---
title: "MobileBench-OL — paper by Wu et al. (2026)"
description: "They rarely test autonomous exploration, long-term planning, or the messiness of real phones, such as ads, lag, and failed… 50 references."
authors: ["Qinzhuo Wu", "Zhizhuo Yang", "Hanhao Li", "Pengzhi Gao", "Wei Liu", "Jian Luan"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.668/"
doi: "10.18653/v1/2026.findings-acl.668"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-mobilebench-ol-comprehensive-chinese-benchmark/"
---
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# MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment

**Authors:** [Qinzhuo Wu](https://md.research.elamur.ai/free/en/authors/qinzhuo-wu/), Zhizhuo Yang, Hanhao Li, Pengzhi Gao, [Wei Liu](https://md.research.elamur.ai/free/en/authors/wei-liu/) and [Jian Luan](https://md.research.elamur.ai/free/en/authors/jian-luan/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.668/) · [PDF](https://aclanthology.org/2026.findings-acl.668.pdf) · [DOI 10.18653/v1/2026.findings-acl.668](https://doi.org/10.18653/v1/2026.findings-acl.668) · [OpenAlex](https://openalex.org/W7166810017)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Existing benchmarks for mobile GUI agents mostly check whether an agent can carry out simple or step-by-step instructions. They rarely test autonomous exploration, long-term planning, or the messiness of real phones, such as ads, lag, and failed actions. MobileBench-OL addresses this with an online benchmark of 1080 tasks from 80 Chinese apps. The apps run on real devices, so apps that need a login or use a lot of memory are included.

The tasks fall into five subsets covering three dimensions:
- basic skills (Base, Long-Tail),
- complex reasoning (Long-Horizon, GUI-Reasoning),
- robustness (Noise-Robust, where artificial disturbances are injected at random).

The authors also built an Auto-Eval framework that judges trajectories without manual work or system data. An instruction-based Reset Mechanism returns each device to its starting state so that runs can be repeated.

Twelve leading agents were tested, and all have clear weaknesses. Even the strongest, UI-TARS-1.5, completes only 15% of long-horizon tasks. Human checks show that Auto-Eval and the reset procedure are reliable, which makes the benchmark a credible way to measure real-world readiness.

## Key points

- **Coverage:** 1080 tasks across 80 apps in 12 App Store categories. The Long-Tail subset alone uses 68 less popular apps to test how well agents generalize to unfamiliar UIs.
- **Difficulty:** Levels are set by golden steps: Easy (\<8), Medium (8–19), Hard (≥20). GUI-Reasoning tasks are graded by an exploration weight instead. This subset covers icon understanding, hierarchical navigation, and hidden function discovery.
- **Noise types:** Noise-Robust uses four: Repeat, Unexecuted, Delay, and Pop-up. Pop-ups do the most damage, because agents often fail to locate the close icon.
- **Auto-Eval accuracy:** On UI-TARS-1.5 runs, Auto-Eval agrees with human judgment 97.50% of the time. The overall success rate is 49.91% by Auto-Eval versus 51.48% by humans, and false positives are rare.
- **Reset Mechanism:** Across 255 reset tasks, humans judged the Reset Mechanism successful in more than 90% of cases. The authors name the lack of cross-app tasks as a current limitation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Xiaomi](https://md.research.elamur.ai/free/en/companies/xiaomi/) — Company, Beijing, China · 14 works · 434 citations

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🧭 Topics (1): [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,341 works

## Cite

Cite as: Qinzhuo Wu, Zhizhuo Yang, Hanhao Li, Pengzhi Gao, Wei Liu and Jian Luan. 2026. MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026mobilebench,
  title = {{MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment}},
  author = {Qinzhuo Wu, Zhizhuo Yang, Hanhao Li, Pengzhi Gao, Wei Liu and Jian Luan},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.668},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.668}
}
```

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*Updated 2 November 2026*