README.md

---
license: mit
pretty_name: MMIB Evaluation Dataset
task_categories:
- visual-question-answering
- multiple-choice
language:
- en
tags:
- vision
- language
- multimodal
- counterfactual
- mechanistic-interpretability
- synthetic
size_categories:
- n<1K
dataset_info:
  features:
  - name: original_image
    dtype: image
  - name: counterfactual1_image
    dtype: image
  - name: counterfactual2_image
    dtype: image
  - name: counterfactual1_type
    dtype: string
  - name: counterfactual2_type
    dtype: string
  - name: counterfactual1_description
    dtype: string
  - name: counterfactual2_description
    dtype: string
  - name: original_question
    dtype: string
  - name: counterfactual1_question
    dtype: string
  - name: counterfactual2_question
    dtype: string
  - name: original_question_difficulty
    dtype: string
  - name: counterfactual1_question_difficulty
    dtype: string
  - name: counterfactual2_question_difficulty
    dtype: string
  - name: original_image_answer_to_original_question
    dtype: string
  - name: original_image_answer_to_cf1_question
    dtype: string
  - name: original_image_answer_to_cf2_question
    dtype: string
  - name: cf1_image_answer_to_original_question
    dtype: string
  - name: cf1_image_answer_to_cf1_question
    dtype: string
  - name: cf1_image_answer_to_cf2_question
    dtype: string
  - name: cf2_image_answer_to_original_question
    dtype: string
  - name: cf2_image_answer_to_cf1_question
    dtype: string
  - name: cf2_image_answer_to_cf2_question
    dtype: string
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Multimodal Mechanistic Interpretability Benchmark (MMIB) Dataset



The **MMIB Dataset** is a highly controlled, synthetic vision-language dataset designed to rigorously evaluate mechanistic interpretability (MI) methods in Large Multimodal Models (VLMs). Built upon procedurally generated CLEVR-style assets, this dataset provides exact ground-truth causal pathways to test whether MI techniques (like causal tracing or interchange interventions) localize genuine cognitive circuits or merely identify descriptive correlations.

Unlike standard VQA benchmarks, MMIB uses strict **automated rejection sampling** to eliminate geometric ambiguity, ensuring every spatial and causal relationship is mathematically verifiable. 

## Dataset Structure & Interventions

This dataset is built on a structured intervention triplet for every base scene. Each row provides a complete $3 \times 3$ cross-modal evaluation matrix (3 images $\times$ 3 text queries), allowing researchers to systematically trace cross-modal information flow.

### 1. Semantic Counterfactuals (Causal Reasoning)
To evaluate the model's internal causal logic, we generate minimal counterfactual pairs where the intervention mathematically guarantees a change in the ground-truth answer ($y' \neq y$).
* **Image-Based CFs:** 10 targeted 3D scene graph edits (e.g., `change_color`, `change_position`, `relational_flip`) that alter the visual logic while keeping the question fixed.
* **Text-Based CFs:** Minimal deterministic mutations to the textual query (e.g., swapping "red" for "blue" or "left" for "right") that guarantee an answer flip on the fixed base image. 

### 2. Negative Counterfactuals (Diagnostic Stress Tests)
To control for basic visual fragility, we generate **Negative Counterfactuals** featuring 8 types of perceptual corruptions (e.g., `add_noise`, `change_lighting`, `apply_fisheye`). These interventions drastically alter the image distribution *without* changing the underlying 3D geometry or ground-truth answer ($y' = y$). They serve as an experimental baseline: if a model fails on these stress tests, its failure on semantic tasks indicates vulnerability to domain shifts rather than flawed causal logic.

## Using the Dataset

### Loading from Python

The dataset is hosted in standard Parquet format. You can load it directly into your mechanistic evaluation pipeline using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the MMIB dataset
ds = load_dataset("scholo/MMB_dataset", split="train")

# Inspect the 3x3 evaluation matrix for the first scene
print("Base Question:", ds[0]['original_question'])
print("Base Image -> Base Question Answer:", ds[0]['original_image_answer_to_original_question'])
print("Semantic CF Image -> Base Question Answer:", ds[0]['cf1_image_answer_to_original_question'])
(No trust_remote_code=True is required.)Directory StructureMMB-Dataset/
├── README.md                           # This dataset card
├── .gitattributes                      # Git LFS configuration
├── data/                               # Dataset files (Parquet format)
│   └── train.parquet                   # Main benchmark matrix
├── Dataset/                            # Raw generation artifacts
│   ├── images/                         # Uncompressed PNG renders (720x720)
│   ├── scenes/                         # JSON 3D scene graphs and metadata
│   ├── image_mapping_with_questions.csv # Source mapping for the 3x3 grid
│   └── run_metadata.json               # Procedural generation engine parameters
Application & ProtocolFollowing the rigorous evaluation protocol established in the MMIB paper, interpretability metrics (such as Circuit Performance Ratio, Circuit-Model Distance, and Interchange Intervention Accuracy) should only be computed on samples where the target VLM correctly answers the base question ($a_b = y$). This behavioral filter ensures that the model possesses the causal circuit prior to mechanistic evaluation.LicenseMIT