Large Vision-Language Models (VLMs) have demonstrated significant potential on complex visual understanding tasks through iterative optimization methods.However, these models generally lack effective self-correction mechanisms, making it difficult for them to independently rectify cognitive biases. Consequently, during multi-turn revisions, they often fall into repetitive and ineffective attempts, failing to achieve stable improvements in answer quality.To address this issue, we propose a novel iterative self-correction framework that endows models with two key capabilities: Capability Reflection and Memory Reflection. This framework guides the model to first diagnose errors and generate a correction plan via Capability Reflection, then leverage Memory Reflection to review past attempts to avoid repetition and explore new solutions, and finally, optimize the answer through rigorous re-reasoning. Experiments on the challenging OCRBench v2 benchmark show that OCR-Agent outperforms the current open-source SOTA model InternVL3-8B by +2.0 on English and +1.2 on Chinese subsets, while achieving state-of-the-art results in Visual Understanding (79.9) and Reasoning (66.5) - surpassing even larger fine-tuned models. Our method demonstrates that structured, self-aware reflection can significantly enhance VLMs' reasoning robustness without additional training. Code: https://github.com/AIGeeksGroup/OCR-Agent.
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Large Language Model Department, Tencent（腾讯大语言模型部）；Nankai University（南开大学）
研究改进轻量级端到端OCR视觉语言模型。基于HunyuanOCR-1.0架构，用DFlash提升效率，提出Agentic Data Flow增强能力，在多任务表现出色，还将发布模型权重和代码。
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Lanzhou University（兰州大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Jinan University（暨南大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）
Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation
MAIS,Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所马克思主义学院）；Tsinghua University（清华大学）；Shanghai Theatre Academy（上海戏剧学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National Cheng Kung University（国立成功大学）；University of Konstanz（康斯坦茨大学）
A Survey on Agentic Multimodal Large Language Models
Nanyang Technological University, Singapore（南洋理工大学，新加坡）；Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；Shenzhen Research Institute of Big Data, China（大数据研究 institute）；Sun Yat-sen University, China（中山大学）；City University of Hong Kong, China（香港城市大学）；Communication University of China, China（通信大学）
InsightX Agent: An LMM-based Agentic Framework with Integrated Tools for Reliable X-ray NDT Analysis
School of Physics and Astronomy, The University of Edinburgh（物理学与天文学学院，爱丁堡大学）；Glasgow College, University of Electronic Science and Technology of China（电子科技大学成都学院）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（机械与电子工程学院，电子科技大学）；Department of Systems Engineering, City University of Hong Kong（系统工程系，香港城市大学）
PhotoFlow: Agentic 3D Virtual Photography Missions
Shanghai Jiao Tong University（上海交通大学）；Northeastern University（东北大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Cornell University（康奈尔大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan University（四川大学）
