withmartian/ares-20q-case-study

---
license: apache-2.0
tags:
- mechanistic-interpretability
- steering-vectors
- transformer-lens
- agents
- reinforcement-learning
---

# ARES 20Q Case Study — Pre-computed Results

Pre-computed activations and experiment results for the
[ARES Mechanistic Interpretability tutorial](https://github.com/withmartian/ares/blob/main/examples/20q_case_study/ares_mi_20q_tutorial.ipynb).

## What's included

| Directory | Size | Description |
|-----------|------|-------------|
| `20q_data/` | ~4.6 GB | 50 episodes of Llama-3.2-1B-Instruct playing Twenty Questions, with layer-8 residual stream activations captured at every step |
| `20q_probing_results/` | 137 KB | Linear probe accuracy plots (per-step and global) |
| `20q_steering_vector_evolution/` | 417 KB | Cosine similarity heatmap, PCA trajectory, and norm plots for per-step steering vectors |
| `deterministic_20q_steering_results/` | 881 KB | Steering experiment results (baseline + 4 alpha values, 20 episodes each) |

## Quick start

```python
from huggingface_hub import snapshot_download

# Download everything (~4.6 GB)
snapshot_download(
repo_id="withmartian/ares-20q-case-study",
repo_type="dataset",
local_dir="outputs",
)

# Or download only the lightweight analysis results (~1.5 MB)
snapshot_download(
repo_id="withmartian/ares-20q-case-study",
repo_type="dataset",
local_dir="outputs",
ignore_patterns=["20q_data/*"],
)
```

## Model & setup

- **Model**: `meta-llama/Llama-3.2-1B-Instruct` (via TransformerLens)
- **Hook point**: `blocks.8.hook_resid_post` (middle layer residual stream)
- **Episodes**: 50 (data collection), 20 per condition (steering)
- **Max steps per episode**: 25 (data collection), 20 (steering)

## Citation

If you use this data, please cite the ARES repository:

```
@software{ares2025,
title={ARES: Agentic Research and Evaluation Suite},
url={https://github.com/withmartian/ares},
year={2025}
}
```