Initial public release

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow 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
+*.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
+results/falsification/rank2_top1_matched/rank2_vs_rank1_kl_L9_H4.json filter=lfs diff=lfs merge=lfs -text
+results/falsification/rank2_top1_matched/rank2_vs_rank1_kl_L9_H4.progress.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2026 Jack Young
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LOAD_EXAMPLE.py

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+#!/usr/bin/env python3
+"""Self-contained 30-line example: load WriteSAE atom and run substitution at the cache slot.
+
+Reproduces the paper's headline 92.4% number on a single firing of feature 412.
+"""
+import torch
+import torch.nn.functional as F
+from huggingface_hub import snapshot_download
+
+# 1. Download the primary cell SAE (5 MB).
+ckpt_dir = snapshot_download(
+    "JackYoung27/writesae-ckpts",
+    allow_patterns=["writesae/qwen0p8b/L9_H4/*"],
+)
+ckpt = torch.load(f"{ckpt_dir}/writesae/qwen0p8b/L9_H4/best.pt",
+                  weights_only=False, map_location="cpu")
+
+# 2. Pick atom F412 (paper ERASE exemplar). Atom is rank-1 outer product v_i w_i^T.
+v_412 = ckpt["sae"].decoder.v[412]
+w_412 = ckpt["sae"].decoder.w[412]
+atom = torch.outer(v_412, w_412)
+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/WriteSAE-6158
+print("\nNext: clone the code repo and run scripts/clean_amplified_kl.py --feature 412")

MODEL_CARD.md

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+# WriteSAE checkpoints — model card
+
+## Artifact
+
+Sparse autoencoders trained on cached GatedDeltaNet write activations, accompanying the paper *WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes*.
+
+- HF repo: [JackYoung27/writesae-ckpts](https://huggingface.co/JackYoung27/writesae-ckpts)
+- Code: [https://github.com/JackYoung27/writesae](https://github.com/JackYoung27/writesae)
+- Paper: see `paper/` in the code repo
+
+## Variants released
+
+| variant | encoder | decoder | downstream PPL Δ (L9 H4, 0.8B) |
+| --- | --- | --- | ---: |
+| WriteSAE (primary) | bilinear $v_i^\top S w_i$ | rank-1 $v_i w_i^\top$ | +0.58% |
+| FlatSAE | linear on vec($S$) | flat | +3.31% |
+| MatrixSAE | linear on vec($S$) | rank-1 | +11.18% |
+| BilinearSAE | bilinear | bilinear | +1.33% |
+
+Each variant carries TopK sparsity (per-variant *k*; BatchTopK supported via `--use_batchtopk`).
+
+## Base models
+
+- Qwen/Qwen3.5-0.8B (primary, all main-text results)
+- Qwen/Qwen3.5-4B (cross-scale replication)
+- Qwen/Qwen3.5-27B (cross-scale replication)
+- fla-hub/delta_net-1.3B-100B, fla-hub/gla-1.3B-100B (cross-arch)
+- state-spaces/mamba-2.8b (cross-arch, no SAE training; spectral audit only)
+
+## Layer / head coverage
+
+- L9 H4 — primary substitution site (0.8B)
+- L1 H4, L17 H4 — cross-layer firing-ordering experiments
+- Full L9, all 16 heads — bestiary distribution and selectivity sweep
+- 47 cells × 8 features — uniqueness controls
+
+## Training
+
+- Architecture: rank-1 decoder atoms $v_i w_i^\top$, bilinear encoder
+- Dictionary size: 16,384 features (`--n_features`; expansion factor configurable)
+- Sparsity: TopK (per-variant *k*)
+- Data: OpenWebText (`Skylion007/openwebtext`, streaming) tokenized with the Qwen3.5 tokenizer; states extracted via `experiments/extraction/extract_states.py`
+- Split: 80/20 train/val, seed 42 (deterministic)
+- Compute: ~200 A100-hours total across all variants
+
+## Evaluation
+
+- Substitution: forward KL at firing positions under three matched-Frobenius-norm conditions (atom, ablation, random rank-1). Pooled *n*=4,851 firings (L1 1,500 + L9 1,851 + L17 1,500) at Qwen3.5-0.8B L9 H4.
+- Closed-form factorization: per-firing logit shift predicted at median *R²*=0.98 across 200 atom×ε cells.
+- Steering: 30 prompts × 5 install positions × 8 target tokens × 3 magnitudes = 3,600 trials.
+
+## Files on the HF repo
+
+```
+JackYoung27/writesae-ckpts/
+  writesae_L{1,9,17}_H{0..15}/                # WriteSAE checkpoints
+  flatsae_L{1,9,17}_H{0..15}/                 # FlatSAE controls
+  matrixsae_L{1,9,17}_H{0..15}/               # MatrixSAE controls
+  bilinearsae_L{1,9,17}_H{0..15}/             # BilinearSAE controls
+  exp_c_full_seed2026/exp_c/predictions/      # 192 prediction NPZ shards
+  memory_edit_L9_H4/                          # F412 ERASE records
+  behavioral_steering_L9_H4/                  # INSTALL records
+  flat_sae_svd_{gdn,mamba2,rwkv7}/            # Cross-arch SVD
+  manifest.json                               # tag → SHA256 + metadata
+```
+
+## Loading
+
+```python
+import torch
+ckpt = torch.load("writesae_L9_H4_nf2048_k32_s42/best.pt", weights_only=False, map_location="cpu")
+print(ckpt["config"], ckpt["val_mse"])
+```
+
+## License
+
+- Code and SAE checkpoints: MIT
+- Base models retain the upstream Tongyi Qianwen license (Qwen3.5) and the licenses of the FLA models (DeltaNet, GLA) and Mamba-2.
+- We do not redistribute base-model weights; all base models are loaded from their public HF release at runtime.
+
+## Citation
+
+```bibtex
+@article{young2026writesae,
+  title  = {WriteSAE: Sparse Atoms that Substitute for Recurrent State Writes},
+  author = {Jack Young},
+  year   = {2026},
+  journal= {arXiv preprint},
+  url    = {https://github.com/JackYoung27/writesae},
+}
+```

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - sparse-autoencoder
+  - interpretability
+  - mechanistic-interpretability
+  - gated-deltanet
+  - mamba
+  - rwkv
+  - linear-attention
+  - state-space-model
+base_model:
+  - Qwen/Qwen3.5-0.8B
+  - Qwen/Qwen3.5-4B
+  - Qwen/Qwen3.5-27B
+language:
+  - en
+pipeline_tag: feature-extraction
+---
+
+# WriteSAE
+
+**WriteSAE: Sparse Autoencoders for Recurrent State**
+
+Jack Young
+
+[Paper](https://arxiv.org/abs/2605.12770) | [Website](https://www.jackyoung.io/research/writesae) | [Code](https://github.com/JackYoung27/writesae)
+
+WriteSAE factors each decoder atom as the rank-1 outer product **vᵢwᵢᵀ**, matching the native **kₜvₜᵀ** write that Gated DeltaNet, Mamba-2, and RWKV-7 install into a **dₖ × dᵥ** matrix cache. Residual SAEs cannot reach that write site; WriteSAE can. Atom substitution beats matched-Frobenius-norm ablation on **92.4%** of *n*=4,851 firings at Qwen3.5-0.8B L9 H4, the closed form predicts measured logit shifts at **R² = 0.98**, and sustained three-position installs lift midrank target-in-continuation from 33.3% to **100%** under greedy decoding. Cross-architecture: GDN rank-1 atoms transfer to Mamba-2-370M at 88.1% over 2,500 firings, with sharpness ordering GDN > RWKV-7 > Mamba-2.
+
+## Quick start
+
+```python
+from huggingface_hub import snapshot_download
+import torch
+
+ckpt_dir = snapshot_download(
+    "JackYoung27/writesae-ckpts",
+    allow_patterns=["writesae/qwen0p8b/L9_H4/*"],
+)
+
+ckpt = torch.load(
+    f"{ckpt_dir}/writesae/qwen0p8b/L9_H4/best.pt",
+    weights_only=False,
+    map_location="cpu",
+)
+
+# Decoder atom 412 — the paper's ERASE example.
+v_412 = ckpt["sae"].decoder.v[412]   # (d_k,)
+w_412 = ckpt["sae"].decoder.w[412]   # (d_v,)
+atom = torch.outer(v_412, w_412)      # (d_k, d_v)
+```
+
+Standalone runnable in [`LOAD_EXAMPLE.py`](LOAD_EXAMPLE.py).
+
+## Variants
+
+| variant | encoder | decoder | role |
+|---|---|---|---|
+| **WriteSAE** | bilinear vᵢᵀ S wᵢ | rank-1 vᵢwᵢᵀ | All headline numbers |
+| FlatSAE | linear on vec(S) | flat | Architectural-prior comparison |
+| MatrixSAE | linear on vec(S) | full-rank | Ablation |
+| BilinearSAE | bilinear | bilinear | Ablation |
+
+## Base models covered
+
+Qwen3.5-0.8B (primary), Qwen3.5-4B, Qwen3.5-27B, Mamba-2-370M, RWKV-7-1.5B, DeltaNet-1.3B, GLA-1.3B. See [`MODEL_CARD.md`](MODEL_CARD.md) for full layer / head coverage and training details.
+
+## Repository layout
+
+```text
+writesae-ckpts/
+  README.md
+  MODEL_CARD.md
+  manifest.json
+  LOAD_EXAMPLE.py
+  LICENSE
+
+  writesae/<base-model>/<layer>_<head>/best.pt        # primary cells
+  flat_baseline/<base-model>_<layer>_<head>/best.pt   # FlatSAE controls
+  results/<test-name>/                                # JSON outputs per paper claim
+```
+
+## Limitations
+
+The closed-form factorization predicts well only on Gated DeltaNet (R² = 0.98 at L9 H4); applied to Mamba-2 or Qwen3.5-4B, it returns negative R². The substitution test itself transfers to Mamba-2 (88.1%); the analytical coefficient does not. Per-atom identity varies across SAE seeds; the class-level register / bundle partition reproduces at CV 4–12%.
+
+## Citation
+
+```bibtex
+@article{young2026writesae,
+  title  = {WriteSAE: Sparse Autoencoders for Recurrent State},
+  author = {Young, Jack},
+  year   = {2026},
+  journal= {arXiv preprint arXiv:2605.12770},
+  url    = {https://github.com/JackYoung27/writesae}
+}
+```
+
+MIT license. Base models retain their upstream licenses; no base-model weights are redistributed.

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+  "n_params": 33564672,
+  "device": "cuda",
+  "code_sha": "249cf01"
+}

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+}

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results/92pct_substitution/forward_kl_4way_all_layers.json

@@ -0,0 +1,558 @@
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results/all_16_heads_L9_representativeness/forward_kl_4way_L9_H10.json

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results/all_16_heads_L9_representativeness/forward_kl_4way_L9_H12.json

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results/all_16_heads_L9_representativeness/forward_kl_4way_all_layers.json

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+}

results/behavioral_steering_100pct_midrank/summary.json

@@ -0,0 +1,597 @@
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+}

results/falsification/4b_closed_form_negR2/closed_form_4b_L12_H8_summary.json

@@ -0,0 +1,216 @@
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