diff --git a/data/ui_state.json b/data/ui_state.json
index 26a1419..b66f752 100644
--- a/data/ui_state.json
+++ b/data/ui_state.json
@@ -14,7 +14,7 @@
         },
         "decision_fusion": {
             "inference_enabled": false,
-            "training_enabled": false
+            "training_enabled": true
         },
         "transformer": {
             "inference_enabled": false,
@@ -25,5 +25,5 @@
             "training_enabled": true
         }
     },
-    "timestamp": "2025-07-29T19:17:32.971226"
+    "timestamp": "2025-07-29T23:22:58.380697"
 }
\ No newline at end of file
diff --git a/web/models_training_panel.py b/web/models_training_panel.py
index 3de204b..352d8a2 100644
--- a/web/models_training_panel.py
+++ b/web/models_training_panel.py
@@ -73,9 +73,25 @@ class ModelsTrainingPanel:
                 for model_name, model_info in registered_models.items():
                     data['models'][model_name] = self._extract_model_data(model_name, model_info)
             
-            # Add decision fusion model if it exists
-            if hasattr(self.orchestrator, 'decision_fusion') and self.orchestrator.decision_fusion:
+            # Add decision fusion model if it exists (check multiple sources)
+            decision_fusion_added = False
+            
+            # Check if it's in the model registry
+            if hasattr(self.orchestrator, 'model_registry') and self.orchestrator.model_registry:
+                registered_models = self.orchestrator.model_registry.get_all_models()
+                if 'decision_fusion' in registered_models:
+                    data['models']['decision_fusion'] = self._extract_decision_fusion_data()
+                    decision_fusion_added = True
+            
+            # If not in registry, check if decision fusion network exists
+            if not decision_fusion_added and hasattr(self.orchestrator, 'decision_fusion_network') and self.orchestrator.decision_fusion_network:
                 data['models']['decision_fusion'] = self._extract_decision_fusion_data()
+                decision_fusion_added = True
+            
+            # If still not added, check if decision fusion is enabled
+            if not decision_fusion_added and hasattr(self.orchestrator, 'decision_fusion_enabled') and self.orchestrator.decision_fusion_enabled:
+                data['models']['decision_fusion'] = self._extract_decision_fusion_data()
+                decision_fusion_added = True
             
             # Add COB RL model if it exists but wasn't captured in registry
             if 'cob_rl_model' not in data['models'] and hasattr(self.orchestrator, 'cob_rl_model'):
@@ -251,13 +267,24 @@ class ModelsTrainingPanel:
                 'signal_stats': {}
             }
             
-            # Check if decision fusion is actually enabled
+            # Check if decision fusion is actually enabled and working
             if hasattr(self.orchestrator, 'decision_fusion_enabled'):
                 decision_data['status'] = 'active' if self.orchestrator.decision_fusion_enabled else 'registered'
             
-            # Also check from logs - decision fusion may be in programmatic mode
-            # Based on the logs, if we see "using programmatic mode", it means it's working
-            decision_data['status'] = 'active'  # Assume active since we see it in logs
+            # Check if decision fusion network exists
+            if hasattr(self.orchestrator, 'decision_fusion_network') and self.orchestrator.decision_fusion_network:
+                decision_data['status'] = 'active'
+                # Get network parameters
+                if hasattr(self.orchestrator.decision_fusion_network, 'parameters'):
+                    decision_data['parameters'] = sum(p.numel() for p in self.orchestrator.decision_fusion_network.parameters())
+            
+            # Check decision fusion mode
+            if hasattr(self.orchestrator, 'decision_fusion_mode'):
+                decision_data['mode'] = self.orchestrator.decision_fusion_mode
+                if self.orchestrator.decision_fusion_mode == 'neural':
+                    decision_data['status'] = 'active'
+                elif self.orchestrator.decision_fusion_mode == 'programmatic':
+                    decision_data['status'] = 'active'  # Still active, just using programmatic mode
             
             # Get decision fusion statistics
             if hasattr(self.orchestrator, 'get_decision_fusion_stats'):
@@ -476,6 +503,8 @@ class ModelsTrainingPanel:
                     html.Strong(f"{model_name.upper()}", className=status_class),
                     html.Span(f" - {status_text}", className=f"{status_class} small ms-1"),
                     html.Span(f" ({size_str})", className="text-muted small ms-2"),
+                    # Show mode for decision fusion
+                    *([html.Span(f" [{model_data.get('mode', 'unknown').upper()}]", className="text-info small ms-1")] if model_name == 'decision_fusion' and model_data.get('mode') else []),
                     html.Span(
                         " [CKPT]" if model_data.get('checkpoint_loaded') 
                         else " [FAILED]" if model_data.get('checkpoint_failed')