Card: E6 weights and the path D retrains (October 2026)

README.md

@@ -81,8 +81,9 @@ PyTorch payload: `torch.load(path, map_location="cpu", weights_only=False)` retu
 
 The trained models behind the October 2026 revision results of the benchmark code
 (`results/revision_october/`, frozen recipes in `configs/paper/october/recipes.yaml`): one entry for every
-trained run that has scored test views in those tables, 651 runs in the cohorts
-P4, P5, P6, P7, V4C, V4D, V4E, V4P6, V4P7, N3GJ2, N5MXR6, N6, N8, RPB and RPBn. Runs that failed during training (they are listed with their failure in the result tables) and
+trained run that has scored test views in those tables, 663 runs in the cohorts
+P4, P5, P6, P7, V4C, V4D, V4E, V4P6, V4P7, N3GJ2, N5MXR6, N6, N8, RPB, RPBn and E6 (the 12 E6 runs, nearest-fill
+copies of zero-fill FNO runs, were added on 2026-10-21 as upload part 3). Runs that failed during training (they are listed with their failure in the result tables) and
 the recipe-screen runs have no model here. The training and test data are the V2 views in the companion
 dataset, under `october2026/`.
 
@@ -105,10 +106,11 @@ result tables.
   the file before the weights were extracted. `torch.load(path, map_location="cpu", weights_only=True)["model_state"]`
   loads it.
 - **Rescoring.** For the cohorts with a frozen recipe in `configs/paper/october/recipes.yaml` (P4, P5, V4C, V4D,
-  N3GJ2, N5MXR6, N6, RPB, RPBn), `reproduce/october/replay_inference.py --run <run_id> --checkpoint weights.pt
+  N3GJ2, N5MXR6, N6, RPB, RPBn, E6), `reproduce/october/replay_inference.py --run <run_id> --checkpoint weights.pt
   --data-root <download>/october2026/v2` rebuilds the network, re-predicts the run's test views and compares each
   view mean with the stored score (relative tolerance 1e-4; recorded checks on CUDA and CPU are in
-  `reproduce/october/README.md`). V4E is listed in `recipes.yaml` as four information cells that the replay script does not rebuild; P6, P7, V4P6, V4P7 and N8 have no entry (they reuse the P4 or V4C recipe of their slice with one change: training-set size, noise, input fill, mixture or grid). The FNOs were
+  `reproduce/october/README.md`; an E6 run, Poisson rough G256 seed 1101, replayed on a CPU matches its stored
+  scores to 2e-7). V4E is listed in `recipes.yaml` as four information cells that the replay script does not rebuild; P6, P7, V4P6, V4P7 and N8 have no entry (they reuse the P4 or V4C recipe of their slice with one change: training-set size, noise, input fill, mixture or grid). The FNOs were
   trained and scored with cuFFT; on a CPU the script reproduces cuFFT's large-batch inverse real FFT
   (`pdeobs.revision.fftcompat`).
 - **Checked after upload.** Every `weights.pt` on the Hub has the SHA-256 listed in its manifest part (checked
@@ -117,6 +119,33 @@ result tables.
   (Raonic et al.; Zenodo record 10406879, CC BY 4.0). That data is not redistributed here; to rescore these
   models, download it from Zenodo and convert it with `python -m pdeobs.revision.external.rpb`.
 
+## Path D retrains (`october2026/retrains/`)
+
+Ten retrains of Poisson medium U256 runs from the public package with `reproduce/october/train_run.py` of the benchmark
+code (reproduction path D, 2026-10-18 to 2026-10-21): P4 FNO seed 1102, P4 Voronoi-CNN seeds 1101-1103, V4C FNO seeds
+1101-1103 and P4n FNO seeds 1101-1103. P4n is the P4 recipe with `--set v4_norm=train_scalar`: the training-set normaliser
+is its only change. The P4 FNO retrains of seeds 1101 and 1103 collapsed to a constant output, as the original runs did,
+and have no model.
+
+```
+october2026/retrains/<name>/
+    weights.pt     same format as the October models
+    model.json     recipe run, --set overrides, final training loss, normaliser, the seven v2eval7 view means of the
+                   retrain, original and released SHA-256
+    history.json   per-epoch training history (numeric fields only)
+october2026/retrains/manifest.json   the model.json records, and the two retrains without a model
+```
+
+These are retrains, not the campaign's models. Retraining is not bit-identical, so their scores differ from the stored
+scores of the original runs; results and discussion are in `reproduce/october/README.md` of the benchmark code. Rescore a
+retrain with its recipe run and the same `--set` (on a CPU, the P4n seed 1101 replay reproduces the view means in its
+`model.json` to 9e-7):
+
+```bash
+python reproduce/october/replay_inference.py --run P4__poisson__ell009__fno__U256__s1101 --set v4_norm=train_scalar \
+    --checkpoint october2026/retrains/P4n__poisson__ell009__fno__U256__s1101/weights.pt --data-root <download>/october2026/v2
+```
+
 ## Citation
 
 Ruichen Xu, Siyao Wang, Fang Wan, Jiacheng Qiu, Wenhan Gao, Jiaxing Zhang, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun and