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Datasets:
KORMo-VL
/
Ko-Bench

Tasks:
Image-Text-to-Text
Modalities:
Image
Text
Formats:
parquet
Languages:
Korean
Size:
< 1K
ArXiv:
Tags:
gui-agent
grounding
korean
web-agent
evaluation
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metadata 
license: cc-by-4.0
language:
  - ko
task_categories:
  - image-text-to-text
size_categories:
  - 10K<n<100K
tags:
  - gui-agent
  - grounding
  - korean
  - web-agent
  - evaluation
configs:
  - config_name: screenspot_ko
    data_files:
      - split: test
        path: screenspot_ko/examples/test.parquet
  - config_name: multimodal_mind2web_ko
    data_files:
      - split: test
        path: multimodal_mind2web_ko/examples/test.parquet
  - config_name: online_mind2web_ko
    data_files:
      - split: test
        path: online_mind2web_ko/examples/test.parquet

Ko-Bench: Korean Web GUI Evaluation

Ko-Bench was created by human annotators.

The suite brings together three Korean benchmarks for GUI grounding, offline action prediction, and live website task completion. The Hub viewer contains 10 examples per benchmark. Complete image benchmarks are provided in gzip archives; Online Mind2Web is provided as an uncompressed JSON file. The examples are subsets of the full release, not additional evaluation tasks.

Benchmark Complete release Viewer examples Full download
Korean ScreenSpot 3,319 grounding questions 10 screenspot_ko.tar.gz
Korean Multimodal Mind2Web 12,905 scored steps from 549 tasks 10 steps multimodal_mind2web_ko.tar.gz
Korean Online Mind2Web 312 live website tasks 10 online_mind2web_ko_tasks.json

Download the complete benchmarks 

from huggingface_hub import snapshot_download

snapshot_download(
    "KORMo-VL/Ko-Bench",
    repo_type="dataset",
    local_dir="Ko-Bench",
    allow_patterns=["README.md", "*/*.tar.gz", "online_mind2web_ko/*.json"],
)

Extract from the release root:

cd Ko-Bench
tar -xzf screenspot_ko/screenspot_ko.tar.gz
tar -xzf multimodal_mind2web_ko/multimodal_mind2web_ko.tar.gz

After extraction, ScreenSpot has screenspot_ko/samples.jsonl and images/; Multimodal Mind2Web has multimodal_mind2web_ko/steps.jsonl, tasks.jsonl, tasks_rewrite.jsonl, and images/. Each JSONL image path is relative to its benchmark directory. Online Mind2Web is a JSON array of task records with exactly five fields: task_id, instruction, start_url, task_level, and task_type. task_type is a list of labels; its values are preserved from the original task types.

Load the 10-example previews 

from datasets import load_dataset

preview = load_dataset("KORMo-VL/Ko-Bench", "screenspot_ko", split="test")
# Other configurations: multimodal_mind2web_ko, online_mind2web_ko

The two image previews embed the original image bytes directly in Parquet, so the viewer does not need to download the full image archives. Complete example records are provided in examples/samples.jsonl. The offline Mind2Web preview also exposes action_json and previous_actions. task_id is the final column in both image previews. Matching unmodified PNG files are included. Preview selection is illustrative and is not intended for reporting benchmark scores.

Tasks and evaluation

  • ScreenSpot: locate the UI element described by a Korean instruction. A predicted point is correct when it falls inside gold_bbox.
  • Multimodal Mind2Web: predict the next action from a screenshot, task instruction, and action history. Evaluate element accuracy, step success, and task success. There are 7,580 scored steps with a target box. The archives contain the 12,905 scored steps; the original complete trajectories contain 14,042 steps, including unscored GOTO, WAIT, and FINISH.
  • Online Mind2Web: execute tasks on live websites and assess whether the requested result was achieved. The release excludes 14 date-dependent tasks from the previous 326-task no-login set. It includes task_level and potentially overlapping task_type labels. Website content can change.

Pixel coordinates and normalized *_norm fields retain their recorded conventions. No action vocabulary or coordinate conversion is applied in this release.

About KORMo-VL

KORMo-VL is a vision-language model developed from scratch by the KAIST MLP Lab, built on top of KORMo-10B. This dataset release provides Korean web GUI evaluation resources under the KORMo-VL organization.

Contact

  • KyungTae Lim, Professor at KAIST. ktlim@kaist.ac.kr

Contributor

Citation

References for KORMo and the original benchmark formats are provided below.

KORMo 

@misc{KORMo,
  author = {Minjun Kim and Hyeonseok Lim and Hangyeol Yoo and Inho Won and Seungwoo Song and Minkyung Cho and Junghun Yuk and Changsu Choi and Dongjae Shin and Huije Lee and Hoyun Song and Alice Oh and KyungTae Lim},
  title = {KORMo: Korean Open Reasoning Model for Everyone},
  year = {2025},
  publisher = {GitHub},
  journal = {Technical Report},
  url = {https://arxiv.org/abs/2510.09426}
}

ScreenSpot

SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

@inproceedings{cheng2024seeclick,
  title = {SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents},
  author = {Kanzhi Cheng and Qiushi Sun and Yougang Chu and Fangzhi Xu and Yantao Li and Jianbing Zhang and Zhiyong Wu},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  pages = {9313--9332},
  publisher = {Association for Computational Linguistics},
  doi = {10.18653/v1/2024.acl-long.505},
  url = {https://aclanthology.org/2024.acl-long.505/}
}

Multimodal Mind2Web

The official Multimodal Mind2Web dataset card cites SeeAct and the original Mind2Web work.

@inproceedings{zheng2024seeact,
  title = {GPT-4V(ision) is a Generalist Web Agent, if Grounded},
  author = {Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su},
  booktitle = {Forty-first International Conference on Machine Learning},
  year = {2024},
  url = {https://openreview.net/forum?id=piecKJ2DlB}
}

Online Mind2Web

An Illusion of Progress? Assessing the Current State of Web Agents

@inproceedings{xue2025an,
  title = {An Illusion of Progress? Assessing the Current State of Web Agents},
  author = {Tianci Xue and Weijian Qi and Tianneng Shi and Chan Hee Song and Boyu Gou and Dawn Song and Huan Sun and Yu Su},
  booktitle = {Second Conference on Language Modeling},
  year = {2025},
  url = {https://openreview.net/forum?id=6jZi4HSs6o}
}

Mind2Web

Both Multimodal Mind2Web and Online Mind2Web also request citation of the original Mind2Web work.

@inproceedings{deng2023mind2web,
  title = {Mind2Web: Towards a Generalist Agent for the Web},
  author = {Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Sam Stevens and Boshi Wang and Huan Sun and Yu Su},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {36},
  pages = {28091--28114},
  year = {2023},
  publisher = {Curran Associates, Inc.},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf}
}