MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Models

Project Page | Paper | Model

Overview

MultihopSpatial is a benchmark designed to evaluate whether vision-language models (VLMs) demonstrate robustness in multi-hop compositional spatial reasoning. Unlike existing benchmarks that only assess single-step spatial relations, MultihopSpatial features queries with 1 to 3 reasoning hops paired with visual grounding evaluation, exposing a critical blind spot: models achieving high multiple-choice accuracy often lack proper spatial localization.

All 4,500 benchmark QA pairs and bounding boxes are strictly annotated by ten trained human experts with an inter-rater agreement of 90% (Krippendorff's α = 0.90).

Key Features

• Multi-hop Composition: Tests 1-hop, 2-hop, and 3-hop sequential spatial reasoning, mirroring real-world embodied AI needs.
• Grounded Evaluation: Addresses the "lucky guess" problem — models must both select the correct answer AND localize it via bounding box (Acc@50IoU).
• Perspective-taking: Includes both ego-centric and exo-centric viewpoints.
• Three Spatial Categories: Attribute (ATT), Position (POS), and Relation (REL), composable into multi-hop questions.
• Training Data: MultihopSpatial-Train (6,791 samples) supports post-training via reinforcement learning (e.g., GRPO).

Dataset Statistics

MultihopSpatial

	Ego-centric	Exo-centric	Total
1-hop	750	750	1,500
2-hop	750	750	1,500
3-hop	750	750	1,500
Total	2,250	2,250	4,500

Spatial Reasoning Compositions

Hop	Categories
1-hop	ATT, POS, REL
2-hop	ATT+POS, ATT+REL, POS+REL
3-hop	ATT+POS+REL

Data Fields

Field	Type	Description
id	int	Unique sample identifier
image_path	string	Image filename (e.g., 000000303219.jpg or 01ce4fd6-..._002114.jpeg)
image_resolution	string	Image resolution in WxH format
view	string	Viewpoint type: "ego" (ego-centric) or "exo" (exo-centric)
hop	string	Reasoning complexity: "1hop", "2hop", or "3hop"
question	string	The spatial reasoning question in plain text with multiple-choice options
question_tag	string	Same question with spatial reasoning type tags (<ATT>, <POS>, <REL>) annotated inline
answer	string	The correct answer choice (e.g., "(c) frame of the reed picture")
bbox	list[float]	Bounding box [x, y, width, height] of the answer object in pixel coordinates

question vs question_tag

• question: Clean natural language question, e.g.,

  "From the perspective of the woman holding the remote control, which object is on her right?"

• question_tag: Same question with spatial reasoning tags marking which type of reasoning each part requires, e.g.,

  *"From the perspective of the woman holding the remote control, which object is <POS>on her right</POS>?"*

  Tags: <ATT>...</ATT> (Attribute), <POS>...</POS> (Position), <REL>...</REL> (Relation)

Data Structure

MultihopSpatial/
├── README.md
├── teaser_2.png
├── data/
│   ├── multihop_test_4500.json
│   ├── multihop_train_6791.json
│   └── images/
│       ├── 000000303219.jpg
│       ├── 000000022612.jpg
│       ├── 01ce4fd6-197a-4792-8778-775b03780369_002114.jpeg
│       └── ...

Usage

from datasets import load_dataset

dataset = load_dataset("etri-vilab/MultihopSpatial")

# Access splits
test_data = dataset["test"]
train_data = dataset["train"]

# Example
sample = test_data[0]
print(sample["question"])
# "From the perspective of the woman holding the remote control, which object is on her right? ..."
print(sample["answer"])
# "(c) frame of the reed picture"
print(sample["bbox"])
# [52.86, 38.7, 70.95, 97.83]
print(sample["hop"])
# "1hop"

Image Sources & License

Component	License	Source
VQA Annotations (questions, answers, bounding boxes)	Apache 2.0	MultihopSpatial (this work)
COCO Images	COCO Terms of Use	MS-COCO
PACO-Ego4D Images	Ego4D License	PACO / Ego4D

The images retain their original licenses. Our VQA annotations (questions, answers, bounding boxes, and metadata) are released under the Apache 2.0 License.

Citation

@article{lee2025multihopspatial,
  title={MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Models},
  author={Lee, Youngwan and Jang, Soojin and Cho, Yoorhim and Lee, Seunghwan and Lee, Yong-Ju and Hwang, Sung Ju},
  journal={arXiv preprint arXiv:2603.18892},
  year={2025}
}

Contact

For questions or issues, please visit the Project Page or open an issue in this repository.