Gate completed checkpoints through training loop

backend/tests/test_belgium_training_loop.py

@@ -76,6 +76,31 @@ def test_failed_iteration_builds_train_only_sampling_for_next_checkpoint(tmp_pat
     assert command[command.index("--output-dir") + 1].endswith("failure-driven-training")
 
 
+def test_dry_run_can_gate_existing_checkpoint_without_training(tmp_path: Path) -> None:
+    audit = tmp_path / "audit.json"
+    audit.write_text(json.dumps({
+        "status": "needs_human_review", "failures": [],
+        "manifest_immutable": True, "spatial_leakage_status": "ok",
+    }))
+    result = subprocess.run(
+        [
+            sys.executable, str(SCRIPT),
+            "--initial-model", str(tmp_path / "candidate.pt"),
+            "--train-yaml", str(tmp_path / "dataset.yaml"),
+            "--train-summary", str(tmp_path / "train-summary.json"),
+            "--dataset-audit", str(audit),
+            "--calibration-summary", str(tmp_path / "cal.json"),
+            "--test-summary", str(tmp_path / "test.json"),
+            "--background-summary", str(tmp_path / "background.json"),
+            "--corpus-manifest", str(tmp_path / "manifest.json"),
+            "--output-dir", str(tmp_path / "output"),
+            "--evaluate-initial-model", "--dry-run",
+        ], capture_output=True, text=True, check=False,
+    )
+    assert result.returncode == 0
+    assert json.loads(result.stdout) == {"training_command": None, "evaluate_existing": True}
+
+
 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/BELGIUM_BUILDING_TRAINING_LOOP.md

@@ -96,6 +96,11 @@ The checkpointed orchestrator invokes this builder after every rejected
 iteration, stores its checksum in `training-loop-state.json`, and uses the
 resulting dataset YAML for the next checkpoint. A restart resumes both the
 candidate weights and that exact failure-driven training input.
+An already completed out-of-band checkpoint enters the same contract with
+`--evaluate-initial-model`: the first iteration skips fitting, copies and
+hashes the checkpoint, and begins at calibration. A rejection then follows
+the identical failure-driven CUDA path and cannot open protected test evidence
+early.
 The orchestrator refuses to start unless every automated frozen-dataset gate
 passes and the corpus contains zero blank/low-variance positive tiles. The
 audit status may remain `needs_human_review` while training and objective

docs/CODEX_EXECUTION_LOG.md

@@ -11777,6 +11777,11 @@ Deployment evidence:
   leak-free failure-driven sampler automatically, records its evidence
   checksum and next dataset YAML in `training-loop-state.json`, and resumes
   both the candidate weights and exact sampling input after interruption.
+- Added `--evaluate-initial-model` for completed checkpoints such as v37. It
+  skips redundant fitting only for the first iteration, copies and hashes the
+  supplied weights, runs calibration first, and rejoins the same automatic
+  sampling/training path after rejection. Protected evidence remains closed
+  until calibration passes.
 - Confirmed v37 epoch 1 completed on CUDA with validation precision `0.601`,
   recall `0.455`, mAP50 `0.474` and mAP50-95 `0.205`; the run remains inactive
   and these internal-validation metrics are not release evidence.
@@ -11786,7 +11791,7 @@ Verified in this pass:
 - `py -3 -m pytest -q backend/tests/test_belgium_training_loop.py backend/tests/test_belgium_training_iteration_assessment.py backend/tests/test_belgium_training_portfolio.py`
   (`12 passed`).
 - `py -3 -m pytest -q backend/tests/test_belgium_training_loop.py backend/tests/test_failure_driven_yolo_sampling.py backend/tests/test_belgium_training_iteration_assessment.py`
-  (`15 passed`).
+  (`16 passed` after adding the completed-checkpoint entry contract).
 
 Open:
 

docs/TODO.md

@@ -973,6 +973,7 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Exclude GRB/PICC features created after the corresponding dated imagery period while retaining auditable rejection evidence.
 - [x] Allow the objective CUDA loop to consume an automatically clean `needs_human_review` corpus while keeping final human sign-off as a separate, mandatory promotion gate.
 - [x] Persist checksummed train-only failure-driven sampling after every rejected loop iteration and resume the next checkpoint from that exact dataset YAML.
+- [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] 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

@@ -191,6 +191,11 @@ def main() -> int:
     parser.add_argument("--min-region-recall", type=float, default=0.4)
     parser.add_argument("--max-pure-empty-fp", type=int, default=0)
     parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument(
+        "--evaluate-initial-model",
+        action="store_true",
+        help="Gate an already trained initial checkpoint before starting the next training iteration.",
+    )
     args = parser.parse_args()
     if args.iterations < 1:
         raise SystemExit("--iterations must be positive")
@@ -223,7 +228,8 @@ def main() -> int:
         name = f"iteration-{index:03d}"
         iteration_dir = args.output_dir / name
         train_run = args.output_dir / "runs" / name
-        command = training_command(
+        evaluate_existing = args.evaluate_initial_model and offset == 0 and not state["iterations"]
+        command = None if evaluate_existing else training_command(
             args.yolo,
             model=model,
             data=train_yaml,
@@ -242,12 +248,18 @@ def main() -> int:
             translate=args.translate,
         )
         if args.dry_run:
-            print(json.dumps({"training_command": command}, indent=2))
+            print(json.dumps({"training_command": command, "evaluate_existing": evaluate_existing}, indent=2))
             return 0
-        run(command, iteration_dir / "training.log")
+        if evaluate_existing:
-        best = train_run / "weights" / "best.pt"
+            best = model
-        if not best.is_file():
+            if not best.is_file():
-            raise RuntimeError(f"Training produced no best checkpoint: {best}")
+                raise RuntimeError(f"Initial checkpoint does not exist: {best}")
+        else:
+            assert command is not None
+            run(command, iteration_dir / "training.log")
+            best = train_run / "weights" / "best.pt"
+            if not best.is_file():
+                raise RuntimeError(f"Training produced no best checkpoint: {best}")
         candidate = iteration_dir / "candidate.pt"
         shutil.copy2(best, candidate)
 
@@ -347,6 +359,7 @@ def main() -> int:
             "iteration": index,
             "candidate": str(candidate),
             "candidate_sha256": sha256(candidate),
+            "training_skipped_for_existing_checkpoint": evaluate_existing,
             "assessment": str(assessment),
             "status": decision["status"],
             "failures": decision["failures"],