---
license: mit
language:
- en
pretty_name: common-o
dataset_info:
  features:
  - name: image_1
    dtype: image
  - name: image_2
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: objects_1
    dtype: string
  - name: objects_2
    dtype: string
  - name: num_objects_image_1
    dtype: int64
  - name: num_objects_image_2
    dtype: int64
  - name: question_template
    dtype: string
  - name: answer_type
    dtype: string
  - name: choices
    dtype: string
  - name: num_choices
    dtype: int64
  - name: num_ground_truth_objects
    dtype: int64
  - name: real_or_synthetic
    dtype: string
  - name: ground_truth_objects
    dtype: string
  splits:
  - name: main
    num_bytes: 5408696753
    num_examples: 10426
  - name: challenge
    num_bytes: 594218345
    num_examples: 12600
  download_size: 1102814055
  dataset_size: 6002915098
configs:
- config_name: default
  data_files:
  - split: main
    path: data/main-*
  - split: challenge
    path: data/challenge-*
---

# Common-O

> measuring multimodal reasoning across scenes


Common-O, inspired by cognitive tests for humans, probes multimodal LLMs' ability to reason across scenes by asking "what’s in common?"

![fair conference content copy.001](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/5av7avUrsBjFuMrWuOiCW.jpeg)

Common-O is comprised of household objects:


![fair conference content copy.003](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/hEvVz2uFR6z-jv1em25eY.jpeg)


We have two subsets: Common-O (3 - 8 objects) and Common-O Complex (8 - 16 objects).

## Multimodal LLMs excel at single image perception, but struggle with multi-scene reasoning


![single_vs_multi_image(1)](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/1cB9iXHrSgyvfXgK6gmGu.png)


## Evaluating a Multimodal LLM on Common-O

```python
import datasets

# get a sample
common_o = datasets.load_dataset("facebook/Common-O")["main"]
# common_o_complex = datasets.load_dataset("facebook/Common-O")["complex"]
x = common_o[3]

output: str = model(x["image_1"], x["image_2"], x["question"])

check_answer(output, x["answer"])
```

To check the answer, we use an exact match criteria:

```python
import re

def check_answer(generation: str, ground_truth: str) -> bool:
    """
    Args:
        generation: model response, expected to contain "Answer: ..."
        ground_truth: comma-separated string of correct answers

    Returns: bool, whether the prediction matches the ground truth
    """
    preds = generation.split("\n")[-1]
    preds = re.sub("Answer:", "", preds)
    preds = preds.split(",")
    preds = [p.strip() for p in preds]
    preds = sorted(preds, key=lambda x: x[0])

    # split into a list
    ground_truth_list = [a.strip() for a in ground_truth.split(",")]
    ground_truth_list = sorted(ground_truth_list)
    return preds == ground_truth_list
```

Some models have specific formatting outputs for their answers, e.g. \boxed{A} or Answer: A. We recommend checking a few responses as you may notice slight variations based on this.
This public set also has slight variations with the set used in the original paper, so while the measured capabilities are identical do not expect an exact replication of accuracy figures.


If you'd like to use a single image model, here's a handy function to turn `image_1` and `image_2` into a single split image:

```python
from PIL import Image

def concat_images_horizontal(
        image1: Image.Image, image2: Image.Image, include_space: bool=True, space_width: int=20, fill_color: tuple=(0, 0, 0)
        )  -> Image.Image:
    # from https://note.nkmk.me/en/python-pillow-concat-images/
    if not include_space:
        dst = Image.new("RGB", (image1.width + image2.width, image1.height))
        dst.paste(image1, (0, 0))
        dst.paste(image2, (image1.width, 0))
    else:
        total_width = image1.width + space_width + image2.width
        max_height = max(image1.height, image2.height)

        dst = Image.new("RGB", (total_width, max_height), color=fill_color)
        dst.paste(image1, (0, (max_height - image1.height) // 2))
        dst.paste(image2, (image1.width + space_width, (max_height - image2.height) // 2))
    return dst
```

For more details about Common-O see the 
- [dataset card](https://huggingface.co/datasets/facebook/Common-O/blob/main/COMMON_O_DATASET_CARD.md)
- [ArXiv Paper](https://arxiv.org/abs/2511.03768)


Cite:
```
@inproceedings{Ross2025what0s,
  title  = {What’s in Common? Multimodal Models Hallucinate When Reasoning Across Scenes},
  author = {Candace Ross and Florian Bordes and Adina Williams and Polina Kirichenko and Mark Ibrahim},
  year   = {2025},
  url    = {https://openreview.net/attachment?id=d0F0N0cu4n&name=supplementary_material}
}
```