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.
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
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
EditPPT is a multi‑agent framework that turns slide editing into a constrained tool‑selection task, using the native PowerPoint COM interface to perform localized shape‑level operations. By separating validation across modalities, its dual‑modal validators assess both instruction fidelity and visual quality, achieving high execution and accuracy rates even on long decks. The authors also introduce DeckEdit‑Bench, a benchmark of 28 human‑authored decks with 582 slides and 183 editing prompts across varying deck lengths.
By Jiheon Kim, Kyudan Jung, Jaegul Choo
arXiv:2601.09487v2 Announce Type: replace
Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts...
By Yunqiao Yang, Wenbo Li, Houxing Ren, Zimu Lu, Ke Wang, Zhiyuan Huang, Zhuofan Zong, Mingjie Zhan, Hongsheng Li
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
SlideGen is a collaborative vision‑language multi‑agent framework designed to generate scientific presentation slides from research papers. It assigns specialized agents to outline the presentation structure, align figures and tables with key claims, generate speaker notes, and compose editable PPTX slides using a diverse layout library. The system introduces a geometry‑aware density metric to evaluate visual clutter and demonstrates significant improvements in layout balance, content coverage, and text coherence over existing baselines on a 200‑paper benchmark.
By Xin Liang, Zhilin Zhang, Xiang Zhang, Haoran Su, Yiwei Xu, Siqi Sun, Chenyu You
arXiv:2610.16784v1 Announce Type: cross
Abstract: Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic des...
By Jiahao Wang, Xiyan Jiang, Chenyuan Wu, Haoqiang Zhang, Xin Luo, Yueze Wang, Defu Lian, Bo Zhang, Jiajun Zhang, Zheng Liu
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
ReFigBench is a benchmark that evaluates how well multimodal coding agents can transform scientific overview figures into editable PowerPoint slides, preserving text, layout, and document structure. The study uses 1,000 real figures from arXiv, testing agents from four model families across two workflows—direct code generation and a specialized PPTX workflow—within ten different harness configurations. Evaluation combines deterministic artifact checks, automated scoring by judges, and blinded human comparisons, revealing that workflow and harness choices significantly affect reconstruction quality and that even the best agents fall short of the ideal rubric.
By Liyang Fan, Chi Wei, Yitai Li, Xinping Bi, Guhong Chen, Chenghao Sun, Haoxiang Yang, Qingwen Li, Kai Yan, Hong Li, Bo Li
arXiv:2608. 03298v1 Announce Type: new Abstract: Agentic presentation generation must preserve source content, maintain coherent visual design, render specialized objects, and produce usable artifacts.
By Shengjun Fang, Chenyang Wu, Zongzhang Zhang
The paper introduces WeGenEditBench, a benchmark with 2,740 evaluation instances and 148 task types for image editing, focusing on local failure detection and prioritization for model improvement. It also presents WeGenEvoStudio, a multi‑agent harness that supports recursive self‑improvement by diagnosing failures, constructing retrieval‑grounded data, and adapting models. Experiments show that models such as GPT‑Image‑2 and Seedream‑5.0‑Pro excel in single‑image and text‑intensive editing, and evolution experiments demonstrate significant targeted gains on selected benchmarks.
By Jun He, Canmiao Fu, Ying Zhang, Zhipeng Huang, Chenyu Mu, Yazhen Xie, Chen Li, Jing Lyu, Weijia Li, Xiang Zhang