arXiv:2512.03042v3 Announce Type: replace-cross Abstract: We introduce PPTArena, a benchmark for PowerPoint editing that evaluates how agents modify real slides from natural-language instructions. Unlike benchmarks that rely on image-PDF renderings or text-to-slide generation, PPTArena features 100 decks with over 1,300 human-curated edits across 2,125 slides, spanning text, charts, animations, and professional master styles. Each edit pairs a ground-truth deck with a target rubric and is scored by two Vision-Language Model (VLM) judges: one rates instruction following from structural diffs, t
The proliferation of advanced Vision-Language Models (VLMs) and the increasing demand for automation in knowledge work are driving the need for benchmarks that reflect complex, real-world tasks like PowerPoint editing.
This benchmark is crucial for developing and evaluating AI agents capable of understanding and executing nuanced instructions in a common business application, pushing the frontier of autonomous productivity tools.
The introduction of PPTArena elevates the standard for evaluating AI agent performance on multimodal, instruction-following tasks, moving beyond simpler text or image-based benchmarks to complex document editing.
- · AI agent developers
- · Productivity software companies
- · Businesses adopting automation
- · Manual presentation designers (eventually)
- · AI teams using only simplified benchmarks
AI models will become more adept at interpreting and executing complex, multi-step instructions for document creation.
The development of highly autonomous agents capable of generating and refining professional-grade presentations will accelerate, diminishing the need for human intervention in this workflow.
The definition of 'white-collar work' will further evolve as AI agents automate an increasing range of sophisticated tasks, shifting human roles towards oversight and strategic direction.
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