diff --git a/docs/CODEX_EXECUTION_LOG.md b/docs/CODEX_EXECUTION_LOG.md
index b42a57fe..84994653 100644
--- a/docs/CODEX_EXECUTION_LOG.md
+++ b/docs/CODEX_EXECUTION_LOG.md
@@ -6895,12 +6895,50 @@ Open:
 - Ran `python scripts/export_operator_yolo_tile_dataset.py --help`: the CLI exposes `--drop-low-variance-negatives`, `--no-drop-low-variance-negatives` and `--blank-range-threshold` without loading GIS dependencies.
 - Ran `bash scripts/run_readiness_check.sh`: 460 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.
 
+## Tower runtime evidence
+
+- Pushed commit `a159370` and redeployed the all-in-one Tower runtime at `http://192.168.10.150:1202`.
+- Deploy validation passed:
+  - live migration smoke passed;
+  - browser runtime verification passed;
+  - container exposed `0.0.0.0:1202->80/tcp`.
+- Regenerated the AOI1024 cleanpx dataset with low-variance negative filtering enabled:
+  - dataset: `/app/storage/operator-data/yolo-building-aoi1024-cleanpx12vis035`
+  - `drop_low_variance_negatives=true`
+  - `blank_range_threshold=3`
+  - tile count: `135`
+  - positive tiles: `114`
+  - negative tiles: `21`
+  - skipped negative tiles: `9`
+  - skipped low-variance negative tiles: `9`
+  - labels: `14632`
+  - train tiles: `108`
+  - validation tiles: `27`
+- Dataset audit:
+  - report: `/app/artifacts/operator-yolo-dataset-audit/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_dataset_quality_audit.json`
+  - status: `ok`
+  - invalid labels: `0`
+  - missing label files: `0`
+  - median normalized box area: `0.001373291016`
+  - small-box share: `0.0`
+- Visual label QA:
+  - report: `/app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_label_qa_summary.json`
+  - contact sheet: `/app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/contact_sheet_001.png`
+  - selected tiles: `32`
+  - rendered tiles: `32`
+  - valid labels: `7670`
+  - invalid labels: `0`
+  - missing images: `0`
+  - missing label files: `0`
+  - low-variance rendered tiles: `0`
+- Visual inspection confirmed that the previous blank white `arendonk_heide` negatives are no longer present in the review sheet. The remaining selected pure-empty negatives are real visible orthophoto/context tiles.
+
 ## Known limitations
 
 - The low-variance gate is deliberately simple and only identifies visually blank/no-data-looking negative tiles.
-- It is opt-in to avoid silently changing historical dataset exports.
 - Operator visual contact-sheet review remains required before any new training run.
+- The filtered dataset is now a cleaner input candidate, but model training is still not guaranteed to improve QA/QC; another training run must be gated through the existing positive-AOI and background promotion reports.
 
 ## Next recommended pass
 
-- Redeploy Tower, regenerate the AOI1024 cleanpx dataset with `--drop-low-variance-negatives`, rerun dataset audit and contact-sheet QA, then decide whether the filtered dataset is suitable for another training run.
+- Train one inactive candidate from the filtered AOI1024 cleanpx dataset, then run the existing positive-AOI matrix and split-background promotion workflow before considering default activation.
diff --git a/docs/TODO.md b/docs/TODO.md
index 9e83b6d5..94b048f5 100644
--- a/docs/TODO.md
+++ b/docs/TODO.md
@@ -132,7 +132,8 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add per-sample YOLO dataset audit diagnostics for parsed labels, median box area, small-box share and AOI-specific warning codes.
 - [x] Add deterministic visual YOLO label QA contact sheets before spending more CPU on another training run.
 - [x] Filter no-data/low-variance pure-empty negative tiles from operator YOLO exports before the next training run.
-- [ ] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
+- [x] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
+- [ ] Train one inactive candidate from the filtered AOI1024 cleanpx YOLO dataset and gate it through the positive-AOI plus split-background promotion workflow.
 - [ ] Apply promoted V1 default building detector only after explicit operator review of the emitted `.env` updates, followed by rebuild/restart and browser/runtime smoke.
 
 ## Sprint 8 status
diff --git a/docs/superpowers/plans/2026-07-11-yolo-low-variance-negative-filter.md b/docs/superpowers/plans/2026-07-11-yolo-low-variance-negative-filter.md
index e2b3e15e..3f7d89f2 100644
--- a/docs/superpowers/plans/2026-07-11-yolo-low-variance-negative-filter.md
+++ b/docs/superpowers/plans/2026-07-11-yolo-low-variance-negative-filter.md
@@ -51,6 +51,6 @@
 
 - [x] Run targeted pytest for the exporter/contact-sheet tests.
 - [x] Run `bash scripts/run_readiness_check.sh`.
-- [ ] Commit and push.
-- [ ] Rebuild/deploy Tower if code changed.
-- [ ] Regenerate the AOI1024 dataset with the new filter enabled, then render contact sheets and record the result.
+- [x] Commit and push.
+- [x] Rebuild/deploy Tower if code changed.
+- [x] Regenerate the AOI1024 dataset with the new filter enabled, then render contact sheets and record the result.