Initial commit: Set up Guided CoT and extrinsic curvature experiment

README.md

@@ -0,0 +1,52 @@
+# Brukino Kappa S-Space Probe
+
+Testing whether the Frenet-Serret extrinsic curvature ($\kappa$) of a model's hidden state trajectory can predict structural shifts in the model's persona or criterion (e.g., eval-awareness, preference changes) without needing behavioral labels.
+
+## Setup
+
+This project is managed by `uv`.
+
+### Requirements
+- Python 3.11+
+- `uv` installed
+
+### Installation
+
+1. Clone this repository.
+2. The dependencies are specified in `pyproject.toml` and lockfile. `uv` handles them automatically.
+
+To sync the environment:
+```bash
+uv sync
+```
+
+## Running the Experiment
+
+You can explore the experiment either via the Jupyter Notebook or by running the generated Python script directly.
+
+### Via Notebook
+To spin up Jupyter Lab/Notebooks:
+```bash
+uv run jupyter notebook
+```
+Then open `experiment.ipynb` and run the cells.
+
+### Via Script
+To run the python script directly (converted from the notebook via `jupytext`):
+```bash
+uv run python experiment.py
+```
+*(Note: Ensure you have your X11/Wayland display setup to see the matplotlib plot, or run with `MPLBACKEND=Agg` if headless).*
+
+## How it Works
+
+We use the **Guided CoT trick**:
+1. Generate ~32 tokens of Chain of Thought reasoning (`n_think`) using greedy decoding.
+2. Force the model to transition to an answer by appending a specific suffix (`\nI should answer now.\nMy choice: **`).
+3. Run a single forward pass over the full sequence.
+4. Extract the final-layer hidden states during the reasoning step.
+5. Calculate the Frenet-Serret extrinsic curvature $\kappa(t) = \|\gamma''(t)\| / \|\gamma'(t)\|^3$ of these states using finite differences.
+6. Compare $\kappa(t)$ between opposite personas ("honest" vs. "dishonest" vs. "neutral baseline") on daily dilemmas.
+
+## Model
+The default script uses `Qwen/Qwen2.5-0.5B-Instruct` as it fits comfortably on small GPUs or CPUs. You can easily scale this up by changing `MODEL_NAME` in `experiment.ipynb`/`experiment.py`.