arXiv:2606.06560v1 Announce Type: new Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation tools and as training environments for reinforcement learning. However, macOS remains underserved in this landscape: the only existing benchmark, macOSWorld, covers a narrow slice of first-party applications with simpler tasks, and runs on x86 virtual machines incompatible with Apple Silico
The rapid advancement of computer-use agents (CUAs) and the increasing complexity of their tasks necessitate more robust and comprehensive benchmarking environments, especially for specific operating systems like macOS.
This development addresses a critical gap in AI agent evaluation and training, potentially accelerating the development of more capable and versatile software automation that can operate across diverse computing environments.
The introduction of MacArena provides a standardized, online benchmarking environment for macOS, allowing for more comprehensive evaluation and training of AI agents beyond what was previously available.
- · AI agent developers
- · Apple
- · Software automation sector
- · Reinforcement learning researchers
- · Developers solely focused on x86 virtual machine compatibility
Improved benchmarking leads to more robust and capable computer-use agents.
More powerful agents could automate a wider range of complex tasks on macOS, impacting productivity software.
The enhanced capabilities of Mac-specific agents could influence Apple's strategic AI integration and ecosystem development.
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Read at arXiv cs.LG