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.
By Michael Ofengenden, Yunze Man, Ziqi Pang, Liang-Yan Gui, Yu-Xiong Wang
SLIDEFORGE is a new AI agent designed for controllable editing of presentation slides. It constructs a Deck State Graph that links visual decomposition, native PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose an evaluation framework that jointly measures component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms existing prompting, screenshot‑based, and generic code‑agent baselines.
By Haozhen Zheng, Fulin Wang, Tianhu Xiong, Yingjie Yu, Shengyi Qian, Hanchao Yu, Alex Schwing, Klara Nahrstedt, Mingyuan Wu
SLIDEFORGE is an LLM‑driven agent designed for controllable editing of slide decks while preserving layout, style, component structure, and native editability. It constructs a Deck State Graph that links visual decomposition, PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose a comprehensive evaluation framework measuring component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms direct prompting, screenshot‑based agents, and generic code‑agent baselines.
arXiv:2610.14253v2 Announce Type: replace
Abstract: Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing....
By Arman Zarei, Sayan Nag, Bryan Wang, Franck Dernoncourt, Varun Manjunatha, Nedim Lipka, Soheil Feizi, Samyadeep Basu
PPTBench is a new benchmark that tests coding agents’ ability to reconstruct scientific flow diagrams from arXiv papers into editable PowerPoint slides. The dataset contains 500 tasks, each requiring agents to produce a single PPTX page with native, editable objects, and a four‑stage Agentic Judge evaluates validity, semantic correctness, rendering quality, and fine‑grained visual quality. Across 31 model configurations, the best score is 67.80, with a median of 19.47, showing that while agents can generate valid PPTX files, they still struggle with semantic and visual accuracy, especially text details.
By Xiaoqiu Wang, Yizhe Chi, Wenyi Li, Deyao Hong, Zhihan Shan, Mingju Gao, Kaisen Yang, Youjie Zheng, Calvin Xiao, Qinhuai Na
ReDeck introduces a step‑level render‑grounded refinement framework for document‑to‑slide generation, breaking slide revision into atomic edit actions with immediate renderer‑derived observations. It employs multi‑granular feedback—step‑level spatial checks, turn‑level adaptive critique, and a submission‑level layout gate—to balance local repair with overall quality. The authors also present DeckQuiz, a benchmark that separates content fidelity, spatial correctness, and design quality, and demonstrate ReDeck’s superior performance across GPT‑5.4, Claude‑4.6, and Gemini‑3.1.
By Muzhao Tian, Zezi Zeng, Yifan Yang, Xin Gao, Yan Li, Zisu Huang, Xiaohua Wang, Changze Lv, Mingxi Cheng, Bei Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Xiaoqing Zheng, Ji Li, Chong Luo