Add the one- and two-query towers (campaign 3) and the five-fold eight-query towers

Six campaign-3 checkpoints (slot1i2cemean_icetf and slot2i2cemean_icetf, folds 0 to 2, _d512) listed in c3_docfix512_ckpt_plan.json; the five five-fold slot8i2cemean_icetf checkpoints at _g4096, at the rank rule's picks, listed in q8_g4096_ckpt_plan.json; README sections for both and the SetPoolN(Q) note.

README.md

@@ -20,8 +20,9 @@ The data folder the towers read is the dataset
 A file is named `ckpt_w9cv_wcle_<arm>_fold<k>[_g<cap>][_<suffix>]_fp_ep<N>.pt`: the training
 arm, the fold of the five-fold split, the anchor budget it trained with (none = 512), and the
 epoch. Each file is a PyTorch state dict of `Pod/w9_cv_worker.py`'s `SetPoolN(4)` (keys `q0`,
-`attn.*`, `head.*`); the VICReg runs hold `{"model": ..., "expander": ...}`. Only tensors, no
-code objects, so `torch.load(path, weights_only=True)` reads them.
+`attn.*`, `head.*`), or of `SetPoolN(Q)` for the `slot<Q>i2cemean_icetf` arms, whose `q0` has shape
+[1, Q, 128]; the VICReg runs hold `{"model": ..., "expander": ...}`. Only tensors, no code objects,
+so `torch.load(path, weights_only=True)` reads them.
 
 Score them against the manuscript's ten 4,096-sentence anchor draws (from the repository root):
 
@@ -95,3 +96,24 @@ packs were redone. Selected Stripped hit@1 on the ten 4,096-sentence draws (five
 | τ = 0.05 | 0.601 ± 0.029 | 0.642 ± 0.026 |
 | τ = 0.10 | 0.510 ± 0.022 (four folds) | 0.520 ± 0.015 |
 
+## One and two latent queries (campaign 3), added 2026-11-10
+
+The latent-query sweep's one- and two-query towers (`slot1i2cemean_icetf`, `slot2i2cemean_icetf`),
+folds 0 to 2, trained on the corrected packs under campaign 2's recipe and suffix: six runs, the
+selected checkpoint of each listed in `c3_docfix512_ckpt_plan.json` (campaign 2's entry format).
+With the four-query I-CE of `docfix512_ckpt_plan.json` and campaign 2's 8 and 16 queries they give
+the sweep's five query counts at 512.
+
+## Eight latent queries, five folds (`_g4096`), added 2026-11-10
+
+The five-fold eight-query tower (`slot8i2cemean_icetf`), trained at the anchor cap of the four-query
+I-CE runs it is compared with (`_g4096`). Its packs were built from the 2,048-sentence review pool
+(the training logs show `anchors: used med 2068`), so the 512-sentence correction does not touch it.
+The worker's own pick (`best_ep` in its `zsbest` record) came from a different selection rule; the
+five checkpoints in `q8_g4096_ckpt_plan.json` are the manuscript's rank rule applied to the towers
+the worker saved with every checkpoint, the procedure that reproduces `rank_ckpt_plan.json`'s picks
+for the four-query runs. Campaign 2's entry format, plus `cap`.
+
+`k10_eval.py`'s `load_tower` builds `SetPoolN(4)`; to score a `slot<Q>` file, build `SetPoolN(Q)`
+from the shape of its `q0` instead.
+

c3_docfix512_ckpt_plan.json

@@ -0,0 +1,68 @@
+{
+ "slot1i2cemean_icetf_fold0_d512": {
+  "arm": "slot1i2cemean_icetf",
+  "fold": 0,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1850,
+  "ckpt": "ckpt_w9cv_wcle_slot1i2cemean_icetf_fold0_d512_fp_ep1850.pt",
+  "sha256": "7a0c9fa191f2277833a076d6260c998c5cc439322ed956785e69db2561fca22e",
+  "ranksel": 102.129047782017
+ },
+ "slot1i2cemean_icetf_fold1_d512": {
+  "arm": "slot1i2cemean_icetf",
+  "fold": 1,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1850,
+  "ckpt": "ckpt_w9cv_wcle_slot1i2cemean_icetf_fold1_d512_fp_ep1850.pt",
+  "sha256": "cc6a92592ac81d681637c390e94dba57f5a1e0fb28b4cd295a6b743bf8f2c16e",
+  "ranksel": 98.1664812043291
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+ "slot1i2cemean_icetf_fold2_d512": {
+  "arm": "slot1i2cemean_icetf",
+  "fold": 2,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1750,
+  "ckpt": "ckpt_w9cv_wcle_slot1i2cemean_icetf_fold2_d512_fp_ep1750.pt",
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+  "ranksel": 103.26830974538323
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+ "slot2i2cemean_icetf_fold0_d512": {
+  "arm": "slot2i2cemean_icetf",
+  "fold": 0,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 2000,
+  "ckpt": "ckpt_w9cv_wcle_slot2i2cemean_icetf_fold0_d512_fp_ep2000.pt",
+  "sha256": "2beba6ddd569fa1d31f645c9c9da2f732b94bd902c40461946a392ae6e640f43",
+  "ranksel": 101.4992104596883
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+ "slot2i2cemean_icetf_fold1_d512": {
+  "arm": "slot2i2cemean_icetf",
+  "fold": 1,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 900,
+  "ckpt": "ckpt_w9cv_wcle_slot2i2cemean_icetf_fold1_d512_fp_ep900.pt",
+  "sha256": "cca438d55807af9e124c9ade4ca15b54054cf1ce1226f861e33c044abf8073a9",
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+ "slot2i2cemean_icetf_fold2_d512": {
+  "arm": "slot2i2cemean_icetf",
+  "fold": 2,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 2000,
+  "ckpt": "ckpt_w9cv_wcle_slot2i2cemean_icetf_fold2_d512_fp_ep2000.pt",
+  "sha256": "7fe02695f5ac123273017d39cfd70ac9bc5795f028235c2397bf5fb1d850255c",
+  "ranksel": 103.84762984590806
+ }
+}

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q8_g4096_ckpt_plan.json

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