PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks
Apurva Gandhi, Vishwas Suryanarayanan, Raja Anwar, Firoz Shaik, Shubhang Desai, Thong Nguyen, Muhammad Raza, Vishal Chowdhary, Graham Neubig
Abstract
Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agents. Microsoft Pow-erPoint is among the most widely adopted and feature-rich environments for presentation creation. We introduce PPT-EVAL, a benchmark of 120 PowerPoint tasks across 12 files that cover both content creation and presentation editing scenarios, organized by difficulty. A central challenge in this domain is evaluation: tasks are complex, multimodal, and often admit many valid solutions. Moreover, today's agents frequently make only partial progress, which binary success metrics fail to capture. To address this, we design a robust evaluation framework to help create task-specific rubrics for PowerPoint tasks, taking inspiration from and building on past works for rubric-based evaluation. These rubrics award partial credit for intermediate steps, penalize unnecessary changes and poor aesthetics, and provide natural language feedback. This nuanced approach proves highly effective, achieving a Kendall's τ b correlation of 0.77 with human judgments. We find that existing frontier agents still struggle with solving PowerPoint tasks, with strong models like Claude-4.5-Opus achieving only a 45% success rate and an average partial score of 57%. The benchmark repository is located at: https: //microsoft.github.io/ppteval .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 550a9b45-0498-4a84-9b97-db6488e41eaeBuilds on6
- OpenCUA: Open Foundations for Computer-Use AgentsXinyuan Wang, Bowen Wang, Dunjie Lu, Junlin Yang et al.NeurIPS 2025 · 151 citations
- Checklists Are Better Than Reward Models For Aligning Language ModelsVijay Viswanathan, Yanchao Sun, Xiang Kong, Meng Cao et al.NeurIPS 2025 · 127 citations
- Go-Browse: Training Web Agents with Structured ExplorationApurva Gandhi, Graham NeubigICLR 2026 · 30 citations
- LLM-Explorer: Towards Efficient and Affordable LLM-based Exploration for Mobile AppsShanhui Zhao, Hao Wen, Wenjie Du, Cheng Liang et al.MobiCom 2025 · 6 citations
- Windows Agent Arena: Evaluating Multi-Modal OS Agents at ScaleRogerio Bonatti, Dan Zhao, Francesco Bonacci, Dillon Dupont et al.ICML 2025
Related papers
- PPTAgent: Generating and Evaluating Presentations Beyond Text-to-SlidesHao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang et al.EMNLP 2025 · 3 citations
- GUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI TasksSaelyne Yang, Jaesang Yu, Yi-Hao Peng, Kevin Qinghong Lin et al.CVPR 2026 · 5 citations
- FeatureBench: Benchmarking Agentic Coding for Complex Feature DevelopmentQixing Zhou, Jiacheng Zhang, Haiyang Wang, Rui Hao et al.ICLR 2026 · 30 citations
- PSBench: Editing Image via GUI Agents in PhotoshopYinuo Zhang, Zian Cheng, Ziya Zhao, Zongyu Li et al.ICML 2026
- AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World ContextsKeyu Li, Junhao Shi, Yang Xiao, Mohan Jiang et al.ACL 2026 · 14 citations
