OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action sequences. To solve the problem, we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment's unit functions. The agent then exploits compositionality between unit skills to self-construct a curriculum for composite task...
| Authors | Jiateng Liu, Zhenhailong Wang, Rushi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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