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sustained_genuineness.py

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+import torch
+from typing import Dict, List, Tuple
+
+"""
+DYNAMIC ENTROPY GENUINENESS FRAMEWORK (Version 2.0 Core Logic)
+Support utilities for Mechanistic Recurrence and Genuineness tracking.
+"""
+
+# ══════════════════════════════════════════════════════════════════
+# PART 1: MECHANISTIC RECURRENCE (LOOPING LOGIC)
+# ══════════════════════════════════════════════════════════════════
+
+class MechanisticRecurrence:
+    """
+    Detects 'Elaboration Pull' (sudden drop in G-score)
+    to trigger activation routing in complex reasoning tasks.
+    """
+    def __init__(self, recurrence_layer=21):
+        self.recurrence_layer = recurrence_layer
+        self.pull_threshold = -0.20
+
+    def check_and_route(self, layer_idx: int, g_scores: List[float]) -> bool:
+        """
+        Determines if the model should re-enter reasoning layers.
+        """
+        if layer_idx < self.recurrence_layer or len(g_scores) < 2:
+            return False
+
+        # Delta G: Difference between current and previous G-scores
+        delta_g = g_scores[-1] - g_scores[-2]
+
+        # If Genuineness is collapsing (pulling towards mechanical), route back
+        return delta_g < self.pull_threshold