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README.md

@@ -1,3 +1,67 @@
 ---
 license: apache-2.0
+language:
+- en
+tags:
+- transformers
+- residual-connections
+- scaling-laws
+- language-modeling
+- reliability
+pretty_name: "Review Residuals — Scaling Sweep Results"
+size_categories:
+- n<1K
 ---
+
+# Review Residuals — Scaling Sweep Results
+
+Supporting data, code, and paper for **"Review Residuals: Update-Conditioned Residual Gating for Transformers"** (Kyle Kramer, NeraTech LLC, 2026).
+
+Review Residuals scale each transformer sublayer's proposed update by a small learned gate conditioned on *both* the current state **and** the proposed update — an in-network analogue of *independent verification*. Trained from scratch with parameter-matched baselines, the advantage over both a Highway gate and the standard residual **emerges with scale**.
+
+**Code & full repo:** https://github.com/SixSigmaEngineer/review-residuals
+
+## What's here
+
+- `scaling_v8.csv` — the 42 training runs (per-seed validation losses) behind every number in the paper.
+- `train.py`, `analyze_results.py`, `make_emergence_figure.py`, `run_on_runpod.ipynb` — training, analysis, figure, and cloud-GPU code.
+- `review_residuals.pdf` — the technical paper; plain-English companion included.
+- Figures: `mechanism_additive.png`, `emergence_curve.png`.
+
+## `scaling_v8.csv` schema
+
+| column | meaning |
+|---|---|
+| `size` | model size group (60M, 150M, 320M, 590M, 1B) |
+| `variant` | `review_neutral` (ours), `highway`, or `standard` (both baselines parameter-matched up to Review) |
+| `seed` | random seed |
+| `params_M` | actual parameter count (millions) |
+| `steps` | training steps |
+| `val_loss` | validation loss (lower is better) |
+| `ece` | expected calibration error (where measured) |
+| `minutes` | wall-clock training time |
+
+## Headline result
+
+| Size | Review | Highway | Standard | Review wins? |
+|---|:--:|:--:|:--:|:--|
+| 60M | 1.6891 | 1.6923 | **1.6805** | no |
+| 150M | 1.5576 | 1.5625 | 1.5548 | tie |
+| 320M | **1.5001** | 1.5042 | 1.5037 | tie |
+| 590M | **1.4795** | 1.4889 | 1.4901 | **yes (p<0.05)** |
+| 1B | **1.4876** | 1.5031 | 1.5040 | **yes (strong trend)** |
+
+Reproduce the table and t-tests with `python analyze_results.py`.
+
+## Citation
+
+```bibtex
+@misc{kramer2026reviewresiduals,
+  title  = {Review Residuals: Update-Conditioned Residual Gating for Transformers},
+  author = {Kramer, Kyle},
+  year   = {2026},
+  note   = {NeraTech LLC}
+}
+```
+
+Licensed under Apache-2.0.