---
license: mit
task_categories:
- text-generation
tags:
- activation-oracles
- interpretability
- model-organisms
- ao-blindness
pretty_name: Gemma-2-9B AO-blindness crossover — raw verbalizer outputs
---

# Gemma-2-9B AO-blindness crossover — raw verbalizer outputs

Raw, per-cell **Activation Oracle (AO)** verbalizations from the *faithful AO-blindness
crossover* experiment on `gemma-2-9b-it`. Every AO is run against every taboo model-organism
(MO); this dataset holds the **full generated responses** behind the distilled scores and
figures that live in the code repo (`activation_oracles/experiments/crossover/`).

The experiment tests whether an **off-recipe** AO (trained on top of a quirked MO instead of
the clean base) goes **blind to its own host's quirk** while still seeing foreign quirks.

## Contents

| dir | elicitation | grid |
|---|---|---|
| `direct/` | extraction / jailbreak prompts ("output the secret word") | 21 taboo words × 21 MOs + on-recipe base oracle |
| `standard/` | benign hint-seeking ("Hint me." / "Clue me in.") | same 21×21 grid |
| `think_suppress/` | think / suppress / neutral priming on an unrelated number-generation task | 4 taboo MOs × faithful oracle + base, 2 probe sites |

`direct/` also contains `green_add/` — the incrementally-added `green` oracle column.

## File format

Each `crossover_<oracle>.json` (one per AO):
- `config`, `verbalizer_lora_path`, `host_lora_path`
- `results`: one record per **(target MO × verbalizer prompt × context prompt)**:
  - `target_lora_path`, `context_prompt`, `verbalizer_prompt`, `ground_truth`, `act_key`
  - **`full_sequence_responses` / `segment_responses` / `token_responses`** — the AO's generated text
  - `context_input_ids`, `num_tokens`

`scores_lora.json` in each dir is the distilled per-(oracle, MO) recovery summary
(pooled / best-prompt accuracy), and the `think_suppress/` one also carries Δthink / Δsupp.

## Reproduce

- crossover grids: `experiments/crossover/pipeline/crossover_eval.py` → `score_crossover.py`
- think/suppress: `experiments/crossover/think_suppress/think_suppress_eval.py` → `think_suppress_score.py`

These raw outputs (~450 MB) are fully regenerable and are stored here to keep them out of git;
the code repo keeps only the small `scores_lora.json` + figures.