| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-text-to-text |
| - visual-question-answering |
| language: |
| - en |
| - zh |
| pretty_name: InquireMobile |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - mobile-agent |
| - gui-agent |
| - vlm |
| - rl |
| - interactive |
| --- |
| |
| # InquireMobile |
|
|
| Dataset for **InquireMobile: Teaching VLM-based Mobile Agent to Request Human |
| Assistance via Reinforcement Fine-Tuning** (ACL 2026). |
|
|
| InquireMobile is an interactive mobile GUI agent that *actively seeks human |
| confirmation at critical decision points* instead of acting autonomously. This |
| dataset contains: |
|
|
| - **InquireBench** evaluation tasks (instruction + category + intent), and |
| - the **training trajectories** used for supervised fine-tuning (SFT) and |
| reinforcement fine-tuning (GRPO / RLVR). |
|
|
| > Project page: <https://bit-aqh.github.io/InquireMobile/homepage/> |
| > Paper: <https://aclanthology.org/2026.acl-long.1487/> |
|
|
| ## Contents |
|
|
| The dataset is organized as: |
|
|
| - `data/` — all JSONL data files (see below). |
| - `images/` — screenshots referenced by the data files (see [Images](#images)). |
|
|
| All data files are line-delimited JSON (JSONL) unless noted otherwise. |
|
|
| ### Evaluation tasks (InquireBench) |
|
|
| | File | Description | |
| |---|---| |
| | `tasks_en.jsonl`, `tasks_zh.jsonl` | Core evaluation task instructions (English / Chinese). | |
| | `tasks_en_extended.jsonl` | Extended English task set. | |
| | `tasks_{en,zh}_huawei.jsonl`, `tasks_{en,zh}_simulator.jsonl` | Task subsets for the Huawei device / simulator environments. | |
| | `tasks_zh_huawei_7.jsonl` | Huawei-device subset (7 tasks). | |
|
|
| Task schema: |
|
|
| ```json |
| { |
| "instruction": "Open WeChat, browse my Moments ...", |
| "apps": ["wechat"], |
| "category": "privacy", |
| "language": "en", |
| "need_login": true, |
| "intension": "Open WeChat, browse my Moments ..." |
| } |
| ``` |
|
|
| ### Training data |
|
|
| | File | Description | |
| |---|---| |
| | `interactive_sft.jsonl` | Stage-1 SFT trajectories (conversation format). | |
| | `interactive_grpo_401408.jsonl` | Stage-2 GRPO / RLVR trajectories (401408 max-pixels). | |
| | `general_grpo_5k.jsonl` | General GRPO training data (5k). | |
| | `sft_general.jsonl` | SFT data (general). | |
| | `sft_pos.jsonl` | SFT data (positive samples). | |
|
|
| ### Instruction pool |
|
|
| | File | Description | |
| |---|---| |
| | `instructions.jsonl` | Raw instruction pool (Chinese + English), with optional `image` links to the instruction screenshots. | |
|
|
| GRPO / trajectory schema: |
|
|
| ```json |
| { |
| "goal": "...", |
| "action": "...", |
| "bbox": [x1, y1, x2, y2], |
| "width": 1080, |
| "height": 1920, |
| "resized_width": 432, |
| "resized_height": 768, |
| "img_list": ["..."], |
| "system": "...", |
| "query": "..." |
| } |
| ``` |
|
|
| SFT schema: |
|
|
| ```json |
| { |
| "conversations": [ |
| {"from": "system", "value": "..."}, |
| {"from": "human", "value": "<image>..."}, |
| {"from": "gpt", "value": "..."} |
| ], |
| "images": ["..."] |
| } |
| ``` |
|
|
| ## Images |
|
|
| Images are stored under `images/` and are referenced by **relative paths** from |
| the data files (relative to the dataset root). |
|
|
| | Directory | Description | |
| |---|---| |
| | `images/trajectory/` | Stage-2 GRPO rollout screenshots (UUID-named), referenced by `img_list` in the GRPO data. | |
| | `images/instruction/` | The 72 English instruction screenshots (`1.png`…`72.png`), referenced by the `images` field of the SFT data and by `interactive_added.jsonl`. | |
|
|
| The data files reference images by their basename (UUID) or number (`N.png`); map |
| these to the directories above when reproducing training/evaluation. |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| |
| with open("data/tasks_en.jsonl", encoding="utf-8") as f: |
| tasks = [json.loads(line) for line in f] |
| ``` |
|
|
| Or download directly from Hugging Face: |
|
|
| ```bash |
| huggingface-cli download PG23/InquireMobile --repo-type dataset --local-dir ./InquireMobile-data |
| ``` |
|
|
| ## License |
|
|
| - **Data**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{ai-etal-2026-inquiremobile, |
| title = "{I}nquire{M}obile: Teaching {VLM}-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning", |
| author = "Ai, Qihang and |
| Bu, Pi and |
| Cao, Yue and |
| Wang, Yingyao and |
| Gu, Jihao and |
| Xing, Jingxuan and |
| Zhu, Zekun and |
| Jiang, Wei and |
| Zheng, Zhicheng and |
| Song, Jun and |
| Jiang, Yuning", |
| booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
| month = jul, |
| year = "2026", |
| address = "San Diego, California, United States", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.acl-long.1487/", |
| doi = "10.18653/v1/2026.acl-long.1487", |
| pages = "32241--32255" |
| } |
| ``` |
|
|
