[Paper] Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam?

Source: arXiv - 2606.10956v1

Overview

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 comprehensive practical-operation tasks across Word, Excel, and PowerPoint. Each task is scored on a 100-point rubric scale using 7,118 machine-gradable criteria, and Score Rate (SR) denotes the mean percentage of rubric points earned across these tasks. We benchmark 7 frontier LLMs and observe stark limitations: single-turn models score a maximum of 36.6%. A stronger agentic system with execution feedback, iterative repair, and broader Office automation access reaches 68.8%, but remains below the 95.5% community-reference score used as a scoring sanity check. Ultimately, our experiments demonstrate that despite recent advancements in code generation, achieving reliable fine-grained Office document automation remains a significant challenge for current code-generating LLM and agent systems.

Key Contributions

This paper presents research in the following areas:

• cs.AI
• cs.CL

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.AI.

Authors

• Tengchao Lv
• Dongdong Zhang
• Jiayu Ding
• Yilin Jia
• Yuzhong Zhao
• Yupan Huang
• Wenshan Wu
• Xiangyang Zhou
• Shaohan Huang
• Nan Yang
• Li Dong
• Lei Cui
• Furu Wei

Paper Information

• arXiv ID: 2606.10956v1
• Categories: cs.AI, cs.CL
• Published: June 9, 2026
• PDF: Download PDF