Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search-centric: answers that should require visual search are often leaked through cross-textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image-search side, the required information can often be obtained via near-exact matching against the full image, while the text-search side is overly direct and insufficiently challenging. To address these issues, we construct the Vision-DeepResearch benchmark (VDR-Bench) comprising 2,000 VQA instances. All questions are created via a careful, multi-stage curation pipeline and rigorous expert review, designed to assess the behavior of Vision-DeepResearch systems under realistic real-world conditions. Moreover, to address the insufficient visual retrieval capabilities of current MLLMs, we propose a simple multi-round cropped-search workflow. This strategy is shown to effectively improve model performance in realistic visual retrieval scenarios. Overall, our results provide practical guidance for the design of future multimodal deep-research systems. The code will be released in https://github.com/Osilly/Vision-DeepResearch.
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Alibaba Group（阿里巴巴集团）
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
Australian Institute for Machine Learning, University of Adelaide（澳大利亚机器学习研究所，阿德莱德大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Macquarie University（麦考瑞大学）；Hanoi University of Science and Technology（河内科学技术大学）；University of Wollongong（沃林根大学）；Flinders University（弗林德斯大学）
Gather and Trace: Rethinking Video TextVQA from an Instance-oriented Perspective
Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences Beijing China；School of Cyber Science；Engineering, Nanjing University of Science；VCIP \& TMCC \& DISSec, College of Computer Science, Nankai University Tianjin China；Key Laboratory of Ethnic Language Intelligent Analysis；Security Governance of MOE, Minzu University of China Beijing China；Institute of Information Engineering, Chinese Academy of Sciences；School of Cyber Security, University of Chinese Academy of Sciences；VCIP \& TMCC \& DISSec, College of Computer Science, Nankai University；Security Governance of MOE, Minzu University of China
WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室 (MAIS)）
LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning
School of AI, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Kling Team, Kuaishou Technology（快手科技 Kling 团队）；Peking University（北京大学）；HKUST（香港科技大学）；CASIA（中国科学院自动化研究所）；Nanjing University（南京大学）；Renmin University of China（中国人民大学）；Tsinghua University（清华大学）
本文提出LatentOmni框架，通过统一的音频-视觉潜在空间进行多模态推理，利用特征级监督和Omni-Sync Position Embedding保持时间一致性，从而在多个音频-视觉推理基准测试中取得最佳性能。
Rethinking Video-Language Model from the Language Input Perspective
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；University College London（伦敦大学学院）；Huazhong University of Science and Technology（华中科技大学）；Wuhan University（武汉大学）
Position: Reasoning After Perception Means Reasoning Without Vision
Tsinghua University（清华大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Zhongguancun Academy（中关村学院）；Nanjing University（南京大学）；Nankai University（南开大学）
EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部长春符号计算与知识工程重点实验室）；Yangtze University（扬子大学）
Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers
The University of Hong Kong（香港大学）；Nanjing University（南京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）
