Same Tasks, Different Apps: Why Mobile GUI Agents Fail to Generalize?

Title:Same Tasks, Different Apps: Why Mobile GUI Agents Fail to Generalize?

Abstract:Mobile GUI agents deployed in real settings must work across different applications that support the same functionality. Most existing benchmarks test each task in only one app, so a high score can mean the agent understands the task, or only that it knows that particular app. We introduce AnyAppBench, a category-controlled live Android benchmark that evaluates cross-application generalization while keeping the user goal fixed. It spans 10 functional categories, 100 task templates, and 520 task--application pairs over 52 applications. Agents run from raw instructions and with app-independent sub-goals, and a VLM judge labels every failed run under a fixed failure taxonomy whose reliability is measured by human annotation. We find that, across 13 agents, success on the original application does not transfer reliably to new applications with the same goal. Furthermore, providing high-level sub-goal decomposition produces only small, category-dependent changes that do not close the gap, and the mix of failure types changes with the target interface. Based on those insights, we believe the AnyAppBench benchmark provides an important stepping stone toward robust real-world deployment of mobile GUI agents. Our code, data and the leaderboard can be found at the project website this https URL.

Accepted to Findings of EMNLP 2026. 30 pages

Artificial Intelligence (cs.AI)

arXiv:2609.34139 [cs.AI]

(or arXiv:2609.34139v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.34139 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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