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README.md

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+---
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+  - image-text-to-text
+language:
+  - en
+tags:
+  - spatial-reasoning
+  - multi-hop
+  - grounding
+  - vision-language
+  - benchmark
+  - VQA
+  - bounding-box
+pretty_name: MultihopSpatial
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/multihop_train_6791.json
+      - split: test
+        path: data/multihop_test_4500.json
+---
+
+# MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Models
+
+<p align="center">
+  <img src="teaser_2.png" width="100%" alt="MultihopSpatial Benchmark Overview">
+</p>
+
+<p align="center">
+  <a href="https://youngwanlee.github.io/multihopspatial_private"><b>Project Page</b></a> |
+  <a href="https://arxiv.org/abs/2603.18892"><b>Paper</b></a>
+</p>
+
+## 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
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("YOUR_HF_REPO/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](https://www.apache.org/licenses/LICENSE-2.0) | MultihopSpatial (this work) |
+| **COCO Images** | [COCO Terms of Use](https://cocodataset.org/#termsofuse) | [MS-COCO](https://cocodataset.org/) |
+| **PACO-Ego4D Images** | [Ego4D License](https://ego4ddataset.com/ego4d-data/license/) | [PACO](https://github.com/facebookresearch/paco) / [Ego4D](https://ego4ddataset.com/) |
+
+> The images retain their original licenses. Our VQA annotations (questions, answers, bounding boxes, and metadata) are released under the Apache 2.0 License.
+
+## Citation
+
+```bibtex
+@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](https://youngwanlee.github.io/multihopspatial_private) or open an issue in this repository.