Optimize hybrid geometric ensemble for massive HSS boost

script.py

@@ -299,9 +299,12 @@ def hybrid_merge(pred_v, pred_e, track_v, track_e, merge_radius=0.8):
         if u_f is not None and v_f is not None and u_f != v_f:
             e = (min(u_f, v_f), max(u_f, v_f))
             if e not in existing_edges:
-                final_e.append(e)
-                existing_edges.add(e)
-                
+                # ONLY append the tracked edge if it connects to a NEWLY DISCOVERED vertex.
+                # This prevents the geometric tracker from aggressively re-wiring the learned model's existing topology!
+                if u_f >= len(pred_v) or v_f >= len(pred_v):
+                    final_e.append(e)
+                    existing_edges.add(e)
+                    
     return np.array(final_v), final_e
 
 # ---------------------------------------------------------------------------
@@ -392,8 +395,8 @@ if __name__ == "__main__":
                     # Apply handcrafted triangulation tracking to catch missing corners/edges
                     try:
                         from triangulation import predict_wireframe_tracks
-                        # Force TRACK_MIN_VIEWS = 2 for aggressive recall
-                        track_v, track_e = predict_wireframe_tracks(sample, min_views=2)
+                        # Use min_views=3 for highly precise, conservative geometric tracks
+                        track_v, track_e = predict_wireframe_tracks(sample, min_views=3)
                         
                         pred_v, pred_e = hybrid_merge(pred_v, pred_e, track_v, track_e, merge_radius=0.8)
                     except Exception as track_e_err: