diff --git a/cct-paper-v7-sv1/README.md b/cct-paper-v7-sv1/README.md
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----
-license: cc-by-4.0
-tags:
-- transformers
-- attention-topology
-- text-classification
-- uncertainty-estimation
-pretty_name: CCT paper — Single-v1 attention-topology features
----
-
-# `cct-paper-v7-sv1` — Single-v1 attention-topology features
-
-Pre-extracted 14,604-scalar Single-v1 attention-topology / hidden-state
-features for all 11 paper configurations from the EMNLP 2026 submission
-*"Class-Centroid Trajectories for Admissible Misclassification Detection
-in Fine-Tuned Transformer Classifiers."*
-
-Use these to **re-train the SV1 or SV1+CCT CatBoost detector from
-scratch without re-running feature extraction**. See
-[`cct-paper-v7-models`](../cct-paper-v7-models) for the underlying
-encoder weights, and [`cct-paper-v7`](../cct-paper-v7) for the lean
-verification pack (paper PDF, smoke tests, paper-result CSVs).
-
-## Layout
-
-```
-cct-paper-v7-sv1/{cfg}/{family}_{split}.npz
-```
-
-Per cfg, 10 sub-families × 3 splits = 30 npz files:
-
-| Sub-family | Dim (roberta-base) | Description |
-|---|---:|---|
-| `ripser`        | 2,016 | per-head H₀/H₁ barcodes of attention |
-| `template`      | 1,008 | per-head template-attention summaries |
-| `graph`         | 7,776 | per-head attention-graph topology |
-| `toktopo_pd`    |   120 | per-layer token-graph persistence diagrams |
-| `toktopo_graph` |    84 | per-layer token-graph structure |
-| `intra_attn`    | 1,152 | intra-attention spectral statistics |
-| `punct`         |   148 | punctuation-token Frobenius distances |
-| `cls_last`      |   768 | raw `[CLS]` at layer 12 |
-| `cls_mid`       |   768 | raw `[CLS]` at layer 6 |
-| `cls_begin`     |   768 | raw `[CLS]` at layer 0 |
-| **Total**       | **14,604** | |
-
-Sizes vary with sample count: ~230 MB per electra cfg, ~250-500 MB per
-roberta cfg (largest is roberta/yelp at 1.9 GB due to N=30k test).
-Total ~5.2 GB.
-
-## NPZ schema
-
-Each `{family}_{split}.npz`:
-
-| Key | Shape | Dtype | Description |
-|---|---|---|---|
-| `features` | `(N, D)` | `float32` | The D scalars from this family per sample |
-| `columns`  | `(D,)`  | `<U…`    | Feature names |
-| `idx`      | `(N,)`  | `int64`  | Row index in the split |
-
-## How to retrain SV1+CCT from cached features
-
-```python
-import numpy as np
-
-CFG = "roberta_newsgroups"
-SV1_FAMS = ["ripser","template","graph","toktopo_pd","toktopo_graph",
-            "intra_attn","punct","cls_last","cls_mid","cls_begin"]
-
-def load_sv1(cfg, split):
-    Xs = [np.load(f"cct-paper-v7-sv1/{cfg}/{f}_{split}.npz")["features"]
-          for f in SV1_FAMS]
-    return np.concatenate(Xs, axis=1).astype(np.float32)
-
-X_tr = load_sv1(CFG, "train")
-# Combine with CCT-25 from cct-paper-v7/features/{cfg}/cct_train.npz
-# to get the full SV1+CCT 14,629-scalar input.
-```
-
-The full SV1+CCT trainer (per-family CatBoost-importance budget cutter
-→ Protocol H CatBoost) is shipped in `cct-paper-v7/repo/cct_reproduce`.
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