Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text reasoning to enhance both effectiveness and efficiency. However, existing evaluations rely on static difficulty labels and simplistic metrics, which fail to capture the dynamic nature of difficulty relative to varying model capacities. Consequently, they obscure the distinction between adaptive mode selection and general performance while neglecting fine-grained process analyses. In this paper, we propose AdaptMMBench, a comprehensive benchmark for adaptive multimodal reasoning across five domains: real-world, OCR, GUI, knowledge, and math, encompassing both direct perception and complex reasoning tasks. AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the selection rationality of different reasoning modes, isolating this meta-cognition ability by dynamically identifying task difficulties based on models' capability boundaries. Moreover, AdaptMMBench facilitates multi-dimensional process evaluation across key step coverage, tool effectiveness, and computational efficiency. Our evaluation reveals that while adaptive mode selection scales with model capacity, it notably decouples from final accuracy. Conversely, key step coverage aligns with performance, though tool effectiveness remains highly inconsistent across model architectures.
EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Ability
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China（计算机科学与技术系，人工智能研究院，清华大学，北京，中国）；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（人工智能产业研究院（AIR），清华大学，北京，中国）；School of Management, Fudan University, Shanghai, China（管理学院，复旦大学，上海，中国）
MMSpec: Benchmarking Speculative Decoding for Vision-Language Models
University of Michigan（密歇根大学）；The Ohio State University（俄亥俄州立大学）；Independent（独立）；Indiana University（印第安纳大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；LMSYS Org（LMSYS组织）
MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nankai University（南开大学）；Beijing Institute of Technology（北京理工大学）；Baichuan Inc.（百度文心）
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Tsinghua University（清华大学）；Jilin University（吉林大学）；Northwest Minzu University（西北民族大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
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）
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
College of Computer Science, Chongqing University（重庆大学计算机学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Kuaishou Technology（快手科技）
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
Renmin University of China（中国人民大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chinese University of Hong Kong（香港中文大学）；Johns Hopkins University（约翰霍普金斯大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）
SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tencent Yuanbao（腾讯元宝）；Tsinghua University（清华大学）；ARC Lab, Tencent（腾讯ARC实验室）；The University of Hong Kong（香港大学）
Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
University of Liverpool（利物浦大学）；University of Exeter（埃克塞特大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanyang Technological University（南洋理工大学）
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）
UC San Diego（加州大学圣地亚哥分校）；Toyota Collaborative Safety Research Center（丰田协作安全研究中心）
Recent progress in autonomous and semi-autonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we pro…
