README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet
  - config_name: 2d_single_loop
    data_files:
      - split: test
        path: data/2d_single_loop/test-*.parquet
  - config_name: 2d_maze_loop
    data_files:
      - split: test
        path: data/2d_maze_loop/test-*.parquet

OrdinalBench

OrdinalBench is a synthetic benchmark for evaluating ordinal reasoning capabilities of vision-language models (VLMs). It tests whether models can correctly identify the N-th element along structured visual paths — circular loops and maze traversals — in 2D top-down scenes.

🌐 Project page: ordinalbench.github.io 🔧 Evaluation toolkit: github.com/ordinalbench/ordinalbench-public 📦 Dataset: huggingface.co/datasets/u-lec16/ordinalbench-dataset

Dataset Summary

Variant	Modality	Images	QA Pairs	Description
2d_single_loop	2D top-down	1,000	15,000	Count along circular arrangements of labeled objects
2d_maze_loop	2D top-down	1,000	15,000	Traverse maze corridors following turn-preference rules
Total	—	2,000	30,000	—

Difficulty Axes

• Ordinal Level (N magnitude): Within Objects (N ≤ total objects), Exceed Objects (N > total objects, ≤ 99), Large Scale (100 ≤ N ≤ 300)
• Object Count / Grid Size: Few (5 / 7×7), Medium (10 / 11×11), Many (20 / 21×21)
• Stride: 1 (every step), 2 (skip-1), 3 (skip-2)

Usage

With 🤗 Datasets

from datasets import load_dataset

# Load all variants
ds = load_dataset("u-lec16/ordinalbench-dataset")

# Load a specific variant
ds_maze = load_dataset("u-lec16/ordinalbench-dataset", "2d_maze_loop")

# Inspect a sample
sample = ds_maze["test"][0]
print(sample["question"])
print(sample["answer"])
sample["image"].show()  # PIL Image

With the Evaluation Toolkit

pip install ordinalbench  # or: uv sync
ob run --model openai --input <annotation.jsonl> --images-dir <images/> --output predictions.jsonl
ob score --preds predictions.jsonl --truth <annotation.jsonl>

Annotation Schema

Each record contains:

Field	Type	Description
question_id	str	Unique question identifier
image	Image	The input image
question	str	Natural language instruction
answer	str	Ground-truth identifier
N	int	Target ordinal position (1-indexed, up to 300)
ordinal_level	str	Within Objects / Exceed Objects / Large Scale
stride	int	Counting stride (1, 2, or 3)
modality	str	2d
loop_type	str	single or maze
trace	list	Step-by-step traversal trace

Data Splits

Currently provides a single test split designed for zero-shot evaluation. Training splits can be generated using the toolkit's data generation pipelines.

Limitations

• Synthetic data — limited real-world visual noise and lighting variation
• Questions are in English only
• Large Scale (N up to 300) may exceed token limits of some models

Citation

@article{ordinalbench2025,
  title={OrdinalBench: A Benchmark for Ordinal Reasoning in Vision-Language Models},
  author={Tozaki, Yusuke},
  year={2025}
}

License

MIT