arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
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APPSim-Bench is a new benchmark for mobile GUI agents that uses controllable simulated apps to balance realism and reproducibility. It includes 557 tasks across 17 popular Chinese and English apps, with a coding-agent-assisted and human-verified workflow that ensures deterministic evaluation. Evaluation of 19 agents shows that autonomous mobile execution is still far from perfect, with the best model completing only 50.27% of tasks and many failures in longer workflows and numerical reasoning.
arXiv:2512. 12634v4 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction.
Mobile GUI agents can execute tasks from natural-language instructions, but their evaluation remains difficult to make both realistic and reproducible. Existing benchmarks typically trade off these go...
arXiv:2608. 15930v1 Announce Type: new Abstract: Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution.
arXiv:2609.22000v1 Announce Type: new Abstract: Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Re...
arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.