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:2610.12230v1 Announce Type: cross
Abstract: Text prompting is an indirect interface for poster generation, requiring users to encode inherently two-dimensional composition intent into a one-dim...
By Yitong Wang, Fangyun Wei, Jinjing Zhao, Sirui Zhang, Hongyang Zhang, Dong Chen, Bo Dai, Yan Lu
arXiv:2608. 16289v3 Announce Type: replace Abstract: Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs.
By Xiaoan Liu, Lichen Ma, Zipeng Guo, Yu He, Xiaoyan Su, Shaojie Guo, Jingling Fu, Xiaolong Fu, Hao Yang, Tongxuan Liu, Yu Guo, Fei Wang, Xinyi Liu, Yongjun Zhang, Junshi Huang
SlideLab is a training‑free, multi‑agent framework that generates scientific presentations directly from research papers. It first plans a coherent narrative, then iteratively builds and refines a shared slide deck using agents for content planning, visual generation, layout refinement, and grounding verification. In a blind human preference study, SlideLab outperformed both open‑source and commercial systems on 77% of papers while using about four times fewer inference tokens than the strongest open‑source baseline. The authors also introduce ConfArena, an audience‑oriented evaluation framework that simulates a conference room and assesses presentations slide by slide, matching human system rankings and detecting issues such as falsified numbers, degraded figures, dropped slides, and shuffled slide order.
By Vidushee Vats, Karun Sharma, Yuxia Wang
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
By Kuangshi Ai, Patrick Phuoc Do, Chaoli Wang
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a single large language model, yet these approaches often expose them to only a limited range of perspectives and directions.
arXiv:2608. 03283v1 Announce Type: new Abstract: Identifying promising scientific ideas remains an important challenge in research practice.
By Zhiyao Cui, Qianyi Wang, Haoyang Yan, Yiqun Zhang, Siyue Ren, Hangfan Zhang, Zelin Tan, Hao Li, Chunjiang Mu, Dexian Cai, Shao Zhang, Chen Zhang, Meng Li, Jianan Chai, Yuting Fan, Zichao Ye, Xiaolei Yang, Xinyao Lu, Yuyang Yu, Wenjie Lou, Xiaosong Wang, Fenghua Ling, Shiyang Feng, Mao Su, Qiaosheng Zhang, Bo Zhang, Yang Chen, Lei Bai, Shuyue Hu
arXiv:2510. 19600v2 Announce Type: replace-cross Abstract: In the quest for scientific progress, communicating research is as vital as the discovery itself.
By Qianli Ma, Siyu Wang, Yilin Chen, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, Zhipeng Zhang
The paper introduces CREW, a collaborative multi‑agent reinforcement learning framework that automates the generation of the Related Work Section in research papers. Unlike previous methods that follow a fixed workflow, CREW allows large language model agents to dynamically select actions—Retrieve, Disseminate, Compose, and Critique—guided by a policy trained with Independent Proximal Policy Optimization. Experiments on a standard benchmark show that CREW improves output quality and reduces token usage compared to strong baselines.
By Hai-Dang Dang, Bao-Yen Pham, Bao Nguyen, Tran Thi Huong, Huynh Thi Thanh Binh
arXiv:2601. 04390v2 Announce Type: replace Abstract: High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create.
By Siyuan Huang, Yifan Zhou, Yutong Gao, Zi Yin, Juyang Bai, Xinxin Liu, Rama Chellappa, Chun Pong Lau, Cheng Peng, Sayan Nag, Shraman Pramanick
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.
By Yasheng Sun, Zezi Zeng, Yifan Yang, Chong Luo, Wenyi Wang, Ziwei Liu, J\"urgen Schmidhuber
arXiv:2606. 00370v1 Announce Type: cross Abstract: Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations.
By Astrid van den Brandt, Kiroong Choe, Sehi L'Yi, Devin Lange, Nils Gehlenborg