Initial release of ARK-Bench

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README.md

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+---
+pretty_name: ARK-Bench
+license: apache-2.0
+tags:
+  - multimodal
+  - retrieval
+  - benchmark
+  - reasoning
+  - knowledge
+  - arxiv:2602.09839
+configs:
+  - config_name: gallery
+    default: true
+    data_dir: data/gallery
+    drop_labels: true
+  - config_name: queries
+    data_dir: data/queries
+    drop_labels: true
+---
+
+<div align="center">
+
+# ARK-Bench
+
+**A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge**
+
+*Retrieval beyond surface-level similarity.*
+
+[![Paper](https://img.shields.io/badge/arXiv-2602.09839-B31B1B)](https://arxiv.org/abs/2602.09839) [![Leaderboard](https://img.shields.io/badge/🤗_Leaderboard-Explore_results-167D7F)](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard) [![Dataset](https://img.shields.io/badge/🤗_Dataset-ARK--Bench-E9B44C)](https://huggingface.co/datasets/XLearning-SCU/ARK-Bench)
+
+**1,750 queries · 36,858 candidates · 17 topics · 6 reasoning skills**
+
+[Quick start](#quick-start) · [Data format](#data-format) · [Evaluation](#evaluation) · [Citation](#citation)
+
+</div>
+
+ARK-Bench evaluates multimodal retrieval that requires **domain knowledge and reasoning**, beyond surface-level similarity. It covers five knowledge domains, 17 topics, six reasoning skills, and 16 visual data types. Queries and candidates can be unimodal or multimodal, and most queries include targeted hard negatives.
+
+Explore model results by knowledge domain, reasoning skill, and cognitive demand on the **[ARK-Bench Leaderboard](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard)**. The headline counts above are those reported in the paper.
+
+<details>
+<summary>See representative query–target examples</summary>
+
+![Examples of knowledge, symbolic, spatial, conceptual, logical, and fine-grained visual reasoning in ARK-Bench.](static/reasoning-examples.png)
+
+</details>
+
+## Quick start
+
+Install the loading library and image dependencies:
+
+```bash
+pip install "datasets[vision]"
+```
+
+Load the query set and retrieval corpus explicitly by configuration name:
+
+```python
+from datasets import load_dataset
+
+DATASET_ID = "XLearning-SCU/ARK-Bench"
+
+queries = load_dataset(DATASET_ID, "queries", split="test")
+gallery = load_dataset(DATASET_ID, "gallery", split="test")
+
+print(f"Queries: {len(queries):,} | Gallery: {len(gallery):,}")
+print(queries.features)
+print(gallery.features)
+```
+
+Both configurations use the **`test` split**. A `topic` such as `Calendar` is a field within that split, not a separate split or configuration. If no configuration is specified, the default is `gallery`.
+
+## Data format
+
+```text
+data/
+├── queries/test/
+│   ├── metadata.jsonl
+│   └── images/<topic>/...
+└── gallery/test/
+    ├── metadata.jsonl
+    └── images/<topic>/...
+```
+
+The tables below describe the **stored metadata**.
+
+| Query field | Meaning |
+| --- | --- |
+| `topic`, `query_id` | Topic and query identifier. |
+| `text` | Query text. |
+| `query_image_file_name` | Query image reference information. |
+| `query_image_alt_texts` | Alternative text associated with query images. |
+| `target` | Relevant gallery IDs within the same topic. |
+| `hard_negative` | Targeted hard-negative annotations. |
+| `reasoning_demand` | Overall cognitive demand: perception-only, knowledge-intensive, reasoning-intensive, or both. |
+| `reasoning_skill` | Reasoning-skill annotation; an instance may involve multiple skills. |
+
+| Gallery field | Meaning |
+| --- | --- |
+| `topic`, `corpus_id` | Together identify a retrieval candidate. |
+| `file_name` | Candidate image path. |
+| `alt_text` | Alternative text associated with the image. |
+
+Image paths are relative to the corresponding metadata directory. [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) converts image references into image features—for example, `file_name` becomes `image`. Inspect `.features` for the loaded column names, and account for queries without images.
+
+## Evaluation
+
+- **Candidate pool:** evaluate each topic against its own full gallery. Use `target` for relevance judgments and reasoning annotations for analysis, keeping these annotations out of model inputs.
+- **Metrics:** Recall@1 (primary), Recall@5, Recall@10, nDCG@5, nDCG@10, and nDCG@20.
+- **Aggregation:** report equal-weight macro averages across the 17 topics and across the six reasoning skills. The leaderboard's Overall Avg is the mean of these two macro averages; submitted scores use the 0–100 scale.
+
+The [paper](https://arxiv.org/abs/2602.09839) documents construction, sources, and the evaluation setup, including image captioning for text-only baselines. Preserve the test split for held-out evaluation and document any preprocessing when reporting results.
+
+**Submit results:** the leaderboard currently covers embedding models. Follow its submission instructions, complete the [score template](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard/resolve/main/scores_template.json), and submit a pull request to the Space's [`scores/` directory](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard/tree/main/scores).
+
+## License
+
+This repository declares **Apache-2.0**. For third-party materials, refer to the original source terms and the data sources documented in the paper.
+
+## Citation
+
+If you use ARK-Bench in your research, please cite:
+
+```bibtex
+@misc{lin2026ark,
+  title         = {{ARK}: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge},
+  author        = {Yijie Lin and Guofeng Ding and Haochen Zhou and Haobin Li and Mouxing Yang and Xi Peng},
+  year          = {2026},
+  eprint        = {2602.09839},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV},
+  url           = {https://arxiv.org/abs/2602.09839}
+}
+```
+
+For dataset questions or issues, open a discussion in the [dataset community](https://huggingface.co/datasets/XLearning-SCU/ARK-Bench/discussions).