---
language:
- ko
- en
tags:
- gui-agent
- web-agent
- evaluation
- evidence-grounded
configs:
- config_name: ko
  data_files:
  - split: test
    path: tasks/150_ko_metadata.json
- config_name: en
  data_files:
  - split: test
    path: tasks/70_en_metadata.json
---

# EVIDENT: Task Metadata for Evidence-Grounded GUI Evaluation

[![GitHub](https://img.shields.io/badge/GitHub-EVIDENT-181717?logo=github&style=for-the-badge)](https://github.com/yerinNam/EVIDENT)

This release contains **220 GUI/web agent task records** for evidence-grounded
trajectory evaluation: 150 Korean and 70 English tasks. Task definitions pair
a user request with its intended outcome and evidence checks.

**Evaluation code and instructions:** [yerinNam/EVIDENT](https://github.com/yerinNam/EVIDENT).

| Task type | Korean | English | Total |
| --- | ---: | ---: | ---: |
| Semantic Grounding | 50 | 27 | 77 |
| Contextual Inference | 50 | 21 | 71 |
| Visual Grounding | 50 | 22 | 72 |
| Total | 150 | 70 | 220 |

## Contents

- `tasks/150_ko_metadata.json`: 150 Korean task records.
- `tasks/70_en_metadata.json`: 70 English task records.

Both files are JSON arrays. Every record includes `id`, `prompt`, `task_type`,
`start_url`, `user_intent`, and `evidence_checks` (a list of checks).
Some records also include `visual_target` (a string) or `visual_cues` (a list).
IDs are unique within each language file. These are task metadata files;
agent trajectories and screenshots must be collected separately.

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download("KORMo-VL/EVIDENT", repo_type="dataset", local_dir="EVIDENT-data")
```

## Evaluate agent trajectories

[EVIDENT](https://github.com/yerinNam/EVIDENT) implements three stages:
extract observed screenshot evidence, review final-answer claims against that
evidence, and judge the task outcome with task metadata and rubrics.

Follow the GitHub README to install dependencies and start the judge server.
Then, from the cloned EVIDENT repository, run:

```bash
python3 -m pipeline.main_eval \
  --tasks-json /path/to/EVIDENT-data/tasks/150_ko_metadata.json \
  --results-root /path/to/agent_results \
  --port 8005
```

Use `70_en_metadata.json` for English. The results directory should contain
one episode directory per task ID, with `trajectory.json` and the screenshots
it references. See the [repository README](https://github.com/yerinNam/EVIDENT#usage)
for single-episode evaluation, final-answer inputs, cache options, and reports.

These tasks use live websites. Content, availability, prices, and rankings
may change; record the evaluation date when reporting results.

## About KORMo-VL

KORMo-VL is a vision-language model developed from scratch by the
[KAIST MLP Lab](https://sites.google.com/view/aailab), built on top of KORMo-10B.
This task metadata release supports GUI/web agent evaluation under the
[KORMo-VL organization](https://huggingface.co/KORMo-VL).

## Source

The task JSON files are preserved byte-for-byte from `yerin/EVIDENT/tasks`.
Evaluation implementation: [yerinNam/EVIDENT](https://github.com/yerinNam/EVIDENT).

## Contact

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

## Contributor

* Yerin Nam ([yerinnam@kaist.ac.kr](mailto:yerinnam@kaist.ac.kr))
* Minkyung Cho ([minkyung.cho@kaist.ac.kr](mailto:minkyung.cho@kaist.ac.kr))
* Minkyu Kim ([minkyukim@kaist.ac.kr](mailto:minkyukim@kaist.ac.kr))
* Seungwoo Song ([sswoo@kaist.ac.kr](mailto:sswoo@kaist.ac.kr))
* Hangyeol Yoo ([hgyoo@kaist.ac.kr](mailto:hgyoo@kaist.ac.kr))
* KyungTae Lim ([ktlim@kaist.ac.kr](mailto:ktlim@kaist.ac.kr))