We present Rodent-Bench, a novel benchmark designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to annotate rodent behaviour footage. We evaluate state-of-the-art MLLMs, including Gemini-2.5-Pro, Gemini-2.5-Flash and Qwen-VL-Max, using this benchmark and find that none of these models perform strongly enough to be used as an assistant for this task. Our benchmark encompasses diverse datasets spanning multiple behavioral paradigms including social interactions, grooming, scratching, and freezing behaviors, with videos ranging from 10 minutes to 35 minutes in length. We provide two benchmark versions to accommodate varying model capabilities and establish standardized evaluation metrics including second-wise accuracy, macro F1, mean average precision, mutual information, and Matthew's correlation coefficient. While some models show modest performance on certain datasets (notably grooming detection), overall results reveal significant challenges in temporal segmentation, handling extended video sequences, and distinguishing subtle behavioral states. Our analysis identifies key limitations in current MLLMs for scientific video annotation and provides insights for future model development. Rodent-Bench serves as a foundation for tracking progress toward reliable automated behavioral annotation in neuroscience research.
RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation
BraiNE Unit, Department of Cancer Research, Luxembourg Institute of Health（卢森堡健康研究所癌症研究部门BraiNE单位）；Bioinformatics and AI Unit, Department of Medical Informatics, Luxembourg Institute of Health（卢森堡健康研究所医学信息学部门生物信息学与人工智能单位）；Multiomics Data Science Research Group, Department of Cancer Research, Luxembourg Institute of Health（卢森堡健康研究所癌症研究部门多组学数据科学研究组）；Department of Radiology, Stanford University（斯坦福大学放射科）
FINER: MLLMs Hallucinate under Fine-grained Negative Queries
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Munich（海德堡-慕尼黑亥姆霍尔茨中心）；Google（谷歌）；LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎电信学院，巴黎理工学院）
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
Sun Yat-Sen University（中山大学）；Alibaba Group（阿里巴巴集团）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology（广东省消防科学与智能应急技术重点实验室）
Beyond RGB: Benchmarking and Enhancing MLLMs for Hyperspectral Image Understanding via Training-Free Reasoning Framework
Sun Yat-sen University（中山大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；China Agricultural University（中国农业大学）；Southwest Jiaotong University（西南交通大学）；Southwest University（西南大学）；National Supercomputing Center in Shenzhen（国家超级计算深圳中心）
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs
Fudan University（复旦大学）；MMLab, The Chinese University of Hong Kong（中大香港人工智能实验室）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）
Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
Wuhan University（武汉大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Harbin Institute of Technology（哈尔滨工业大学）
Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
Fudan University（复旦大学）；College of Future Information Technology（未来信息技术学院）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
本研究构建UAVQA-Bench基准，识别MLLM用于无人机图像理解的三类失效模式，提出含DSPE、CAIR、DAAS的无训练多智能体系统UAV-MAS，其32B版本在基准上准确率超Gemini 3 Pro 4.0个百分点
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning
Fudan University（复旦大学）；East China Normal University（华东师范大学）；Singapore Management University（新加坡管理大学）；Singapore University of Technology and Design（新加坡科技设计大学）
MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image
College of Computer, National University of Defense Technology（国防科技大学计算机学院）；School of Computer and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；CCNU（中国地质大学）
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学孙逸仙纪念医院）；Southern Medical University（南方医科大学）；Zhongshan Hospital, Fudan University（复旦大学中山医院）；Department of Neurosurgery, Prince of Wales Hospital（威尔士王室医院神经外科部）
M-A-P；Interdisciplinary Centre for Security, Reliability and Trust (SnT), Université du Luxembourg（卢森堡大学安全、可靠性与跨学科中心（SnT））；University of Manchester（曼彻斯特大学）；Zhejiang University（浙江大学）；The University of New South Wales（新南威尔士大学）；USTC（中国科学技术大学）；Dartmouth College（达特茅斯学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；NTU（南洋理工大学）
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；WeChat AI, Tencent Inc., China（微信AI，腾讯公司，中国）
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；Zhejiang University（浙江大学）
MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）；Baidu Inc.（百度公司）；University of California（加州大学）；University of Science and Technology of China（中国科学技术大学）
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Shenzhen Institutes of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；M-A-P；IIT Kharagpur（印度理工学院克勒格布尔分校）；Huazhong University of Science and Technology（华中科技大学）；Yokohama National University（横滨国立大学）；Shenzhen University（深圳大学）；Zhejiang University（浙江大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Beihang University（北京航空航天大学）；Dartmouth College（达特茅斯学院）；University of Waterloo（滑铁卢大学）
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Huazhong University of Science and Technology（华中科技大学）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；University of Science and Technology of China（中国科学技术大学）；CDUT（成都理工大学）；University of Memphis（孟菲斯大学）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；SICAU（四川农业大学）；University of Manchester（曼彻斯特大学）；SWU（西南大学）；UCAS（中国科学院大学）；UMich（密歇根大学）
Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一学院计算机科学与统计学院）；Department of Mathematics and Computer Science, Eindhoven University of Technology（埃因霍温理工大学数学与计算机科学系）
