Update title, drop pre_registration artifacts

LOAD_EXAMPLE.py

@@ -23,5 +23,5 @@ print(f"Atom F412: shape {tuple(atom.shape)}, ||F = {atom.norm():.4f}")
 
 # 3. To run the actual substitution test (atom replaces native cache write at one
 #    firing position, measure forward KL), see scripts/clean_amplified_kl.py in the
-#    code repo: https://anonymous.4open.science/r/matrix-sae
+#    code repo: https://anonymous.4open.science/r/WriteSAE-6158
 print("\nNext: clone the code repo and run scripts/clean_amplified_kl.py --feature 412")

MODEL_CARD.md

@@ -2,7 +2,7 @@
 
 ## Artifact
 
-Sparse autoencoders trained on cached Gated DeltaNet write activations and matching architectures, accompanying the paper *WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes* (NeurIPS 2026 submission).
+Sparse autoencoders trained on cached Gated DeltaNet write activations and matching architectures, accompanying the paper *WriteSAE: Sparse Autoencoders for Recurrent State* (NeurIPS 2026 submission).
 
 ## Intended use
 
@@ -62,7 +62,7 @@ Code and SAE checkpoints: MIT. Base models retain their upstream licenses.
 
 ```bibtex
 @inproceedings{anon2026writesae,
-  title  = {WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes},
+  title  = {WriteSAE: Sparse Autoencoders for Recurrent State},
   author = {Anonymous},
   booktitle = {Submitted to NeurIPS 2026},
   year   = {2026}

README.md

@@ -10,9 +10,9 @@ tags:
 
 # WriteSAE Checkpoints
 
-Sparse autoencoders trained on recurrent state writes. This repository contains the checkpoints and result files for *WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes*, an anonymous NeurIPS 2026 submission.
+Sparse autoencoders trained on recurrent state writes. This repository contains the checkpoints and result files for *WriteSAE: Sparse Autoencoders for Recurrent State*, an anonymous NeurIPS 2026 submission.
 
-The main artifact is a WriteSAE trained on Qwen3.5-0.8B, layer 9, head 4. The repo also includes cross-layer, cross-scale, and cross-architecture checkpoints plus JSON/NPZ files for the paper's reported tests.
+The main artifact is a WriteSAE trained on Qwen3.5-0.8B, layer 9, head 4. The repo also includes cross-layer, cross-scale, and cross-architecture checkpoints plus JSON files for the paper's reported tests.
 
 ## What is here
 
@@ -21,10 +21,9 @@ The main artifact is a WriteSAE trained on Qwen3.5-0.8B, layer 9, head 4. The re
 | WriteSAE checkpoints | [`writesae/`](writesae) | Primary rank-1 decoder SAEs. |
 | FlatSAE + SVD baselines | [`flat_baseline/`](flat_baseline) | Baselines for cited comparison cells. |
 | Result files | [`results/`](results) | JSON outputs grouped by paper claim. |
-| Pre-registration artifacts | [`pre_registration/`](pre_registration) | 192 NPZ prediction shards from Stage 2. |
 | Manifest | [`manifest.json`](manifest.json) | SHA256 hashes, sizes, and descriptions. |
 | Loading example | [`LOAD_EXAMPLE.py`](LOAD_EXAMPLE.py) | Minimal checkpoint-loading script. |
-| Reproducibility card | [`MODEL_CARD.md`](MODEL_CARD.md) | NeurIPS checklist item 13. |
+| Reproducibility card | [`MODEL_CARD.md`](MODEL_CARD.md) | Reproducibility metadata. |
 
 This repository does not include base model weights. Load those from their upstream Hugging Face repositories.
 
@@ -93,10 +92,6 @@ matrix-sae-ckpts/
     all_16_heads_L9_representativeness/
     falsification/
     ablations/
-
-  pre_registration/
-    README.md
-    predictions/
 ```
 
 ## Included model families
@@ -130,8 +125,8 @@ matrix-sae-ckpts/
 The code is in the anonymous source repository, separate from this checkpoint repository:
 
 ```bash
-git clone https://anonymous.4open.science/r/matrix-sae
-cd matrix-sae
+git clone https://anonymous.4open.science/r/WriteSAE-6158
+cd WriteSAE-6158
 pip install -e .
 python scripts/clean_amplified_kl.py   --feature 412   --sae-checkpoint <hf-cache>/writesae/qwen0p8b/L9_H4/best.pt   --states-dir <local>/states/Qwen3.5-0.8B/L9   --out out/headline.json
 ```
@@ -167,7 +162,7 @@ Do not use these files as production model-editing tools or as safety interventi
 
 ```bibtex
 @inproceedings{anon2026writesae,
-  title     = {WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes},
+  title     = {WriteSAE: Sparse Autoencoders for Recurrent State},
   author    = {Anonymous},
   booktitle = {Submitted to NeurIPS 2026},
   year      = {2026}

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pre_registration/README.md

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-# Pre-registration — Stage 2 prediction pass
-
-This directory holds the 192 NPZ shards produced by the pre-registered Stage 2 prediction pass for the closed-form three-factor logit law.
-
-## Background
-
-The pre-registration document (`paper-9pager/pre_registration.md` in the code repo) was filed on 2026-04-17 with seven R²-based hypotheses at Qwen3.5-0.8B Gated DeltaNet L1, L9, L17. The Stage 2 pass produced predictions WITHOUT yet running the observed comparison; the Stage 3 observed pass populates via `scripts/slotfill.py --experiment exp_c_observe`.
-
-## Files
-
-192 `.npz` shards named `L<layer>_H<head>_<sae_kind>_seed2026_predict.npz`, where:
-
-- `<layer>` ∈ {1, 9, 17}
-- `<head>` ∈ {0, ..., 15}
-- `<sae_kind>` ∈ {bilinear, flat_svd, random, orthogonal}
-
-Each shard contains the closed-form prediction `Δℓ ≈ G · <v_i, q_t> · <v_i, W_U[tok]>` evaluated under one (layer, head, SAE-kind) configuration, before the corresponding observed run.
-
-## Audit
-
-The pre-registration audit script (`scripts/prereg_r2_computation.py` in the code repo) walks these shards plus the Stage 3 observed shards and computes per-hypothesis R² with the falsification thresholds named in the pre-reg document. The audit confirms which hypotheses were corroborated, which fell within the predicted falsification region, and which were inconclusive.

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