arXiv:2606.10956v1 Announce Type: cross Abstract: The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet their ability to navigate complex, professional-grade productivity software is largely untested. We argue that Office automation is an ideal environment for benchmarking document-automation capability, as it requires long-horizon planning and reasoning, precise parameter configuration, and multi-application integration. To quantify this capability, we introduce an evaluation based on China's National Computer Rank Examination (NCRE), featuring 200 c
The accelerating deployment of LLM agents for computer automation necessitates robust benchmarking to understand their practical limitations and capabilities in complex real-world scenarios.
This research provides a standardized method to evaluate LLM agent proficiency in professional-grade software, directly informing their readiness for large-scale enterprise automation and workflow transformation.
The explicit introduction of a standardized, multi-application proficiency exam for LLM agents provides a new, tangible benchmark for assessing the operational maturity and commercial viability of these AI systems.
- · AI agent developers
- · Productivity software providers
- · Enterprises adopting automation
- · Manual data entry roles
- · Inefficient workflow software
This benchmark will accelerate development of LLM agents capable of handling complex office tasks, leading to more sophisticated automation across various sectors.
Improved LLM agent proficiency could redefine the nature of administrative and knowledge work, shifting human roles towards oversight and higher-level strategic tasks.
Widespread adoption of highly proficient LLM agents could significantly boost white-collar productivity, impacting labor markets and potentially creating new economic models.
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Read at arXiv cs.CL