VLM-SlideEval: Evaluating VLMs on Structured Comprehension and Perturbation Sensitivity in PPT

Vision-language models (VLMs) are increasingly used to evaluate multimodal content, including presentation slides, yet their slide-specific understanding remains underexplored \emph{despite their growing role as critics in agentic, model-forward pipelines}.We introduce \textbf{VLM-SlideEval}, an evaluation framework that probes VLMs along three axes: (1) element-level extraction from slide images aligned to ground truth; (2) robustness to controlled perturbations in geometry, style, and text; and (3) higher-level comprehension, such as recovering a deck's narrative order from shuffled slides.Using publicly available decks from Zenodo\footnote{\scriptsize{\url{https://zenodo.org}; HF viewer: \hfurl}}, we standardize ground-truth element metadata from PowerPoint XML and live renderings into a unified, verifiable schema.Empirically, VLMs underperform on pixel-accurate extraction and show non-trivial agreement, fidelity and consistency under controlled perturbations, while performing better on single-slide content understanding; however, they do not reliably capture narrative structure across slides.These results highlight the limits of current VLMs for slide evaluation and motivate calibrated, critic-in-the-loop evaluators that drive iterative refinement and selection in agentic pipelines.