# Computer Use Agents

The work introduces OSWorld-Science, a benchmark and evaluation environment combining expert-defined scientific tasks, artifact-based execution graders, and a shared agent harness, and evaluates 12 vision-language models across 7 scientific domains, 17 software packages, and 3 languages, finding that the strongest model, Claude Fable 5.1, averages 73.7% and about 40% on the hardest task set, with failures concentrated in the interaction layer and result verification rather than in scientific reasoning itself.

The work introduces OSWorld-Science, a benchmark and evaluation environment combining expert-defined scientific tasks, artifact-based execution graders, and a shared agent harness, and evaluates 12 vision-language models across 7 scientific domains, 17 software packages, and 3 languages, finding that the strongest model, Claude Fable 5.1, averages 73.7% and about 40% on the hardest task set, with failures concentrated in the interaction layer and result verification rather than in scientific reasoning itself.

The authors built EngiWorld, a benchmark structured around the complete engineering design loop, with 1,301 expert-curated tasks across 6 domains (CAD, CAE, CAM, BIM, EDA, and 3D visualization) and 26 professional software platforms, evaluated through a unified domain-verifier suite that checks geometric validity, physical feasibility, and rule compliance of final and intermediate artifacts; across seven frontier models the strongest reaches an EngiScore of only 44.3, and just 3.6% of multi-software attempts succeed.

The authors built EngiWorld, a benchmark structured around the complete engineering design loop, with 1,301 expert-curated tasks across 6 domains (CAD, CAE, CAM, BIM, EDA, and 3D visualization) and 26 professional software platforms, evaluated through a unified domain-verifier suite that checks geometric validity, physical feasibility, and rule compliance of final and intermediate artifacts; across seven frontier models the strongest reaches an EngiScore of only 44.3, and just 3.6% of multi-software attempts succeed.

The work builds HybridCUA-8K, a data pipeline of 5,000 GUI-only, CLI-only, and interleaved GUI–CLI trajectories plus 3,000 verified RLVR tasks, and a two-stage framework of supervised fine-tuning followed by online reinforcement learning with CLI-aware rewards; the resulting HybridCUA-9B reaches 53.6% accuracy on OSWorld (14.8 points above its Qwen3.5-9B base, with average steps falling from 31.6 to 14.0) and 36.0% on WindowsAgentArena (a 4.0-point gain).

The work builds HybridCUA-8K, a data pipeline of 5,000 GUI-only, CLI-only, and interleaved GUI–CLI trajectories plus 3,000 verified RLVR tasks, and a two-stage framework of supervised fine-tuning followed by online reinforcement learning with CLI-aware rewards; the resulting HybridCUA-9B reaches 53.6% accuracy on OSWorld (14.8 points above its Qwen3.5-9B base, with average steps falling from 31.6 to 14.0) and 36.0% on WindowsAgentArena (a 4.0-point gain).