetic_state=data["kinetic_state"]
-            )
-            for node_id, data in state["nodes"].items()
-        }
-        
-        # Restore parameters
-        self.energy_threshold = state["params"]["energy_threshold"]
-        self.learning_rate = state["params"]["learning_rate"]
+"""
+Biokinetic Neural Mesh - A biomimetic neural routing system
+Combines biological neural patterns with kinetic state processing for ultra-fast routing
+"""
+
+try:
+    import numpy as np
+except Exception:
+    np = None
+
+try:
+    import torch
+except Exception:
+    torch = None
+
+from typing import Dict, List, Tuple, Optional, Set, Any
+from dataclasses import dataclass
+import logging
+from pathlib import Path
+import json
+from .quantum_spiderweb import QuantumSpiderweb  # Changed to relative import
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class SynapticNode:
+    """Represents a node in the biokinetic mesh"""
+    id: str
+    energy: float = 1.0
+    connections: Dict[str, float] = None
+    activation_pattern: 'np.ndarray' = None
+    kinetic_state: float = 0.0
+    
+    def __post_init__(self):
+        self.connections = self.connections or {}
+        self.activation_pattern = self.activation_pattern or np.random.rand(128)
+
+class BioKineticMesh:
+    """
+    Biokinetic Neural Mesh - A biomimetic routing system
+    
+    Features:
+    - Ultra-fast pattern recognition (<0.3ms)
+    - Self-evolving neural pathways
+    - Energy-based routing
+    - Synaptic pruning for optimization
+    - Fractal memory patterns
+    - Quantum state integration
+    - Multi-perspective resonance
+    - Adaptive pathway evolution
+    """
+    
+    def __init__(self, 
+                 initial_nodes: int = 512,
+                 energy_threshold: float = 0.3,
+                 learning_rate: float = 0.01,
+                 prune_threshold: float = 0.1,
+                 quantum_influence: float = 0.3,
+                 perspective_resonance: float = 0.2):
+        self.nodes: Dict[str, SynapticNode] = {}
+        self.energy_threshold = energy_threshold
+        self.learning_rate = learning_rate
+        self.prune_threshold = prune_threshold
+        self.quantum_influence = quantum_influence
+        self.perspective_resonance = perspective_resonance
+        
+        # Kinetic state tensors
+        if torch is not None:
+            self.kinetic_matrix = torch.zeros((initial_nodes, initial_nodes))
+            self.energy_gradients = torch.zeros(initial_nodes)
+        else:
+            self.kinetic_matrix = None
+            self.energy_gradients = [0.0] * initial_nodes
+        
+        # Pattern recognition layers
+        if np is not None:
+            self.pattern_embeddings = np.random.rand(initial_nodes, 128)
+        else:
+            self.pattern_embeddings = [[0.0]*128 for _ in range(initial_nodes)]
+        
+        # Activation history
+        self.activation_history: List['np.ndarray'] = []
+        
+        # Integration components
+        self.quantum_resonance: Dict[str, float] = {}  # Quantum state influence
+        self.perspective_weights: Dict[str, Dict[str, float]] = {}  # Per-node perspective weights
+        self.active_pathways: Set[Tuple[str, str]] = set()  # Currently active neural pathways
+        
+        # Initialize mesh
+        # Initialize mesh
+        try:
+            self._initialize_mesh(initial_nodes)
+        except Exception as e:
+            logger.warning(f"Failed to fully initialize mesh: {e}")
+        
+    def _initialize_mesh(self, node_count: int):
+        """Initialize the biokinetic mesh with initial nodes"""
+        for i in range(node_count):
+            node_id = f"BK_{i}"
+            self.nodes[node_id] = SynapticNode(
+                id=node_id,
+                energy=1.0,
+                activation_pattern=np.random.rand(128)
+            )
+            
+        # Create initial connections (sparse)
+        for node in self.nodes.values():
+            connection_count = np.random.randint(5, 15)
+            target_nodes = np.random.choice(
+                list(self.nodes.keys()), 
+                size=connection_count, 
+                replace=False
+            )
+            node.connections = {
+                target: np.random.rand() 
+                for target in target_nodes 
+                if target != node.id
+            }
+
+    def route_intent(self, 
+                    input_pattern: np.ndarray, 
+                    context: Optional[Dict] = None) -> Tuple[str, float]:
+        """
+        Route an input pattern through the mesh to determine intent
+        Returns in under 0.3ms
+        """
+        # Convert input to energy pattern
+        energy_pattern = self._compute_energy_pattern(input_pattern)
+        
+        # Fast activation: fall back to python loop if torch missing
+        activations = []
+        for node in self.nodes.values():
+            try:
+                act = self._compute_node_activation(node, energy_pattern, context)
+            except Exception:
+                act = 0.0
+            activations.append(act)
+
+        # Find highest energy path
+        max_idx = int(max(range(len(activations)), key=lambda i: activations[i]))
+        node_id = list(self.nodes.keys())[max_idx]
+        confidence = float(activations[max_idx])
+        
+        # Update kinetic state
+        self._update_kinetic_state(node_id, confidence)
+        
+        return node_id, confidence
+
+    def _compute_energy_pattern(self, input_pattern: np.ndarray) -> torch.Tensor:
+        """Convert input pattern to energy distribution"""
+        # Normalize input
+        if np is not None:
+            input_norm = input_pattern / (np.linalg.norm(input_pattern) + 1e-12)
+        else:
+            # Simple python normalization
+            mag = sum(x*x for x in input_pattern) ** 0.5
+            input_norm = [x / (mag + 1e-12) for x in input_pattern]
+
+        # Create energy tensor if torch available
+        if torch is not None and np is not None:
+            energy = torch.from_numpy(input_norm).float()
+            energy = self._apply_kinetic_transform(energy)
+            return energy
+        else:
+            return input_norm
+
+    def _compute_node_activation(self, 
+                               node: SynapticNode, 
+                               energy_pattern: torch.Tensor,
+                               context: Optional[Dict]) -> float:
+        """Compute node activation based on energy pattern and context"""
+        # Base activation from pattern match (torch optional)
+        if torch is not None:
+            base_activation = torch.cosine_similarity(
+                energy_pattern,
+                torch.from_numpy(node.activation_pattern).float().unsqueeze(0),
+                dim=1
+            )
+            base_val = base_activation.item()
+        else:
+            # fallback cosine similarity
+            a = energy_pattern if isinstance(energy_pattern, (list, tuple)) else energy_pattern.tolist()
+            b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern)
+            dot = sum(x*y for x,y in zip(a,b))
+            norm_a = sum(x*x for x in a) ** 0.5
+            norm_b = sum(x*x for x in b) ** 0.5
+            base_val = dot / (norm_a * norm_b + 1e-12)
+        
+        # Apply kinetic state
+        kinetic_boost = node.kinetic_state * self.learning_rate
+        
+        # Context influence
+        context_factor = 1.0
+        if context:
+            context_pattern = self._context_to_pattern(context)
+            if torch is not None:
+                context_match = torch.cosine_similarity(
+                    torch.from_numpy(context_pattern).float().unsqueeze(0),
+                    torch.from_numpy(node.activation_pattern).float().unsqueeze(0),
+                    dim=1
+                )
+                context_factor = 1.0 + (context_match.item() * 0.5)
+            else:
+                # simple fallback dot match
+                a = context_pattern
+                b = node.activation_pattern.tolist() if hasattr(node.activation_pattern, 'tolist') else list(node.activation_pattern)
+                dot = sum(x*y for x,y in zip(a,b))
+                norm_a = sum(x*x for x in a) ** 0.5
+                norm_b = sum(x*x for x in b) ** 0.5
+                match = dot / (norm_a * norm_b + 1e-12)
+                context_factor = 1.0 + (match * 0.5)
+        
+        return (base_val + kinetic_boost) * context_factor
+
+    def _apply_kinetic_transform(self, energy: torch.Tensor) -> torch.Tensor:
+        """Apply kinetic transformation to energy pattern"""
+        if torch is not None:
+            # Create momentum factor
+            momentum = torch.sigmoid(self.energy_gradients.mean())
+            
+            # Apply momentum to energy
+            energy = energy * (1.0 + momentum)
+            
+            # Normalize
+            energy = energy / energy.norm()
+            
+            return energy
+        else:
+            mean_grad = sum(self.energy_gradients)/len(self.energy_gradients) if self.energy_gradients else 0.0
+            momentum = 1.0 / (1.0 + (2.718281828 ** (-mean_grad)))
+            energy = [e * (1.0 + momentum) for e in energy]
+            mag = sum(x*x for x in energy) ** 0.5
+            energy = [x / (mag + 1e-12) for x in energy]
+            return energy
+
+    def _update_kinetic_state(self, node_id: str, activation: float):
+        """Update kinetic state of the network"""
+        # Update node energy
+        node = self.nodes[node_id]
+        node.kinetic_state += self.learning_rate * (activation - node.kinetic_state)
+        
+        # Update connected nodes
+        for target_id, weight in node.connections.items():
+            if target_id in self.nodes:
+                target = self.nodes[target_id]
+                target.kinetic_state += (
+                    self.learning_rate * weight * (activation - target.kinetic_state)
+                )
+
+    def _context_to_pattern(self, context: Dict) -> np.ndarray:
+        """Convert context dictionary to pattern vector"""
+        # Create empty pattern
+        if np is not None:
+            pattern = np.zeros(128)
+        else:
+            pattern = [0.0]*128
+        
+        # Add context influences
+        if "mode" in context:
+            pattern += self.pattern_embeddings[
+                hash(context["mode"]) % len(self.pattern_embeddings)
+            ]
+        
+        if "priority" in context:
+            priority_factor = float(context["priority"]) / 10.0
+            pattern *= (1.0 + priority_factor)
+            
+        # Normalize
+        if np is not None:
+            pattern = pattern / (np.linalg.norm(pattern) + 1e-8)
+        else:
+            mag = sum(x*x for x in pattern) ** 0.5
+            pattern = [x / (mag + 1e-8) for x in pattern]
+        
+        return pattern
+
+    def prune_connections(self):
+        """Remove weak or unused connections"""
+        for node in self.nodes.values():
+            # Find weak connections
+            weak_connections = [
+                target_id
+                for target_id, weight in node.connections.items()
+                if weight < self.prune_threshold
+            ]
+            
+            # Remove weak connections
+            for target_id in weak_connections:
+                del node.connections[target_id]
+                
+            # Normalize remaining connections
+            if node.connections:
+                total_weight = sum(node.connections.values())
+                for target_id in node.connections:
+                    node.connections[target_id] /= total_weight
+
+    def integrate_quantum_state(self, quantum_web: QuantumSpiderweb, node_id: str):
+        """Integrate quantum web state with biokinetic mesh"""
+        # Get quantum state for this node
+        quantum_state = quantum_web.get_node_state(node_id)
+        
+        if quantum_state:
+            # Update quantum resonance
+            self.quantum_resonance[node_id] = quantum_state["coherence"]
+            
+            # Influence node connections based on quantum state
+            node = self.nodes.get(node_id)
+            if node:
+                quantum_boost = quantum_state["coherence"] * self.quantum_influence
+                for target_id in node.connections:
+                    node.connections[target_id] *= (1.0 + quantum_boost)
+                    
+                # Update node's kinetic state
+                node.kinetic_state += quantum_boost
+                
+    def integrate_perspective_results(self, 
+                                   node_id: str,
+                                   perspective_results: Dict[str, Dict[str, Any]]):
+        """Integrate perspective processing results into the mesh"""
+        if node_id not in self.perspective_weights:
+            self.perspective_weights[node_id] = {}
+            
+        # Update perspective weights based on confidence
+        total_confidence = 0.0
+        for perspective, result in perspective_results.items():
+            if "confidence" in result:
+                confidence = result["confidence"]
+                self.perspective_weights[node_id][perspective] = confidence
+                total_confidence += confidence
+        
+        if total_confidence > 0:
+            # Normalize weights
+            for perspective in self.perspective_weights[node_id]:
+                self.perspective_weights[node_id][perspective] /= total_confidence
+            
+            # Apply perspective resonance to node
+            node = self.nodes.get(node_id)
+            if node:
+                resonance = sum(
+                    weight * self.perspective_resonance 
+                    for weight in self.perspective_weights[node_id].values()
+                )
+                node.kinetic_state *= (1.0 + resonance)
+                
+    def strengthen_pathway(self, node_sequence: List[str], reward: float):
+        """Strengthen a successful pathway with integrated effects"""
+        for i in range(len(node_sequence) - 1):
+            current_id = node_sequence[i]
+            next_id = node_sequence[i + 1]
+            
+            if current_id in self.nodes and next_id in self.nodes:
+                current_node = self.nodes[current_id]
+                
+                # Add path to active pathways
+                self.active_pathways.add((current_id, next_id))
+                
+                # Calculate integrated boost
+                quantum_boost = self.quantum_resonance.get(current_id, 0.0)
+                perspective_boost = sum(
+                    self.perspective_weights.get(current_id, {}).values()
+                ) / max(len(self.perspective_weights.get(current_id, {})), 1)
+                
+                total_boost = (
+                    1.0 +
+                    quantum_boost * self.quantum_influence +
+                    perspective_boost * self.perspective_resonance
+                )
+                
+                # Strengthen connection with integrated boost
+                if next_id in current_node.connections:
+                    current_node.connections[next_id] += (
+                        self.learning_rate * reward * total_boost
+                    )
+                else:
+                    current_node.connections[next_id] = (
+                        self.learning_rate * reward * total_boost
+                    )
+                    
+                # Update kinetic state
+                current_node.kinetic_state += (
+                    self.learning_rate * reward * total_boost
+                )
+
+    def save_state(self, path: Path):
+        """Save mesh state to file"""
+        state = {
+            "nodes": {
+                node_id: {
+                    "energy": node.energy,
+                    "connections": node.connections,
+                    "kinetic_state": node.kinetic_state,
+                    "activation_pattern": node.activation_pattern.tolist()
+                }
+                for node_id, node in self.nodes.items()
+            },
+            "params": {
+                "energy_threshold": self.energy_threshold,
+                "learning_rate": self.learning_rate,
+                "prune_threshold": self.prune_threshold
+            }
+        }
+        
+        with open(path, 'w') as f:
+            json.dump(state, f)
+
+    def load_state(self, path: Path):
+        """Load mesh state from file"""
+        with open(path, 'r') as f:
+            state = json.load(f)
+            
+        # Restore nodes
+        self.nodes = {
+            node_id: SynapticNode(
+                id=node_id,
+                energy=data["energy"],
+                connections=data["connections"],
+                activation_pattern=np.array(data["activation_pattern"]),
+                kinetic_state=data["kinetic_state"]
+            )
+            for node_id, data in state["nodes"].items()
+        }
+        
+        # Restore parameters
+        self.energy_threshold = state["params"]["energy_threshold"]
+        self.learning_rate = state["params"]["learning_rate"]
         self.prune_threshold = state["params"]["prune_threshold"]