Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use, where models must connect visual artifacts to sourced claims and maintain consistency across narrative, citations, and visual references. We further propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity, each producing fine-grained signals that support error diagnosis beyond a single overall score. Experiments across 25 state-of-the-art models reveal systematic trade-offs between generation quality, citation discipline, and multimodal grounding, highlighting that strong prose alone does not guarantee faithful evidence use and that multimodal integrity remains a key bottleneck for deep research agents.
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture
Sun Yat-Sen University（中山大学）；Southwest University（西南大学）；Northeastern University（东北大学）；National Supercomputing Center in Shenzhen（深圳国家超算中心）；Southwest Jiaotong University（西南交通大学）；China Agricultural University（中国农业大学）；Tsinghua University（清华大学）
ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge
College of Computer Science, Sichuan University, Chengdu, China（四川大学计算机学院）；National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University, China（四川省工程仿真基础算法与模型国家重点实验室）
PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies
Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；Interdisciplinary Transformation University Austria（跨学科转型大学奥地利）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Stanford University（斯坦福大学）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）
SciVQR: A Multidisciplinary Multimodal Benchmark for Advanced Scientific Reasoning Evaluation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；OPPO AI Center（OPPO AI中心）
Alexa AI, Amazon Inc（亚马逊公司Alexa人工智能部门）；University of Southern California（南加州大学）
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol. Our experiments investigate the impact of different modalities on multi-agent learning performance. We also introduce a simple message passing me…
Microsoft（微软）；Microsoft Research Asia（微软亚洲研究院）；Southwest Jiaotong University（西南交通大学）；ShanghaiTech University（上海科技大学）
In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus on natural language tasks, GEM is a large-scale vision-language benchmark, which consists of GEM-I for image-language tasks and GEM-V for video-language tasks. Comparing with existing multimodal datasets such as MSCOCO and Flicker30K for image-language tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the …
MR$^2$-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；Renmin University of China（中国人民大学）；Baidu Inc., China（百度公司）
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents
School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；Alibaba Group, Hangzhou, China（阿里巴巴集团）
EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning
Tencent AI Lab（腾讯 AI 实验室）；The University of Hong Kong（香港大学）；ARC Lab, Tencent PCG（腾讯平台与内容事业群 ARC 实验室）；University of California, Berkeley（加州大学伯克利分校）；Peng Cheng Laboratory（鹏城实验室）
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
University of Waterloo（滑铁卢大学）；Georgia Institute of Technology（佐治亚理工学院）；Hong Kong University of Science and Technology（香港科技大学）；Google DeepMind（谷歌DeepMind）
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；The University of Adelaide（阿德莱德大学）；Zhejiang University（浙江大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
University of Electronic Science and Technology of China（电子科技大学）；Sun Yat-sen University（中山大学）；University of Washington（华盛顿大学）；Microsoft（微软公司）；The Chinese University of Hong Kong（香港中文大学）
本文提出覆盖数学、物理、化学和编程的 EMMA 多模态推理基准，揭示先进 MLLMs 在复杂跨模态、多步推理上仍存在明显不足。
MICON-Bench: Benchmarking and Enhancing Multi-Image Context Image Generation in Unified Multimodal Models
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中国教育部，厦门大学）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）
Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks
HKUST(GZ)（香港科技大学（广州））；HKUST（香港科技大学）；South China University of Technology（华南理工大学）；Shanghai Jiao Tong University（上海交通大学）；University of Pisa（比萨大学）；University of Trento（特伦托大学）
MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；College of Information Technology, United Arab Emirates University（阿拉伯联合酋长国大学信息学院）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）
