Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task.
arXiv:2606. 08063v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions.
By Jiaqi Tang, Jianmin Chen, Youyang Zhai, Wei Wei, Runtao Liu, Mengjie Zhao, Xiangyu Wu, Qingfa Xiao, Qifeng Chen
arXiv:2608.29374v1 Announce Type: new
Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...
By Yu Cheng, Arushi Goel, Hakan Bilen
arXiv:2609.00232v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmar...
By Yue Zhou, Yuan Wu, Yi Chang
arXiv:2606.03401v2 Announce Type: replace
Abstract: Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated conten...
By WenZhe Li, Qihang Yan, Liang Chen, Junying Wang, Farong Wen, Yijin Guo, Chunyi Li, Zicheng Zhang, Guangtao Zhai
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.
By Weixin Chen, Antonio Vergari, Han Zhao
arXiv:2608. 13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in an image), with limited attention to behavioral reliability under uncertainty (how they behave when visual evidence is missing or misleading).
By Paul Osemudiame Oamen, Owusu-Banahene Osei, Ananya Mukherjee, Christian Greisinger, Steffen Eger, Pius Onobhayedo, Wei Zhao
The paper presents a systematic study of scientific image synthesis, comparing pixel‑based generation and programmatic approaches. It introduces ImgCoder, a logic‑driven framework that follows an "understand‑plan‑code" workflow to enhance structural precision, and SciGenBench, a benchmark that evaluates images for information utility and logical validity. The authors find that pixel‑based models exhibit systematic failure modes and that fine‑tuning large multimodal models on rigorously verified synthetic images consistently improves downstream reasoning performance.
By Honglin Lin, Zheng Liu, Chonghan Qin, Qizhi Pei, Yu Li, Zhanping Zhong, Xin Gao, Yanfeng Wang, Conghui He, Lijun Wu
arXiv:2608.22174v1 Announce Type: new
Abstract: Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding...
By Yubo Zhu, Zhehan Kan, Jingyi Yang, Miaolin Chen, Jinbo Xing, Kai Zhu, Zijian Wang, Sheng Zhong, Wei Tong
arXiv:2506.09557v2 Announce Type: replace-cross
Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorat...
By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao
The paper introduces CAIT, a benchmark of 400 synthetic scenes featuring counter‑intuitive actions that challenge multimodal large language models (MLLMs). Human participants and proprietary models like Claude and Gemini perform well, but standard open‑source instruction‑tuned MLLMs fail, largely due to a strong language prior that overrides contradictory visual evidence. The study shows that Chain‑of‑Thought reasoning can help but introduces new issues, while targeted fine‑tuning and structured prompting can reduce reliance on language priors and improve visual grounding.
By Chen Ling, Tongwei Zhang, Hanqian Li, Nai Ding
arXiv:2607. 22600v1 Announce Type: new Abstract: Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios, inappropriate encodings, and misleading color mappings-can systematically alter interpretation while preserving the underlying data.
By Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman, Mir Tafseer Nayeem, Enamul Hoque