arXiv:2606.24551v1 Announce Type: new Abstract: Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with differences in tasks, initial states, verifiers, and permitted actions. We introduce a matched execution-layer benchmark of 440 desktop tasks across 18 applications and 12 workflow categories, where screen-only GUI agents and skill-mediated CLI agents receive identical goals, states, and final-state verifiers while being restricted to modality-native actions. In this con
The proliferation of AI agents necessitates robust, standardized benchmarks to understand their capabilities and limitations in real-world computer interaction, distinguishing modality effectiveness.
This benchmark directly addresses critical bottlenecks in AI agent performance, which is crucial for the efficient automation of complex white-collar tasks and the development of more capable autonomous systems.
The introduction of a matched benchmark allows for a clearer comparison between GUI-based and CLI-based agent performance, highlighting specific weaknesses and strengths for future development.
- · AI Agent developers
- · Automation software providers
- · Productivity software companies
- · Inefficient AI agent development approaches
- · Manual IT support processes
Improved understanding and optimization of AI agent interaction modalities will lead to more effective and reliable autonomous systems.
Faster adoption and deployment of AI agents in enterprise environments as their performance and reliability can be more accurately predicted and improved.
Significant reduction in certain types of human-computer interaction jobs as agents become more adept at complex, multi-application tasks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI