Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields
arXiv:2606.11042v1 Announce Type: new Abstract: Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple applications, and short-horizon tasks, leaving it largely unknown whether modern agents can follow user instructions to autonomously operate domain-specific
The rapid advancement of AI models necessitates more robust and long-horizon evaluation methods to understand their real-world capabilities and limitations in complex tasks.
Evaluating AI agents across diverse, domain-specific, and long-horizon professional tasks is crucial for determining their true utility and for advancing beyond current general-purpose benchmarks.
The focus of AI agent evaluation is starting to shift from simple benchmarks to complex, real-world professional workflows, highlighting the need for more sophisticated testing environments like Workflow-GYM.
- · AI agent developers
- · Automation software companies
- · Businesses adopting AI agents
- · Productivity software
- · Manual white-collar workflow providers
- · Companies with outdated AI evaluation methods
Improved evaluation leads to more capable and reliable AI agents for professional tasks.
Widespread adoption of advanced AI agents could significantly restructure white-collar industries and job functions.
The ability of AI to autonomously operate complex professional software could lead to new forms of organizational structures and economic efficiencies.
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Read at arXiv cs.AI