snapshot: WIP 5j replay reliability (B1 watchdog + dialog handlers + grounding drift)

Snapshot avant correction du blocage relance Léa (3 incidents 24h: SSH refusé,
polls morts ×2). Point de rollback stable.

Contenu:
- agent_v1/core/executor.py: 5 patchs dialog handling (saveas drift, close_tab
  hotkey fallback, confirm_save Unicode apostrophe, foreground dialog
  recontextualization, runtime_dialog in-loop) + helpers normalize_window_hint,
  requires_post_verify_window_transition
- agent_v1/core/grounding.py: garde drift template fix (fallback_x/y plumbed)
- server_v1/replay_watchdog.py (NEW): orphan watchdog B1, scan 10s timeout 30s
- server_v1/api_stream.py: dispatched_action plumbing, watchdog lifespan,
  metrics endpoint
- server_v1/replay_engine.py: _schedule_retry préserve original_action +
  dispatched_action
- stream_processor.py: gardes _infer_tab_switch_target (no false switch_tab
  on save_as dialog open) + _attach_expected_window_before
- tests/integration: test_replay_watchdog.py (8 cas), test_stream_processor.py
- tests/unit: test_executor_verify_window_guard.py (start_button, close_tab,
  runtime_dialog, post_verify, transition fallbacks)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

agent_v0/server_v1/replay_learner.py

@@ -188,7 +188,12 @@ class ReplayLearner:
         """
         target_spec = action.get("target_spec", {})
         by_text = target_spec.get("by_text", "")
-        window_title = target_spec.get("window_title", "")
+        window_title = (
+            target_spec.get("window_title", "")
+            or action.get("window_title", "")
+            or target_spec.get("expected_window_before", "")
+            or (target_spec.get("context_hints") or {}).get("window_title", "")
+        )
         x_pct = correction.get("x_pct", 0.0)
         y_pct = correction.get("y_pct", 0.0)
 
@@ -207,20 +212,36 @@ class ReplayLearner:
 
         # Stocker dans target_memory.db pour le lookup futur
         try:
-            from .replay_memory import get_target_memory_store
-            store = get_target_memory_store()
-            if store:
-                store.record_success(
-                    screen_signature="human_correction",
+            from .replay_memory import memory_record_success
+            stored = False
+            if window_title:
+                stored = memory_record_success(
+                    window_title=window_title,
                     target_spec=target_spec,
-                    resolved_position={"x_pct": x_pct, "y_pct": y_pct},
+                    x_pct=float(x_pct),
+                    y_pct=float(y_pct),
                     method="human_supervised",
-                    score=1.0,
+                    confidence=1.0,
                 )
+            else:
+                logger.warning(
+                    "[APPRENTISSAGE] Correction humaine non persistée : "
+                    "window_title absent pour '%s'",
+                    by_text,
+                )
+
+            if stored:
                 logger.info(
                     f"[APPRENTISSAGE] Correction stockée dans target_memory : "
                     f"'{by_text}' → ({x_pct:.4f}, {y_pct:.4f})"
                 )
+            elif window_title:
+                logger.warning(
+                    "[APPRENTISSAGE] Correction humaine non persistée : "
+                    "échec memory_record_success pour '%s' dans '%s'",
+                    by_text,
+                    window_title,
+                )
         except Exception as e:
             logger.warning(f"Learning: échec stockage target_memory: {e}")