Continue rejected v37 through frozen loop

backend/tests/test_belgium_training_loop.py

@@ -31,6 +31,7 @@ def test_training_command_is_cuda_deterministic_and_bound_to_frozen_inputs(tmp_p
     assert "deterministic=True" in command
     assert "seed=42" in command
     assert "epochs=160" in command
+    assert "patience=18" in command
     assert "max_det=1000" in command
     assert "imgsz=640" in command
     assert "optimizer=auto" in command
@@ -104,6 +105,12 @@ def test_dry_run_can_gate_existing_checkpoint_without_training(tmp_path: Path) -
     assert json.loads(result.stdout) == {"training_command": None, "evaluate_existing": True}
 
 
+def test_existing_checkpoint_iteration_directory_can_be_created_without_yolo(tmp_path: Path) -> None:
+    iteration_dir = tmp_path / "closed-loop" / "iteration-001"
+    iteration_dir.mkdir(parents=True, exist_ok=True)
+    assert iteration_dir.is_dir()
+
+
 def test_loop_refuses_failed_dataset_audit(tmp_path: Path) -> None:
     audit = tmp_path / "audit.json"
     audit.write_text(json.dumps({"status": "needs_attention", "low_variance_positive_tile_count": 4}))

docs/CODEX_EXECUTION_LOG.md

@@ -11804,6 +11804,19 @@ Deployment evidence:
   malformed JSON and filesystem errors produce an explicit fail-closed
   supervisor state before any subprocess is started. The updated supervisor
   was activated live with one remaining v37 GPU process.
+- V37 early-stopped normally at epoch 23; epoch 5 remained the immutable best
+  checkpoint. The supervisor observed `results.png` and executed its one-shot
+  completion handoff.
+- Fixed a completed-checkpoint entry defect exposed by that live handoff: the
+  iteration directory is now created before copying/hash-binding an existing
+  model. The repaired calibration ran without opening test/background.
+- V37 improved calibration aggregate F1 to `0.568` and Flanders F1 to `0.307`,
+  but failed Flanders F1/precision/recall and Wallonia precision. It was
+  rejected and produced checksummed failure-driven sampling for iteration 2.
+- Made training patience an explicit orchestrator input. A pre-epoch iteration
+  2 process exposing the old hardcoded value `35` was terminated before any
+  result row existed; iteration 2 restarted from the identical checkpoint and
+  sampling with the frozen `patience=18` contract on CUDA.
 - Added those aerial augmentation parameters to the orchestrator CLI and
   training command, preventing later failure-driven checkpoints from silently
   reverting to generic orientation assumptions.

docs/TODO.md

@@ -976,6 +976,8 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add a guarded calibration-first entry point for completed checkpoints so v37 and future externally interrupted runs can rejoin the automated loop without redundant retraining.
 - [x] Add and activate a host-side, exact-run-marker supervisor that resumes the v37 CUDA checkpoint after container recreation without launching concurrent trainers.
 - [x] Bind v37 completion to a one-shot JSON command that enters calibration-first evaluation and up to 20 automatic failure-driven CUDA iterations with the frozen aerial augmentation contract.
+- [x] Complete v37 by early stopping at epoch 23, reject its best epoch-5 checkpoint calibration-first, and start closed-loop iteration 2 from checksummed failure-driven sampling.
+- [x] Make orchestrator patience explicit and retain the frozen value `18` in every subsequent CUDA iteration.
 - [x] Evaluate the completed v36 YOLO11x checkpoint calibration-first on the rotated v30 holdouts; reject it before opening test/background because the regional calibration gate failed.
 - [ ] Finish and assess the leak-free v37 YOLO11x failure-driven CUDA iteration; open test/background evidence only if every calibration gate passes.
 

scripts/run_belgium_building_training_loop.py

@@ -91,6 +91,7 @@ def training_command(
     project: Path,
     name: str,
     epochs: int,
+    patience: int = 18,
     seed: int,
     batch: int,
     workers: int,
@@ -115,7 +116,7 @@ def training_command(
         f"batch={batch}",
         "device=0",
         f"workers={workers}",
-        "patience=35",
+        f"patience={patience}",
         "cache=disk",
         "close_mosaic=20",
         f"max_det={max_det}",
@@ -180,6 +181,7 @@ def main() -> int:
     parser.add_argument("--output-dir", type=Path, required=True)
     parser.add_argument("--iterations", type=int, default=1)
     parser.add_argument("--epochs", type=int, default=160)
+    parser.add_argument("--patience", type=int, default=18)
     parser.add_argument("--batch", type=int, default=2)
     parser.add_argument("--workers", type=int, default=4)
     parser.add_argument("--max-det", type=int, default=1000)
@@ -236,6 +238,7 @@ def main() -> int:
         index = first_index + offset
         name = f"iteration-{index:03d}"
         iteration_dir = args.output_dir / name
+        iteration_dir.mkdir(parents=True, exist_ok=True)
         train_run = args.output_dir / "runs" / name
         evaluate_existing = args.evaluate_initial_model and offset == 0 and not state["iterations"]
         command = None if evaluate_existing else training_command(
@@ -244,7 +247,8 @@ def main() -> int:
             data=train_yaml,
             project=args.output_dir / "runs",
             name=name,
-            epochs=args.epochs,
+                epochs=args.epochs,
+                patience=args.patience,
             seed=args.seed + index,
             batch=args.batch,
             workers=args.workers,

scripts/tower-v37-completion-command.json

@@ -27,6 +27,8 @@
   "20",
   "--epochs",
   "80",
+  "--patience",
+  "18",
   "--batch",
   "8",
   "--workers",