RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents
RecreationWorld benchmarks hybrid computer-use agents that rediscover and reimplement apps across five platforms.
RecreationWorld is a five-platform framework for hybrid computer-use agents that interleave GUI exploration, coding, and visual verification. Given a running reference application and no prescribed workflow, an agent must discover its behavior and build a faithful implementation, with the reference acting as an oracle for hidden tests. RecreationBench holds out 250 tasks across domains and platforms; GPT-6 Astra leads at 58.1% overall but passes all programmatic tests on only 2.8% of tasks. Models trained on trajectories from open-source apps improve on five out-of-distribution coding and hybrid computer-use benchmarks. The benchmark, environments, and test suites are released.
- RecreationWorld covers Ubuntu, macOS, Windows, Android, and Web with GUI and coding tools.
- Agents must rediscover a running reference and reimplement it without a prescribed workflow.
- RecreationBench contains 250 held-out tasks with programmatic and visual assertions.
- GPT-6 Astra scores 58.1% overall but passes every programmatic test on only 2.8% of tasks.
- Models trained on generated trajectories improve on five out-of-distribution benchmarks.
- Products
- RecreationWorldRecreationBench
- AI models
- GPT-6 Astra
From the original article
1 min read at arxiv.orgComputer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface, implement software, and run and visually verify their artifacts.
By Shuai Bai · arxiv.org
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