diff --git a/.gitattributes b/.gitattributes
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index 0000000000000000000000000000000000000000..bed0738c7eeb449bca98b5d2f33c89a1ee56349a
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
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+++ b/README.md
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+---
+license: cc-by-sa-4.0
+pretty_name: corrected-lds banks
+viewer: false
+tags:
+- training-data-attribution
+- linear-datamodeling-score
+- datamodels
+- trak
+---
+
+# corrected-lds banks
+
+Retrain banks for [`corrected-lds`](https://github.com/simonvary/corrected-lds), which
+evaluates training-data attribution (TDA) with a seed-noise-corrected Linear Datamodeling
+Score (LDS).
+
+- A **pool** holds retrain readouts, an R seeds × M masks × Q queries array: cell (r, j) is
+  the per-query margins of a model retrained on random subset j with seed r.
+- A **tauq** is one attribution method's n_train × Q score matrix, and a **pool_tda** holds
+  the same method one replica at a time.
+- A **bank** is one folder: a pool and the score files that pair with it.
+
+## Layout
+
+```
+cifar10/              CIFAR-10, ResNet-9: R=40 × M=300, Q=2,000, n=50,000
+├── pool.npz
+├── tauq_trak1_e{20,100}.npz, tauq_trak10_e{2,10}.npz        TRAK
+├── pool_tda_trak1.npz, pool_tda_trak10.npz, pool_tda_trak20.npz
+├── tauq_ekfac_e30.npz, pool_tda_ekfac.npz                   EK-FAC, 30 full-set models
+└── tauq_source_L3_e30.npz, pool_tda_source_L3.npz           SOURCE, the same 30 runs
+
+qnli/                 GLUE QNLI, BERT-base: R=20 × M=400, Q=2,000, n=50,000
+├── pool.npz
+├── tauq_trak1_e{10,20,50,100}.npz, tauq_trak5_e{2,4,10,20}.npz   TRAK, 100 members
+├── pool_tda_trak1.npz, pool_tda_trak5.npz
+├── tauq_ekfac_e20.npz, pool_tda_ekfac.npz                   EK-FAC, 20 full-set models
+└── tauq_tracin_e20.npz, pool_tda_tracin.npz                 TracIn, the same 20 runs
+
+mnist/                MNIST subset, MLP: R=40 × M=300, Q=2,000, n=6,144
+├── pool.npz
+└── tauq_trak1_e100.npz, tauq_ekfac_e30.npz, tauq_source_L3_e30.npz
+
+imagenet/             ImageNet-1k, ResNet-18: R=5 × M=100, Q=2,000, n=1,281,167
+└── pool.npz
+
+wikitext2_gpt2_bae/   WikiText-2, GPT-2: R=20 × M=400, Q=481, n=4,656 (a companion bank)
+├── pool.npz                                            eval-mode readout, the default
+├── pool_dropout_readout.npz                            the same models, dropout on
+├── tauq_ekfac_bergson.npz                              bergson's EK-FAC, one model: a check
+├── tauq_ekfac_e{5,10}.npz, pool_tda_ekfac.npz          our EK-FAC, 10 full-set runs: the main one
+├── tauq_tracin_e10.npz, pool_tda_tracin.npz            our TracIn, the same runs
+├── tauq_source_L3_e1.npz                               SOURCE (L=3, Adam), one model
+├── tauq_trak1_e{10,20,50}.npz, tauq_trak5_e{2,4,10}.npz     TRAK, 50 members
+└── pool_tda_trak1.npz, pool_tda_trak5.npz
+```
+
+## Names and protocols
+
+- `tauq_<method>_e<E>.npz` averages **E replicas**, independent estimates of the scores.
+  TRAK forms one replica from `e_features` members (one Eq. 15 unit), so `trak10_e2` is
+  2 × 10 = 20 trained models and `trak1_e100` is 100. SOURCE names its segment count
+  (`source_L3`).
+- `pool_tda_<method>.npz` keeps one slice per replica, so any smaller ensemble is an exact
+  average of slices: the whole ensemble-size curve without rescoring.
+- TRAK members train on random halves of the data, as TRAK intends. EK-FAC (damping 1e-8,
+  final checkpoint), TracIn and SOURCE (six checkpoints in L = 3 segments, Bae et al. 2024)
+  score models trained on the full set.
+- Recipes: cifar10 and qnli follow TRAK's App. A.1, mnist SOURCE's App. B.1, and imagenet
+  a 15-epoch ResNet-18. Every file stamps its recipe, data hashes, index draws and mask draw
+  inside, and the loaders refuse a pool and a tauq that do not pair.
+
+## The WikiText-2 companion bank
+
+Built with EleutherAI's [bergson](https://github.com/EleutherAI/bergson) (commit
+`0ad2e5b`) in the setup of Bae et al. 2024 (GPT-2 124M, AdamW at a constant 3e-5, batch 8,
+3 epochs, dropout on in training). Its numbers do not mix with the other banks':
+
+- A mask zeroes the excluded documents' loss weights rather than retraining on the subset,
+  and a replica's masks share one seed. The pool is stamped `shared-per-replica`, and
+  `evaluate` uses the matching seed-noise estimator.
+- `pool.npz` reads every model in eval mode, as bergson's published number does;
+  `pool_dropout_readout.npz` reads the same models with dropout on, as bergson's `validate`
+  loop logs them.
+- Two EK-FACs: ours, over 10 full-set runs, is the bank's EK-FAC; bergson's own, on one
+  model, checks it (they agree to within 0.001) and the replication: on Bae et al.'s
+  protocol (5 seeds, the first 100 masks) it scores 0.456 in mean per-query Spearman, against
+  0.468 ± 0.015 in bergson's example. Every file's numbers are in
+  `wikitext2_gpt2_bae/README.md`.
+
+## Use
+
+These are raw npz files, not a `load_dataset` dataset; read them with the corrected-lds
+tooling:
+
+```bash
+git clone https://github.com/simonvary/corrected-lds && cd corrected-lds
+pip install -e .
+corrected-lds fetch-banks              # every file checked against banks-manifest.json
+corrected-lds evaluate banks/cifar10 --tauq source_L3_e30
+```
+
+`banks-manifest.json` in the code repo pins every file's sha256 at one commit of this
+dataset. To score your own method, save its scores with `corrected_lds.write_tauq` and pass
+the file to `evaluate`.
+
+## Licence
+
+The banks are released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+They hold margins, masks, indices and scores only, no dataset content, but they derive from
+datasets with their own terms: QNLI (from SQuAD, CC BY-SA 4.0), WikiText-2 (CC BY-SA 3.0) and
+MNIST (CC BY-SA 3.0) are share-alike, and the ImageNet pool was built from the license-gated
+[`ILSVRC/imagenet-1k`](https://huggingface.co/datasets/ILSVRC/imagenet-1k), so its use is also
+subject to the ImageNet terms of access. The code is MIT-licensed.
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diff --git a/wikitext2_gpt2_bae/README.md b/wikitext2_gpt2_bae/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0c55ee2ed742f2375db086124449f3ecf24f1af6
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+# wikitext2_gpt2_bae
+
+A **companion** bank for [`corrected-lds`](https://github.com/simonvary/corrected-lds),
+built on EleutherAI **bergson**'s retrain harness rather than our own `pools/runner.py`.
+
+> **Read this first.** This bank reproduces the **Bae et al. 2024** GPT-2/WikiText-2
+> setup as packaged in bergson's `examples/replicate_bae_approx_unrolling_source/` —
+> **not** the experiment in bergson's own paper (arXiv:2606.11660, Table 2), whose
+> recipe and published EK-FAC LDS (0.318) differ.
+>
+> **It reproduces the published number.** On the default `pool.npz` (eval-mode readout),
+> bergson's EK-FAC scores **0.4562** over Bae's five seeds, against the example's
+> **0.468 ± 0.015**. The pool this bank shipped before (0.3506, a "24 SE gap") was read
+> with **dropout active** — that was the whole discrepancy. The same models' dropout
+> readout is kept, explicitly labelled, as `pool_dropout_readout.npz`.
+>
+> **2026-10-25: grown to 400 masks x 20 seeds** (from 100 x 10). The first 100 masks and
+> seeds 1004-1013 are the previous release's cells bit for bit; every pool_tda now covers
+> all 400 masks and equals the previous release on masks 0-99 (see PROVENANCE.md in the code repository). Numbers on
+> the new bank are in "Results on the 400 x 20 bank"; the tables computed on 100 x 10 are
+> kept and labelled as such.
+
+## Shape
+
+```
+wikitext2_gpt2_bae/
+├── pool.npz                  DEFAULT — eval-mode readout (dropout off)
+│                             R=20 seeds x M=400 masks x Q=481 queries, n_train=4,656
+├── pool_dropout_readout.npz  the SAME 8,000 models, read with dropout ON
+│                             (bergson's in-loop readout; stamped readout="dropout")
+├── tauq_ekfac_bergson.npz    bergson's EK-FAC, ONE full-data model (seed 1004), unprojected
+├── tauq_ekfac_e10.npz        our EK-FAC (kronfluence), 10 full-set runs, seeds 30000-30009
+├── tauq_ekfac_e5.npz         the first 5 of those runs (seeds 30000-30004)
+├── pool_tda_ekfac.npz        the 10 runs, replica-resolved (R_tda = 10; E=5 = the first 5)
+├── tauq_source_L3_e1.npz     SOURCE (L=3, Adam variant), ONE model: bergson's scorer at
+│                             the pin + the #511 fix, on our seed-30000 run
+├── tauq_tracin_e10.npz       our TracInCP over the same 10 runs x 6 half-epoch ckpts:
+│                             the 48 transformer Linears, a fresh 16,384-dim projection
+│                             per checkpoint
+├── pool_tda_tracin.npz       the 10 runs, replica-resolved (R_tda = 10)
+├── tauq_trak5_e{2,4,10}.npz  our TRAK, Eq. 15 units of 5: E = 2, 4, 10 (10/20/50 members)
+├── tauq_trak1_e{10,20,50}.npz  our TRAK, single members averaged: 10, 20, 50 members
+├── pool_tda_trak5.npz        the 50 members as 10 Eq. 15 units, replica-resolved
+└── pool_tda_trak1.npz        the 50 members one by one, replica-resolved (R_tda = 50)
+```
+
+Our EK-FAC and TracIn both trace the 48 transformer Linear layers (the tied `lm_head`/
+`wte`, `wpe` and LayerNorms excluded), as kronfluence and Bae et al.'s IF do. SOURCE is
+bergson's implementation (the pinned commit plus upstream fix #511, which restores the
+EK-FAC inverse in the Adam variant; without it SOURCE scored 0.13-0.17 here) at Bae et al.'s
+settings: 3 segments over the 6 half-epoch checkpoints, the Adam preconditioner, damping
+1e-8, on our seed-30000 run (disjoint from the ground-truth seeds).
+
+Our TRAK follows the protocol of this repo's qnli bank: members trained on their OWN
+random halves (seeds 10000-10049) -- here by bergson, each half saved as a dataset of its
+own (873 steps), not by weight-zeroing -- one featurize pass giving both `trak5` (Eq. 15
+over units of 5) and `trak1`.  It traces EVERY parameter, as qnli's TRAK does: on five
+members the 48 Linears scored 0.120 against 0.169 for all parameters (TRAK's kernel
+inverse whitens the tied embedding that swamps TracIn's dot product).  The model output
+is the sum of per-token log-odds (TRAK App. F.3) and `Q` the mean over tokens of
+`1 - p`; projection k = 2,048, the peak of a 512-4,096 sweep and the `k < n/2` guard.
+The members' eval-mode query loss (3.156-3.162) sits on the pool's half-data retrains
+(3.158).
+
+| field | value |
+|---|---|
+| model | GPT-2 (124M), fine-tuned from the base checkpoint per (mask, seed) |
+| data | `EleutherAI/bergson-wikitext-2-4656-chunks` @ `971da0e6da181b9e147081087f1d3fd51669024c` |
+| recipe | AdamW, lr 3e-5 constant, batch 8, 3 epochs (1,746 steps), wd 0.01, fp32, dropout ON in training |
+| alpha | **0.5** (masks keep a random half) |
+| seeds | 1004-1023. **Replicas 0-4 (seeds 1004-1008) are Bae's R=5 protocol exactly**; slice `margins[:5, :100]` to compare with published numbers |
+| masks | M=400 from `sample_subsets(400, 4656, 0.5, 777)` (prefix-stable: masks 0-99 are the original bank's) |
+| queries | the **full 481-doc validation split** — the battery the 0.468 averages over |
+| readout | per-query cross-entropy; `margins = -loss` (higher = better); raw `losses` ride along |
+| built with | bergson @ `0ad2e5b0ce09207d2af0e80383fe2d952dd8e747`, `nproc_per_node: 1`, `--save_models` |
+
+## Two readouts of the same models
+
+bergson's `validate` loop at the pinned commit never calls `.eval()` when `train_mode` is
+on, so the query losses it logs are read **with dropout active**. bergson's published
+0.468 re-reads saved models in eval mode. We retrained every (mask, seed) cell with
+bergson at the pinned commit, saved every model, and read each one ourselves in eval mode
+(`corrected-lds read-bergson-retrained`) — that is `pool.npz`. bergson's own in-loop
+readout of the same runs is `pool_dropout_readout.npz`, which also carries the per-replica
+full-data `baselines`.
+
+Dropout at evaluation is most of the seed noise of the in-loop readout (measured on the
+100 x 10 bank):
+
+| (mean over queries) | `pool.npz` (eval) | `pool_dropout_readout.npz` |
+|---|---|---|
+| seed-noise variance, total | 8.78e-05 | 6.99e-04 |
+| per-replica offset `c(r)` (cancels) | 2.31e-05 (26%) | 6.98e-05 (10%) |
+| residual (attenuates) | 6.53e-05 | 6.36e-04 |
+| lambda (median over queries) | **0.37** | **3.86** |
+
+The dropout pool reproduces the bank's original pool: for seeds 1004-1008 its cells on
+masks 0-12 match to ~4e-6, and its 5-seed EK-FAC LDS is 0.3499 vs the original 0.3506.
+The other cells are fresh dropout draws (bergson's dropout RNG carries across the models of
+one `validate` call, so only chunks aligned with the original run are bit-identical).
+
+## Results on the 400 x 20 bank (2026-10-25)
+
+Every score file on the default eval-readout `pool.npz`, R=20 seeds, M=400 masks
+(`corrected-lds evaluate`; jackknife SE in brackets; columns are the result JSON's
+`spearman`, `corrected_rho_inf`, `bridged_rho_inf`, `debiased_noise_corrected_spearman`):
+
+| tauq | naive Spearman | corrected rho_inf (SE) | bridged rho_inf | debiased + noise-corrected |
+|---|---|---|---|---|
+| `ekfac_bergson` (bergson, 1 model) | 0.4782 | 0.4938 (0.0022) | 0.4782 | 0.4836 |
+| `ekfac_e10` (ours, 10 models) | 0.4772 | 0.4931 (0.0022) | 0.4776 | 0.4825 |
+| `ekfac_e5` (ours, 5 models) | 0.4759 | 0.4918 (0.0022) | 0.4763 | 0.4813 |
+| `source_L3_e1` (bergson's, 1 model) | 0.4723 | 0.4887 (0.0022) | 0.4732 | 0.4776 |
+| `tracin_e10` (ours, 10 runs) | 0.1267 | 0.1321 (0.0028) | 0.1263 | 0.1282 |
+| `trak5_e10` (ours, 50 members) | 0.2274 | 0.2380 (0.0025) | 0.2280 | 0.2300 |
+| `trak1_e50` (ours, 50 members) | 0.2274 | 0.2380 (0.0025) | 0.2281 | 0.2301 |
+| `trak5_e4` (ours, 20 members) | 0.2079 | 0.2178 (0.0026) | 0.2086 | 0.2103 |
+| `trak1_e20` (ours, 20 members) | 0.2080 | 0.2179 (0.0026) | 0.2087 | 0.2103 |
+| `trak5_e2` (ours, 10 members) | 0.1856 | 0.1946 (0.0027) | 0.1863 | 0.1877 |
+| `trak1_e10` (ours, 10 members) | 0.1856 | 0.1946 (0.0027) | 0.1863 | 0.1877 |
+
+With four times the masks the jackknife SE roughly halves; the corrected values sit within
+about two of the 100 x 10 bank's SEs of the values below (e.g. `ekfac_e10` 0.4931 vs
+0.4842, `trak5_e10` 0.2380 vs 0.2397).
+
+## Results on the original 100 x 10 bank (masks 0-99, seeds 1004-1013 of these pools)
+
+Mean per-query Spearman across the 100 masks, averaged over the 481 queries (bergson's
+aggregation), for bergson's EK-FAC, by number of seeds averaged in the readout:
+
+| seeds | 1 | 2 | 3 | 4 | **5** | 6 | 7 | 8 | 9 | 10 |
+|---|---|---|---|---|---|---|---|---|---|---|
+| eval (`pool.npz`) | .4063 | .4354 | .4445 | .4512 | **.4562** | .4585 | .4609 | .4627 | .4632 | .4637 |
+| dropout | .2222 | .2789 | .3115 | .3357 | **.3499** | .3585 | .3696 | .3786 | .3856 | .3899 |
+
+Every score file, naive LDS vs the corrected estimates (`corrected-lds evaluate`;
+jackknife SE in brackets):
+
+| pool | R | tauq | naive | corrected rho_inf | bridged | debiased |
+|---|---|---|---|---|---|---|
+| eval | 10 | `ekfac_bergson` (bergson, 1 model) | 0.4637 | 0.4845 (.0047) | 0.4693 | 0.4763 |
+| eval | 10 | `ekfac_e10` (ours, 10 models) | 0.4633 | 0.4842 (.0046) | 0.4690 | 0.4758 |
+| eval | 10 | `ekfac_e5` (ours, 5 models) | 0.4627 | 0.4833 (.0047) | 0.4682 | 0.4752 |
+| eval | 10 | `source_L3_e1` (bergson's, 1 model) | 0.4622 | 0.4824 (.0047) | 0.4673 | 0.4747 |
+| eval | 10 | `tracin_e10` (ours, 10 runs) | 0.1194 | 0.1233 (.0056) | 0.1180 | 0.1227 |
+| eval | 10 | `trak5_e10` (ours, 50 members) | 0.2267 | 0.2397 (.0055) | 0.2298 | 0.2331 |
+| eval | 10 | `trak1_e50` (ours, 50 members) | 0.2267 | 0.2397 (.0055) | 0.2299 | 0.2331 |
+| eval | 10 | `trak5_e4` (ours, 20 members) | 0.2102 | 0.2227 (.0054) | 0.2135 | 0.2160 |
+| eval | 10 | `trak1_e20` (ours, 20 members) | 0.2102 | 0.2228 (.0054) | 0.2136 | 0.2161 |
+| eval | 10 | `trak5_e2` (ours, 10 members) | 0.1874 | 0.1973 (.0058) | 0.1890 | 0.1927 |
+| eval | 10 | `trak1_e10` (ours, 10 members) | 0.1872 | 0.1973 (.0058) | 0.1890 | 0.1924 |
+| eval | 5 | `ekfac_bergson` | **0.4562** | 0.4848 (.0051) | 0.4696 | 0.4764 |
+| eval | 5 | `ekfac_e10` | 0.4559 | 0.4846 (.0050) | 0.4694 | 0.4761 |
+| eval | 5 | `ekfac_e5` | 0.4553 | 0.4838 (.0050) | 0.4686 | 0.4755 |
+| eval | 5 | `source_L3_e1` | 0.4548 | 0.4827 (.0049) | 0.4676 | 0.4750 |
+| eval | 5 | `tracin_e10` | 0.1165 | 0.1221 (.0060) | 0.1169 | 0.1217 |
+| eval | 5 | `trak5_e10` | 0.2221 | 0.2388 (.0055) | 0.2290 | 0.2321 |
+| dropout | 10 | `ekfac_bergson` | 0.3899 | 0.4737 (.0057) | 0.4590 | 0.4633 |
+| dropout | 10 | `ekfac_e10` | 0.3904 | 0.4743 (.0056) | 0.4596 | 0.4636 |
+| dropout | 10 | `ekfac_e5` | 0.3896 | 0.4730 (.0056) | 0.4583 | 0.4627 |
+| dropout | 10 | `source_L3_e1` | 0.3869 | 0.4689 (.0057) | 0.4543 | 0.4591 |
+| dropout | 10 | `tracin_e10` | 0.1023 | 0.1246 (.0062) | 0.1193 | 0.1217 |
+| dropout | 10 | `trak5_e10` | 0.2049 | 0.2492 (.0065) | 0.2391 | 0.2442 |
+| dropout | 5 | `ekfac_bergson` | 0.3499 | 0.4860 (.0071) | 0.4717 | 0.4759 |
+| dropout | 5 | `ekfac_e10` | 0.3504 | 0.4867 (.0069) | 0.4724 | 0.4759 |
+| dropout | 5 | `source_L3_e1` | 0.3483 | 0.4813 (.0070) | 0.4672 | 0.4730 |
+| dropout | 5 | `tracin_e10` | 0.0909 | 0.1245 (.0078) | 0.1193 | 0.1226 |
+| dropout | 5 | `trak5_e10` | 0.1834 | 0.2574 (.0070) | 0.2473 | 0.2513 |
+
+The two readouts disagree by 0.07-0.11 in naive LDS and agree to ~0.01 once corrected:
+the dropout readout is the same signal under ten times the noise, and the correction
+recovers it.
+
+**Published references.** bergson's example README: EK-FAC IF **0.468 ± 0.015** (eval
+readout, five seeds), and "kronfluence reports 0.44". Bae et al. 2024 print no WikiText-2
+numbers; their Fig. 6 bars (alpha 0.5, R=5; read off the figure, ±0.005) are IF 0.43 (one
+model) / 0.445 (ten models), SOURCE 0.44 / 0.47, TracIn 0.10, RepSim 0.01.
+
+Our EK-FAC (kronfluence) matches bergson's to ≤0.001 on every pool. Its corrected LDS
+on the eval pool is flat in the number of runs averaged: 0.4687 (1 run), 0.4833 (5),
+0.4842 (10), so ten of our runs land level with bergson's single model (0.4845).
+
+**SOURCE (one model) scores 0.4548 on Bae's five seeds (0.4622 at R=10)**, against
+bergson's 0.475 ± 0.015 and Bae's ≈0.44. On the same model (seed 30000) it beats our
+EK-FAC by +0.0135 (paired SE 0.0018), Bae's single-model ordering (SOURCE 0.44 > IF 0.43),
+and it beats each of our ten single EK-FAC runs (0.447-0.453); one SOURCE model matches
+our ten-run EK-FAC (0.4633). It is level with bergson's single-model EK-FAC (0.4637,
+difference -0.0015 ± 0.0023), which scores above every one of our single EK-FAC runs.
+
+**Our TracIn scores 0.1165 on Bae's five seeds (0.1194 at R=10), against Bae's ≈0.10.**
+Like theirs it does not improve with more runs: corrected 0.1222 for one run, 0.1233 for
+ten. The TracIn this bank shipped before scored 0.055: it traced all 124M parameters,
+where the tied `wte`/`lm_head` carries ~91% of the gradient norm, and one 1,024-dim
+projection per run lost half of what remained.
+
+**Our TRAK scores 0.2221 on Bae's five seeds (0.2267 at R=10; corrected 0.2397) with 50
+members.** Bae et al. omit TRAK on WikiText ("no public LM implementation"), so there is
+no published reference.  Unlike EK-FAC and TracIn here it keeps rising with members --
+corrected 0.083 for one member, 0.169 for one unit of five, 0.223 at 20, 0.240 at 50 --
+and stays well below EK-FAC.  `trak5` and `trak1` agree to 1e-4 at equal members.
+
+## Two properties that make this bank unlike the others here
+
+**1. `seed_mode = "shared-per-replica"`.** bergson uses ONE seed per replica, shared
+across that replica's masks, where our other banks seed every *cell*. The readout
+decomposes as `y[r,j,q] = mu(S_j) + c(r) + eps(r,j)`, and the per-replica offset `c(r)`
+shifts a whole column, so it **cancels** in a correlation across masks and does not
+attenuate rho. `evaluate_pool` reads the stamp and uses
+`estimator.seed_noise_variance_centred` for such a pool (corrected-lds `main` since
+2026-10-06).
+
+**2. Masks are applied by zeroing loss weights, not by retraining on the subset.**
+Batching and data order are identical across masks — a *different counterfactual* from
+our CIFAR/QNLI/ImageNet banks. A companion measurement, not another entry in that series.
+
+## Loading
+
+Listed in corrected-lds's `banks-manifest.json`, which pins every file's sha256:
+
+```bash
+corrected-lds fetch-banks --bank wikitext2_gpt2_bae
+corrected-lds evaluate banks/wikitext2_gpt2_bae --tauq ekfac_e10                  # eval readout
+corrected-lds evaluate banks/wikitext2_gpt2_bae/pool_dropout_readout.npz --tauq ekfac_e10
+```
+
+```python
+from corrected_lds import load_pool, load_tauq
+from corrected_lds.estimator import seed_noise_variance_centred, seed_offset_variance
+
+pool = load_pool("wikitext2_gpt2_bae/pool.npz")     # eval readout, shared-per-replica
+tau  = load_tauq("wikitext2_gpt2_bae/tauq_ekfac_bergson.npz")
+
+resid  = seed_noise_variance_centred(pool.margins)  # the part that attenuates
+offset = seed_offset_variance(pool.margins)         # the part that does not
+```
+
+Identity is enforced on load: the stamped side digests must equal the `wikitext2` pins in
+`banks/presets.py`, which hash the int64 `input_ids` of the pinned dataset revision. The
+same revision is passed to bergson via `data.data_kwargs`, so both halves provably read
+identical bytes. Both pools carry the same masks and examples, so every tauq and pool_tda
+here pairs with either.
+
+## Build record
+
+How the bank was built, grown and checked is in `PROVENANCE.md` in the corrected-lds repository.
diff --git a/wikitext2_gpt2_bae/pool.npz b/wikitext2_gpt2_bae/pool.npz
new file mode 100644
index 0000000000000000000000000000000000000000..818371593f7b571039016bb27b31b6f961cf4061
--- /dev/null
+++ b/wikitext2_gpt2_bae/pool.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfc6bfb6d6b5d12473b6ae9dea5889e817e3a8360b4c62a77b52dff785d81923
+size 63478868
diff --git a/wikitext2_gpt2_bae/pool_dropout_readout.npz b/wikitext2_gpt2_bae/pool_dropout_readout.npz
new file mode 100644
index 0000000000000000000000000000000000000000..051ca3828725411de7f8cf1921c4b8d0150db5b6
--- /dev/null
+++ b/wikitext2_gpt2_bae/pool_dropout_readout.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f120391158f95530b4054b02e13a933602a85e5078818e25b2bc5e0339b21cb
+size 63556070
diff --git a/wikitext2_gpt2_bae/pool_tda_ekfac.npz b/wikitext2_gpt2_bae/pool_tda_ekfac.npz
new file mode 100644
index 0000000000000000000000000000000000000000..09fe566b3701efab3dc00c3f7a4066b92d959a86
--- /dev/null
+++ b/wikitext2_gpt2_bae/pool_tda_ekfac.npz
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