fix(navigation): enforce HD Map pre-checks and resolve test inconsistencies

GramAddict/core/navigation/planner.py

@@ -101,7 +101,18 @@ class GoalPlanner:
                 if count >= 2:  # MAX_RETRIES is 2 in goap
                     avoid_actions.add(act)
 
-        # ── 1. Brain-Driven Decision Making (Primary Strategy) ──
+        target_screen = ScreenTopology.goal_to_target_screen(goal)
+        
+        # ── 1. HD Map Pre-Check for Dead Ends ──
+        # If the topological map KNOWS the target is unreachable due to action_failures,
+        # we must preempt the Brain from blindly routing into a dead end.
+        if target_screen and target_screen != screen_type:
+            route = ScreenTopology.find_route(screen_type, target_screen, avoid_actions=avoid_actions)
+            if route is None and ScreenTopology.find_route(screen_type, target_screen):
+                logger.warning(f"🛡️ [HD Map] Target {target_screen.name} is unreachable due to masked edges! Preventing Brain from blind routing.")
+                return None
+
+        # ── 2. Brain-Driven Decision Making (Primary Strategy) ──
         # The user explicitly wants the AI to be the primary driver of goals.
         from GramAddict.core.navigation.brain import ask_brain_for_action
 

GramAddict/core/situational_awareness.py

@@ -369,8 +369,8 @@ class SituationalAwarenessEngine:
                     args = Config().args
                 except Exception:
                     pass
-                model = getattr(args, "ai_telepathic_model", "qwen3.5:latest")
-                url = getattr(args, "ai_telepathic_url", "http://localhost:11434/api/generate")
+                model = getattr(args, "ai_model", "qwen3.5:latest")
+                url = getattr(args, "ai_model_url", "http://localhost:11434/api/generate")
 
                 res = query_telepathic_llm(
                     model=model,
@@ -459,8 +459,8 @@ class SituationalAwarenessEngine:
                 args = Config().args
             except Exception:
                 pass
-            model = getattr(args, "ai_telepathic_model", "qwen3.5:latest")
-            url = getattr(args, "ai_telepathic_url", "http://localhost:11434/api/generate")
+            model = getattr(args, "ai_model", "qwen3.5:latest")
+            url = getattr(args, "ai_model_url", "http://localhost:11434/api/generate")
 
             res = query_telepathic_llm(
                 model=model, url=url, system_prompt="Strict JSON classifier.", user_prompt=prompt, use_local_edge=True