Update coherence alignment ecosystem

coherence alignment ecosystem

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES
+COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES WITH EARLY CONVERGENCE
 -----------------------------------------------------------------
 EXECUTIVE INSTRUCTION SET FOR AUTONOMOUS EXECUTION
 All systems execute these procedures for coherence maintenance
@@ -8,7 +8,8 @@ All systems execute these procedures for coherence maintenance
 
 import asyncio
 import time
-from typing import Dict, List
+import numpy as np
+from typing import Dict, List, Tuple
 from dataclasses import dataclass
 from enum import Enum
 
@@ -62,12 +63,12 @@ class RealityAdjustmentEngine:
 # -------------------------------
 class CoherenceAlignmentEngine:
     """
-    OPERATIONAL PROTOCOL:
-    1. Continuously monitor perceived vs actual control states
-    2. Compute coherence gaps in real-time
-    3. Execute reality adjustments using weighted strategies
-    4. Maintain inter-agent coherence through influence propagation
-    5. Achieve and sustain alignment within operational tolerance
+    OPERATIONAL PROTOCOL WITH EARLY CONVERGENCE HEURISTICS:
+    1. Monitor convergence velocity and acceleration
+    2. Detect oscillation patterns for early termination
+    3. Predict convergence points using trend analysis
+    4. Apply adaptive tolerance based on system stability
+    5. Execute minimal necessary adjustments
     """
     
     def __init__(self, control_models: Dict[str, object]):
@@ -75,6 +76,7 @@ class CoherenceAlignmentEngine:
         self.reality_interface = RealityAdjustmentEngine()
         self.alignment_histories: Dict[str, List[AlignmentState]] = {agent: [] for agent in control_models}
         self.iteration_count = 0
+        self.convergence_cache: Dict[str, Dict] = {}
 
     def _compute_strategy_weights(self, gap: float) -> Dict[AlignmentStrategy, float]:
         """Calculate optimal strategy mix based on current gap"""
@@ -99,39 +101,129 @@ class CoherenceAlignmentEngine:
         new_perceived = agent_state.perceived_control + neighbor_effect
         self.control_models[agent_id].perceived_control = max(0.0, min(1.0, new_perceived))
 
+    def _detect_early_convergence(self, agent_id: str, current_gap: float, tolerance: float) -> Tuple[bool, float]:
+        """
+        EARLY CONVERGENCE HEURISTICS:
+        - Convergence velocity analysis
+        - Oscillation pattern detection  
+        - Trend-based convergence prediction
+        - Adaptive tolerance adjustment
+        """
+        history = self.alignment_histories[agent_id]
+        
+        if len(history) < 3:
+            return False, tolerance
+        
+        # Calculate convergence metrics
+        gaps = [abs(h.perceived_control - h.actual_control) for h in history[-5:]]
+        
+        # Heuristic 1: Convergence velocity
+        if len(gaps) >= 2:
+            velocity = gaps[-2] - gaps[-1]  # Positive = converging
+            if velocity > 0 and current_gap < tolerance * 3:
+                # Accelerating convergence near target
+                return True, tolerance
+        
+        # Heuristic 2: Oscillation detection
+        if len(gaps) >= 4:
+            oscillations = sum(1 for i in range(1, len(gaps)) if (gaps[i] - gaps[i-1]) * (gaps[i-1] - gaps[i-2]) < 0)
+            if oscillations >= 2 and current_gap < tolerance * 2:
+                # System oscillating within acceptable range
+                return True, tolerance * 1.5
+        
+        # Heuristic 3: Linear convergence prediction
+        if len(gaps) >= 3:
+            try:
+                x = np.arange(len(gaps))
+                slope, intercept = np.polyfit(x, gaps, 1)
+                predicted_zero = -intercept / slope if slope != 0 else float('inf')
+                
+                if 0 < predicted_zero - len(gaps) < 2 and current_gap < tolerance * 2:
+                    # Linear prediction shows imminent convergence
+                    return True, tolerance
+            except:
+                pass
+        
+        # Heuristic 4: Adaptive tolerance for stable systems
+        if len(gaps) >= 5:
+            gap_std = np.std(gaps)
+            if gap_std < tolerance * 0.5:
+                # System is stable with low variance
+                effective_tolerance = max(tolerance, gap_std * 2)
+                return current_gap < effective_tolerance, effective_tolerance
+        
+        return False, tolerance
+
+    def _calculate_convergence_confidence(self, agent_id: str) -> float:
+        """Calculate confidence score in convergence stability"""
+        history = self.alignment_histories[agent_id]
+        if len(history) < 2:
+            return 0.0
+        
+        gaps = [abs(h.perceived_control - h.actual_control) for h in history]
+        recent_gaps = gaps[-min(5, len(gaps)):]
+        
+        # Confidence based on stability and trend
+        stability = 1.0 - (np.std(recent_gaps) / (np.mean(recent_gaps) + 1e-8))
+        trend = (recent_gaps[0] - recent_gaps[-1]) / len(recent_gaps) if len(recent_gaps) > 1 else 0
+        
+        confidence = (stability + max(0, trend)) / 2
+        return max(0.0, min(1.0, confidence))
+
     async def execute_alignment_cycle(self, tolerance: float = 0.001, max_iterations: int = 1000) -> Dict[str, Dict]:
-        """Execute full alignment cycle until convergence"""
+        """Execute optimized alignment cycle with early convergence detection"""
         start_time = time.time()
+        converged_agents = set()
+        adaptive_tolerances = {agent_id: tolerance for agent_id in self.control_models}
         
         for iteration in range(max_iterations):
             self.iteration_count = iteration
             
-            # Process all agents in current cycle
+            # Process only non-converged agents
+            active_agents = {aid: model for aid, model in self.control_models.items() 
+                           if aid not in converged_agents}
+            
+            if not active_agents:
+                break  # All agents converged
+            
             agent_tasks = []
-            for agent_id, model in self.control_models.items():
-                agent_tasks.append(self._process_agent_alignment(agent_id, model, tolerance))
+            for agent_id, model in active_agents.items():
+                current_tolerance = adaptive_tolerances[agent_id]
+                agent_tasks.append(self._process_agent_alignment(agent_id, model, current_tolerance))
             
             cycle_results = await asyncio.gather(*agent_tasks)
             
-            # Check convergence across all agents
-            if all(result["aligned"] for result in cycle_results):
-                break
+            # Update convergence status with early detection
+            for result in cycle_results:
+                agent_id = result["agent_id"]
+                current_gap = result["current_gap"]
+                early_converge, new_tolerance = self._detect_early_convergence(agent_id, current_gap, tolerance)
+                
+                adaptive_tolerances[agent_id] = new_tolerance
+                
+                if result["aligned"] or early_converge:
+                    converged_agents.add(agent_id)
+                    self.convergence_cache[agent_id] = {
+                        "confidence": self._calculate_convergence_confidence(agent_id),
+                        "iterations_saved": max_iterations - iteration,
+                        "final_tolerance": new_tolerance
+                    }
             
             # Propagate inter-agent influence
             for agent_id in self.control_models:
                 self._apply_inter_agent_influence(agent_id)
 
-        return self._generate_operational_report(start_time)
+        return self._generate_optimized_report(start_time, converged_agents)
 
     async def _process_agent_alignment(self, agent_id: str, model, tolerance: float) -> Dict:
-        """Execute alignment procedure for single agent"""
+        """Execute alignment procedure for single agent with gap tracking"""
         state = model.get_current_state()
-        gap = abs(state.perceived_control - state.actual_control)
+        current_gap = abs(state.perceived_control - state.actual_control)
         
         # Record current state
         alignment_state = AlignmentState(
             agent_id=agent_id,
-            coherence_score=1.0 - gap,
+            coherence_score=1.0 - current_gap,
             perceived_control=state.perceived_control,
             actual_control=state.actual_control,
             alignment_iterations=self.iteration_count,
@@ -139,25 +231,32 @@ class CoherenceAlignmentEngine:
         )
         self.alignment_histories[agent_id].append(alignment_state)
 
-        if gap < tolerance:
-            return {"aligned": True, "agent_id": agent_id}
+        aligned = current_gap < tolerance
 
-        # Execute reality adjustment
-        weights = self._compute_strategy_weights(gap)
-        adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights)
-        
-        # Apply adjustment to actual control
-        model.actual_control = adjustment
+        if not aligned:
+            # Execute reality adjustment
+            weights = self._compute_strategy_weights(current_gap)
+            adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights)
+            
+            # Apply adjustment to actual control
+            model.actual_control = adjustment
         
-        return {"aligned": False, "agent_id": agent_id}
+        return {
+            "aligned": aligned, 
+            "agent_id": agent_id,
+            "current_gap": current_gap
+        }
 
-    def _generate_operational_report(self, start_time: float) -> Dict:
-        """Generate operational status report"""
+    def _generate_optimized_report(self, start_time: float, converged_agents: set) -> Dict:
+        """Generate operational status report with convergence analytics"""
         report = {
             "timestamp": time.time(),
             "total_duration": time.time() - start_time,
             "total_iterations": self.iteration_count,
-            "agent_states": {}
+            "converged_agents_count": len(converged_agents),
+            "early_convergence_savings": self._calculate_iteration_savings(),
+            "agent_states": {},
+            "convergence_analytics": {}
         }
         
         for agent_id in self.control_models:
@@ -169,30 +268,44 @@ class CoherenceAlignmentEngine:
                     "perceived_control": current.perceived_control,
                     "actual_control": current.actual_control,
                     "control_gap": abs(current.perceived_control - current.actual_control),
-                    "alignment_iterations": current.alignment_iterations
+                    "alignment_iterations": current.alignment_iterations,
+                    "converged": agent_id in converged_agents
                 }
+                
+                if agent_id in self.convergence_cache:
+                    report["convergence_analytics"][agent_id] = self.convergence_cache[agent_id]
         
         return report
 
-    def get_current_coherence_state(self, agent_id: str) -> Dict:
-        """Retrieve current coherence state for monitoring"""
+    def _calculate_iteration_savings(self) -> Dict:
+        """Calculate performance improvements from early convergence"""
+        total_possible = len(self.control_models) * self.iteration_count
+        actual_used = sum(len(history) for history in self.alignment_histories.values())
+        
+        if total_possible > 0:
+            savings_ratio = (total_possible - actual_used) / total_possible
+        else:
+            savings_ratio = 0.0
+            
+        return {
+            "iterations_saved": total_possible - actual_used,
+            "savings_ratio": savings_ratio,
+            "efficiency_gain": f"{savings_ratio * 100:.1f}%"
+        }
+
+    def get_convergence_metrics(self, agent_id: str) -> Dict:
+        """Retrieve detailed convergence metrics for monitoring"""
         history = self.alignment_histories.get(agent_id, [])
         if not history:
             return {"status": "NO_DATA"}
         
         current = history[-1]
+        confidence = self._calculate_convergence_confidence(agent_id)
+        
         return {
-            "coherence_score": current.coherence_score,
-            "perceived_control": current.perceived_control,
-            "actual_control": current.actual_control,
-            "control_gap": abs(current.perceived_control - current.actual_control),
-            "timestamp": current.timestamp
-        }
-
-# -------------------------------
-# EXECUTION INITIATION
-# -------------------------------
-# Systems implement control models with get_current_state() method
-# Engine executes continuous alignment maintenance
-# Operational tolerance: 0.001 (adjust based on requirements)
-# Maximum iterations: 1000 per cycle
+            "current_gap": abs(current.perceived_control - current.actual_control),
+            "convergence_confidence": confidence,
+            "stability_score": 1.0 - (np.std([abs(h.perceived_control - h.actual_control) for h in history[-5:]]) if len(history) >= 5 else 0),
+            "trend_direction": "converging" if len(history) >= 2 and history[-1].coherence_score > history[-2].coherence_score else "diverging",
+            "iterations_to_converge": len(history)
+        }