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PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks

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Computer Science > Machine Learning

arXiv:2606.31154 (cs)
[Submitted on 30 Jun 2026]

Title:PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks

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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 PowerPoint 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 {\tau}-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 is located at: this https URL.
Comments: Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.31154 [cs.LG]
  (or arXiv:2606.31154v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.31154
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Apurva Gandhi [view email]
[v1] Tue, 30 Jun 2026 05:26:51 UTC (11,210 KB)
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