Add reproducible watermark cohort, timing, and reports

scripts/README.md

@@ -26,6 +26,7 @@ explicitly names a tracked canonical result.
 | `visible_eval.py` | Benchmark registered visible-mark detectors. |
 | `visible_removal_audit.py` | Audit visible-removal results over a local corpus. |
 | `watermark_benchmark.py` | Run hash-pinned image cases while keeping detection, removal observation, and fidelity separate. |
+| `watermark_benchmark_report.py` | Aggregate repeated benchmark results with artifact-aware counts and separate cold/warm timing. |
 
 ## Calibration and corpus preparation
 
@@ -44,6 +45,7 @@ explicitly names a tracked canonical result.
 | `visible_positives.py` | List corpus images carrying a registered visible mark. |
 | `visible_recall_sample.py` | Build an unbiased blinded sample for recall measurement. |
 | `visible_sheets.py` | Build blinded contact sheets for relaxation candidates. |
+| `watermark_benchmark_cohort.py` | Build the deterministic synthetic DWT-DCT and TrustMark cohort consumed by `watermark_benchmark.py`. |
 | `retrain_photo_classify.py` | CPU-retrain the 2026-08-31 photo heads from a sha256-keyed cache pack, no images. |
 | `publish_photo_classify_hf.py` | Upload the photo-classify card and freeze weights to Hugging Face `wiltodelta/raiw-photo-classify`. Manual; the Action `publish-photo-classify-hf.yml` is the write-token path. |
 | `verify_engine_selection_fixtures.py` | Verify hashes, dimensions, and prompt pairing in the tracked auto-engine content matrix. |