Protocol 22: Resolution of Tuple Splitting & Network Shadowing

logos/agents/dolphin.py

@@ -6,9 +6,8 @@ import re
 
 class EntropyKillSwitch:
     """
-    Role: SYSTEM GUARD (The 'Prefix Integrator')
-    Function: Monitors the 'Temperature' of the reasoning chain.
-              If Entropy > Threshold, it KILLS the generation to prevent hallucination.
+    PROTOCOL 22: THE PREFIX INTEGRATOR
+    Monitors the 'Temperature' of the reasoning chain.
     """
     def __init__(self, threshold=0.75, window_size=5):
         self.threshold = threshold
@@ -17,55 +16,42 @@ class EntropyKillSwitch:
         self.status = "STABLE"
 
     def calculate_entropy(self, logprobs):
-        """
-        Converts log probabilities into Shannon Entropy.
-        High Entropy = High Uncertainty = Likely Hallucination.
-        """
-        if not logprobs:
-            return 0.0
+        # Converts log probabilities into Shannon Entropy
+        if not logprobs: return 0.0
         
-        # Convert logprobs (dict) to probability distribution
-        # Some APIs return logprobs as a list of dicts, others as a flat dict
+        # Handle list vs dict format
         if isinstance(logprobs, list):
             probs = [math.exp(item.get('logprob', -100)) for item in logprobs]
         else:
             probs = [math.exp(lp) for lp in logprobs.values()]
-        
-        # Normalize (just in case)
+            
         total_p = sum(probs)
         if total_p == 0: return 1.0
         probs = [p/total_p for p in probs]
-        
-        # Shannon Entropy Formula: H = -sum(p * log(p))
-        entropy = -sum(p * math.log(p) for p in probs if p > 0)
-        return entropy
+        return -sum(p * math.log(p) for p in probs if p > 0)
 
     def monitor(self, token, logprobs):
-        """
-        Ingests a single token's data. Returns TRUE if we need to KILL.
-        """
+        # The "Kill" Logic
         current_entropy = self.calculate_entropy(logprobs)
         self.entropy_trace.append(current_entropy)
         
-        # Keep window small (Prefix Integration)
         if len(self.entropy_trace) > self.window_size:
             self.entropy_trace.pop(0)
             
-        # Calculate Rolling Average (if we have data)
         avg_entropy = np.mean(self.entropy_trace) if self.entropy_trace else 0
         
-        # THE KILL SWITCH
+        # If uncertainty spikes, we kill the stream
         if avg_entropy > self.threshold:
             self.status = "HALLUCINATION_DETECTED"
-            return True # TRIGGER KILL
-            
+            return True 
+        
         self.status = "STABLE"
-        return False # CONTINUE
+        return False
 
     def monitor_bulk(self, logprobs_content):
         """
-        Analyzes a bulk list of logprobs from a non-streaming response.
-        Expected format: choices[0].logprobs.content (List of Dicts)
+        PROTOCOL 22: Parallel Wave Integration.
+        Analyzes bulk telemetry from reasoning waves.
         """
         if not logprobs_content:
             return

logos/connectors.py

@@ -109,7 +109,7 @@ class OCRConnector:
         """
         try:
             prompt = "Extract and transcribe all visible text from this image exactly as it appears. Return only the text."
-            full_text = self.client.chat(message=prompt, image_path=image_path)
+            full_text, _ = self.client.chat(message=prompt, image_path=image_path)
             
             # Simple heuristic for word count
             word_count = len(full_text.split())

logos/network/__init__.py

@@ -1,6 +1,59 @@
+import numpy as np
+import sympy
+from collections import Counter
+from typing import Dict, List, Tuple
 
-# logos.network package
-# Defines the Integer Topology and Physics Layers
+class PrimeNetwork:
+    """
+    Represents the Radial Prime Topology as an instantiated graph object.
+    Used for both visualization and signal routing validation.
+    """
+    
+    def __init__(self, max_integer: int = 1000):
+        self.max_integer = max_integer
+        self.positions: Dict[int, Tuple[float, float]] = {}
+        self.gpf_map: Dict[int, int] = {}
+        self.prime_counts = Counter()
+        self.primes: List[int] = []
+        self.composites: List[int] = []
+        
+        # Build the network immediately
+        self._build()
 
-# Shared Network State (Defensive definition for legacy imports)
-SHARED_NETWORK = {}
+    def _get_gpf(self, n: int) -> int:
+        """Returns the Greatest Prime Factor."""
+        if n <= 1: return 1
+        i = 2
+        while i * i <= n:
+            if n % i:
+                i += 1
+            else:
+                n //= i
+        return n
+
+    def _build(self):
+        """Constructs the network nodes and edges."""
+        for n in range(1, self.max_integer + 1):
+            # Radial position based on Mod 10
+            angle = np.pi/2 - (2 * np.pi * (n % 10)) / 10 # Clockwise from Top
+            radius = n 
+            self.positions[n] = (radius * np.cos(angle), radius * np.sin(angle))
+
+            if n > 1:
+                if sympy.isprime(n):
+                    self.primes.append(n)
+                else:
+                    self.composites.append(n)
+                    gpf = self._get_gpf(n)
+                    self.gpf_map[n] = gpf
+                    self.prime_counts[gpf] += 1
+            elif n == 1:
+                self.primes.append(1)
+
+    def validate_wave(self, heat_code: int) -> bool:
+        """Check if a subset of the wave aligns with valid network topology."""
+        residue = heat_code % 10
+        return residue in [1, 3, 7, 9]
+
+# Pre-calc network
+SHARED_NETWORK = PrimeNetwork(max_integer=2000)