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README.md

@@ -84,3 +84,18 @@ paper for the full method and the honest negatives.
 
 Code: **GPL-3.0**. Paper & docs: **CC-BY-SA-4.0**. The frozen base model (Gemma 4) is subject to Google's
 Gemma terms; this work distributes the **channel adapter and method**, not Gemma's weights.
+
+## What's in this repo
+
+| file | what |
+|---|---|
+| `adapter_model.safetensors` | the trained channel adapter — **185.8M params, bf16** (the integrated 6/6 checkpoint, step 10000) |
+| `adapter_config.json` | dims, channel sizes, K-per-channel, base model |
+| `modeling_channels.py` | the `ChannelInjectionDelta` + `ChanneledLayer` modules (post-layer gated cross-attention + ReZero) |
+| `proprioceptive-channels.pdf` | the paper |
+
+This is the **channel adapter only** — not Gemma's weights. It wraps a **frozen `google/gemma-4-E2B-it`**
+(35 text layers, hidden 1536); load Gemma from Google, then wrap each layer with `ChanneledLayer` and load
+these weights. With ReZero α at its trained values the channels drive behavior; with α=0 the model is
+bit-exact to vanilla Gemma. See `modeling_channels.py` and the paper for the wiring and the six channel
+dimensions (memory 1024 · affect 2 · time 16 · ethics 24 · identity 1024 · continuity 1024).