Record operator YOLO dataset audit results

docs/CODEX_EXECUTION_LOG.md

@@ -5655,6 +5655,12 @@ Open:
 ## What was tested
 
 - `python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q`
+- `bash scripts/run_readiness_check.sh`
+- Tower live audit after pulling commit `5898e54`:
+  - `yolo-building-tile-dataset`: `needs_attention`; only 3 positive samples and no background negatives.
+  - `yolo-building-tile-expanded160`: `ok`; 10 samples, 8 positive samples, 360 tiles, 11,213 labels, no missing/invalid label rows.
+  - `yolo-building-tile-hardneg160r4`: `needs_attention`; repeated background negatives are 91.1% of negative tiles.
+  - `yolo-building-tile-hardneg160r8`: `needs_attention`; repeated background negatives are 95.4% of negative tiles.
 
 ## Known limitations
 
@@ -5663,4 +5669,4 @@ Open:
 
 ## Next recommended pass
 
-- Run the audit against the existing Tower tile datasets and use the results to decide the next training-data expansion pass.
+- Add more unique hard-negative/background AOIs before another hard-negative training run. The current label files are clean, so the bottleneck is dataset diversity and balance rather than label-file corruption.

docs/TODO.md

@@ -441,4 +441,6 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add a dataset/label-quality audit for generated operator YOLO tile datasets.
 - [x] Report sample coverage, validation coverage, repeated hard-negative pressure and YOLO label area integrity.
 - [x] Wire the audit script into the readiness syntax gate.
-- [ ] Use live audit output to decide whether the next model pass needs more positive AOIs, label cleanup or unique hard negatives.
+- [x] Use live audit output to decide whether the next model pass needs more positive AOIs, label cleanup or unique hard negatives.
+- [ ] Add more unique background/hard-negative AOIs before repeating hard-negative-balanced YOLO training.
+- [ ] Keep `yolo-building-tile-expanded160` as the clean current training baseline; avoid promoting r4/r8 repeat-heavy datasets as defaults.