Multimodal Large Language Models (MLLMs) have achieved remarkable performance by integrating powerful language backbones with large-scale visual encoders. Among these, latent Chain-of-Thought (CoT) methods enable implicit reasoning in continuous hidden states, facilitating seamless vision-language integration and faster inference. However, existing heuristically predefined supervision signals in latent CoT provide limited guidance for preserving critical visual information in intermediate latent states. To address this limitation, we propose CrystaL (Crystallized Latent Reasoning), a single-stage framework with two paths to process intact and corrupted images, respectively. By explicitly aligning the attention patterns and prediction distributions across the two paths, CrystaL crystallizes latent representations into task-relevant visual semantics, without relying on auxiliary annotations or external modules. Extensive experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines, achieving substantial gains in fine-grained visual understanding while maintaining robust reasoning capabilities.
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
Xidian University（西安电子科技大学）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Science and Technology of China（中国科学技术大学）
Find Them All: Unveiling MLLMs for Versatile Person Re-identification
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究所）；Macao Polytechnic University（澳门 polytechnic 大学）；Shanghai AI Laboratory（上海人工智能实验室）
GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究院）；School of Humanities, Shanghai Jiao Tong University（上海交通大学人文学院）；Shanghai AI Laboratory（上海人工智能实验室）
In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting
State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University（虚拟现实技术与系统国家重点实验室，北京航空航天大学计算机科学与工程学院）；College of AI, Tsinghua University（清华大学人工智能学院）
Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
Meituan（美团）；Shanghai Jiao Tong University（上海交通大学）；Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
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）
Can a Large Language Model Assess Urban Design Quality? Evaluating Walkability Metrics Across Expertise Levels
Singapore-ETH Centre, Future Cities Lab Global Programme, CREATE campus（新加坡-ETH中心，未来城市实验室全球计划，CREATE校区）；National University of Singapore（新加坡国立大学）；Takenaka Corporation（Takenaka公司）；Department of Architecture, National University of Singapore（新加坡国立大学建筑系）
Benchmarking the Thinking Mode of Multimodal Large Language Models in Clinical Tasks
Bytedance（字节跳动）；Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；Stanford University（斯坦福大学）；University of Michigan（密歇根大学）
University of Chinese Academy of Sciences（中国科学院大学）；New Laboratory of Pattern Recognition (NLPR), CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Shandong University（山东大学）
