PhoneWorld: Scaling Phone-Use Agent Environments
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917543212482560 |
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| author | Tang, Zhengyang Liu, Yuxuan Lai, Xin Li, Junyi Lyu, Pengyuan Jason Guo, Yiduo Fang, Zhengyao Ding, Yang Zhang, Yi Wang, Weinong Shen, Huawen Zhou, Xingran Wu, Liang Tang, Fei Fan, Sunqi Peng, Shangpin Ruan, Zheng Zhang, Anran Wang, Benyou Yan, Rui Wen, Ji-Rong Zhang, Chengquan Hu, Han |
| author_facet | Tang, Zhengyang Liu, Yuxuan Lai, Xin Li, Junyi Lyu, Pengyuan Jason Guo, Yiduo Fang, Zhengyao Ding, Yang Zhang, Yi Wang, Weinong Shen, Huawen Zhou, Xingran Wu, Liang Tang, Fei Fan, Sunqi Peng, Shangpin Ruan, Zheng Zhang, Anran Wang, Benyou Yan, Rui Wen, Ji-Rong Zhang, Chengquan Hu, Han |
| contents | A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Existing mobile-agent benchmarks have made important progress on evaluation, but they do not by themselves provide a scalable way to construct many new phone-use environments. We present PhoneWorld, a reusable pipeline that converts real GUI trajectories and screenshots into controllable phone-use environments, executable tasks, automatic verifiers, and training rollouts. Rather than hand-building one mobile benchmark at a time, PhoneWorld uses real trajectories to recover which screens matter, how screens connect, which interactions must change environment state, and which user goals admit automatic verification. From these signals, it builds runnable mock Android apps backed by read-only app content and mutable state, then derives executable tasks, rule-based verifiers, and training rollouts from the same environments. In its current instantiation, PhoneWorld covers 34 apps across 16 domains, spanning common consumer mobile behaviors such as search, browsing, shopping, booking, media, and social interaction. Under a fixed training budget, replacing 10K steps from an auxiliary AndroidWorld corpus in an AndroidWorld-based baseline with broad PhoneWorld supervision improves all four evaluation benchmarks at once, raising HYMobileBench by 17.7 points, AndroidControl by 6.0 points, AndroidWorld by 14.7 points, and PhoneWorld by 52.5 points. We then study two additional scaling questions: increasing the amount of PhoneWorld supervision strongly improves PhoneWorld performance, and under a fixed PhoneWorld budget, expanding app coverage yields even larger gains. Overall, PhoneWorld shifts the focus from building one mobile benchmark at a time to scaling the supply of phone-use environments themselves. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29486 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PhoneWorld: Scaling Phone-Use Agent Environments Tang, Zhengyang Liu, Yuxuan Lai, Xin Li, Junyi Lyu, Pengyuan Jason Guo, Yiduo Fang, Zhengyao Ding, Yang Zhang, Yi Wang, Weinong Shen, Huawen Zhou, Xingran Wu, Liang Tang, Fei Fan, Sunqi Peng, Shangpin Ruan, Zheng Zhang, Anran Wang, Benyou Yan, Rui Wen, Ji-Rong Zhang, Chengquan Hu, Han Computation and Language Artificial Intelligence Machine Learning A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Existing mobile-agent benchmarks have made important progress on evaluation, but they do not by themselves provide a scalable way to construct many new phone-use environments. We present PhoneWorld, a reusable pipeline that converts real GUI trajectories and screenshots into controllable phone-use environments, executable tasks, automatic verifiers, and training rollouts. Rather than hand-building one mobile benchmark at a time, PhoneWorld uses real trajectories to recover which screens matter, how screens connect, which interactions must change environment state, and which user goals admit automatic verification. From these signals, it builds runnable mock Android apps backed by read-only app content and mutable state, then derives executable tasks, rule-based verifiers, and training rollouts from the same environments. In its current instantiation, PhoneWorld covers 34 apps across 16 domains, spanning common consumer mobile behaviors such as search, browsing, shopping, booking, media, and social interaction. Under a fixed training budget, replacing 10K steps from an auxiliary AndroidWorld corpus in an AndroidWorld-based baseline with broad PhoneWorld supervision improves all four evaluation benchmarks at once, raising HYMobileBench by 17.7 points, AndroidControl by 6.0 points, AndroidWorld by 14.7 points, and PhoneWorld by 52.5 points. We then study two additional scaling questions: increasing the amount of PhoneWorld supervision strongly improves PhoneWorld performance, and under a fixed PhoneWorld budget, expanding app coverage yields even larger gains. Overall, PhoneWorld shifts the focus from building one mobile benchmark at a time to scaling the supply of phone-use environments themselves. |
| title | PhoneWorld: Scaling Phone-Use Agent Environments |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.29486 |