| --- |
| license: cc-by-nc-4.0 |
| language: |
| - zh |
| - en |
| task_categories: |
| - reinforcement-learning |
| tags: |
| - gui-agents |
| - reinforcement-learning-environments |
| - browser |
| - trace2world |
| pretty_name: Trace2World |
| size_categories: |
| - 1K<n<10K |
| extra_gated_prompt: | |
| Trace2World environments are released for non-commercial research on GUI agents and |
| reinforcement learning (CC BY-NC 4.0). Requests are reviewed manually; please provide |
| the following information. |
| extra_gated_fields: |
| Full name: text |
| Institution or company: text |
| Email: text |
| Intended use: |
| type: select |
| options: |
| - Academic research |
| - Non-commercial research in industry |
| - Other |
| I agree not to redistribute the dataset: checkbox |
| I agree to use the dataset only for non-commercial research under its CC BY-NC license: checkbox |
| --- |
| |
| <div align="center"> |
| <img src="assets/trace2world-banner.png" width="100%" alt="Trace2World: interaction traces converge into one source-validated app world that yields an executable app and learner-adaptive tasks"> |
|
|
| <h1>Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training</h1> |
|
|
| <h3>Environments</h3> |
| </div> |
|
|
| The data release of **Trace2World: Reconstructing App Worlds from Real |
| Interaction Traces for Scalable GUI Agent Training**. Trace2World separates |
| global behavior reconstruction from implementation: it reconciles partial |
| real-app traces into a shared, source-linked app model, compiles an executable |
| environment, and accepts it only after independent replay of the original |
| workflows. From 1,570 traces it constructs 50 environments; on 200 workflows |
| across ten apps, 91.1% are reproduced end to end versus 59.8% for the strongest |
| matched task-local baseline. The release bundles the reconstructed environments with their frozen |
| evaluators and state-kernel data, together with the two task collections: |
|
|
| - **Train set** — 2,048 optimization tasks: 1,280 seed tasks and 768 learner-guided tasks, drawn |
| from the 40 policy-training applications. |
| - **Test set** — Trace2World-Bench, 1,044 sealed tasks: 256 entity replacements, 512 held-out |
| workflows, 100 cross-app returns, and 176 tasks from the ten unseen applications. |
|
|
| The code that hosts, evaluates and drives the environments, and the constructor |
| that turns traces into environments, live in the GitHub repository |
| [Tongyi-MAI/trace2app](https://github.com/Tongyi-MAI/trace2app). |
|
|
| ## Access |
|
|
| Access is gated and reviewed manually. Request access on this page with the |
| form above; once approved, authenticate with a Hugging Face token |
| (`huggingface-cli login` or `HF_TOKEN`) before downloading. |
|
|
| ## Usage |
|
|
| ```bash |
| git clone https://github.com/Tongyi-MAI/trace2app.git && cd trace2app |
| pip install ./trace2world_env huggingface_hub && playwright install chromium |
| |
| python scripts/download_environments.py # environments/ -> data/trace2world, verified against the manifest |
| t2w-serve --environments-dir data/trace2world --state-root /tmp/t2w-state --host 127.0.0.1 --port 6800 |
| |
| ``` |
|
|
| `t2w-serve` hosts the whole portfolio in one process and routes each `task_id` |
| to its environment's pages, tasks, evaluator and state kernel. The HTTP API, |
| action space and reward semantics are documented in |
| [`trace2world_env/README.md`](https://github.com/Tongyi-MAI/trace2app/blob/main/trace2world_env/README.md); |
| the construction path and its paper-algorithm mapping in |
| [`trace2world_pipeline/README.md`](https://github.com/Tongyi-MAI/trace2app/blob/main/trace2world_pipeline/README.md) |
| and [`WORKFLOW.md`](https://github.com/Tongyi-MAI/trace2app/blob/main/trace2world_pipeline/WORKFLOW.md); |
| the download / verification scripts in |
| [`scripts/`](https://github.com/Tongyi-MAI/trace2app/tree/main/scripts). |
|
|
| ## Layout |
|
|
| ```text |
| environments_manifest.json environments, per-environment task ids, SHA-256 of every file under environments/ |
| environments/<id>/ |
| ├── resource_manifest.json entry page, task table, evaluator, runtime profile, viewport |
| ├── tasks.json tasks: task_id, instruction, start_page, initial_state |
| ├── evaluators/ frozen checkpoint / state evaluator plans and scripts |
| ├── data/ provenance, host graph, state-kernel data (SQLite where used) |
| └── app/ generated pages, images, runtime scripts, instrumentation |
| train/ Train set: 2,048 task manifests (1,280 seed + 768 learner-guided) |
| test/ Test set: Trace2World-Bench, 1,044 sealed task manifests |
| ``` |
|
|
| ## Applications |
|
|
| All 50 applications provide construction traces and all 50 are reconstructed as |
| one environment each. Policy optimization uses tasks from the 40 policy-training |
| applications only; the ten held-out applications, one per domain, supply |
| construction traces but no seed, candidate, screening or RL-update task. |
|
|
| | Domain | Policy-training apps (40) | Held-out eval app (10) | |
| |---|---|---| |
| | Social & community | WeChat, QQ, Weibo, Xiaohongshu | Zhihu | |
| | News & short video | Kuaishou, Bilibili, Toutiao, Tencent News | Douyin | |
| | Long video & audio | iQIYI, Youku, NetEase Cloud Music, Kugou Music, Ximalaya | QQ Music | |
| | Commerce & resale | Taobao, JD, Xianyu, Dewu | Pinduoduo | |
| | Local services & food | Meituan, Dianping, Dingdong Maicai, KFC, Hema | Ele.me | |
| | Mobility, travel & tickets | Amap, Ctrip, Didi, Railway 12306, Damai | Taopiaopiao | |
| | Auto, housing & jobs | 58Tongcheng, Autohome, Dongchedi, BOSS Zhipin | Beike | |
| | Work, learning & search | Feishu, WPS Office, Baidu, DingTalk, Youdao Dictionary, Baidu Netdisk | Tencent Meeting | |
| | Payment & logistics | Alipay, SF Express | Cainiao | |
| | Health & fitness | Boohee Health | Keep | |
|
|
| Trace collection is an evidence quota fixed before construction: an application |
| with at least three independent service lines receives 50 traces, every other |
| application 20 (19 multi-service apps x 50 + 31 single-service apps x 20 = |
| 1,570). Trace2World-Bench (1,044 tasks: 256 entity replacements, 512 held-out |
| workflows, 100 cross-app tasks, 176 tasks from unseen apps) is one frozen |
| evaluation view over the environment portfolio; the 1,280 seed and 768 |
| learner-guided optimization tasks are drawn from the 40 policy-training apps. |
|
|
| All environments render with a 430x884 CSS viewport at device scale factor 2 |
| (860x1768 screenshots). Task instructions are in Chinese, matching the source |
| apps. |
|
|
| ## Integrity |
|
|
| `environments_manifest.json` lists the SHA-256 and size of every file under |
| `environments/`; `scripts/verify_environments.py` in the code repository |
| checks a download against it. The environments are frozen exactly as used for |
| the numbers reported in the paper; rewards come from the frozen checkpoint / |
| evidence contracts and the host does not rewrite or relax them. |
|
|
| ## Scope and terms |
|
|
| - The environments are reconstructed simulations of the applications' |
| user-facing behavior, not scraped copies, and connect to no real service. |
| Page content (names, listings, prices, addresses) is synthetic or was taken |
| from the source traces; it does not describe real accounts. |
| - Source traces were collected by consenting operators from accounts they |
| control, with consent covering research use and public release. Screenshots |
| are captured from public consumer apps. |
| - This dataset is released for non-commercial research on GUI agents and |
| reinforcement learning (CC BY-NC 4.0). Trademarks and copyrighted content |
| visible in the screenshots and page content remain the property of their |
| respective owners; application names identify the systems studied without |
| implying affiliation or endorsement. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{wang2026trace2worldreconstructingappworlds, |
| title={Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training}, |
| author={Zezhou Wang and Hanzhang Zhou and Panrong Tong and Quyu Kong and Pengxiang Li and Steven Hoi and Zhuzhong Qian and Yue Wang}, |
| year={2026}, |
| eprint={2610.30792}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2610.30792}, |
| } |
| ``` |
|
|
