Although multimodal large language models (MLLMs) excel in high-level vision-language reasoning, they lack inherent awareness of visual saliency, making it difficult to identify key visual elements. To bridge this gap, we propose Saliency-R1, the first unified MLLM framework that jointly tackles three representative and heterogeneous saliency tasks: Salient Object Detection (SOD), Salient Instance Segmentation (SIS), and Co-salient Object Detection (CoSOD), enhancing the model's capacity for saliency reasoning. We introduce a textual interface with structured tags (<rg>, <ins>) to encode region- and instance-level referring expressions, enabling a single referring segmenter to produce task-appropriate masks. To train the MLLM efficiently, we propose Confidence-Guided Policy Optimization (CGPO), a novel single-sample reinforcement learning algorithm. CGPO improves on GRPO by replacing group-normalized advantages with a per-sample signal based on reward-confidence discrepancy, thereby reducing computational waste, mitigating signal dilution, and lowering training overhead. Our model exceeds or matches the performance of robust open/closed-source MLLMs and specialized state-of-the-art methods across all three tasks, demonstrating the efficacy of our framework in saliency reasoning.
DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry
Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University（南方医科大学深圳口腔医院（平山））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University（北京大学膜生物学国家重点实验室、北京心代谢分子医学重点实验室、分子医学研究院、国家生物医学成像中心、未来技术学院）；Freedom AI；Division of Applied Oral Sciences & Community Dental Care Faculty of Dentistry, The University of Hong Kong（香港大学牙科学院应用口腔科学与社区牙科护理系）；Beijing Institute of Collaborative Innovation（北京协同创新研究院）；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）；Shenzhen Loop Area Institute（深圳河套学院）；Shenzhen Institute of Big Data（深圳大数据研究院）
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
Nanyang Technological University（南洋理工大学）；ByteDance（字节跳动）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；The University of Sydney（悉尼大学）
ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
School of Future Technology, Dalian University of Technology（大连理工大学未来技术学院）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）；OPPO Research Institute（OPPO研究院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
Perception-R1: Advancing Multimodal Reasoning Capabilities of MLLMs via Visual Perception Reward
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；iFLYTEK AI Research（iFLYTEK人工智能研究院）；Hong Kong University of Science and Technology（香港科学与技术大学）
From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（智能信息处理重点实验室，计算技术研究所，中国科学院（ICT/CAS））；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（人工智能安全国家重点实验室，计算技术研究所，中国科学院（ICT/CAS））；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs
Eastern Institute of Technology（东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Uppsala University（乌普萨拉大学）；University of Science and Technology of China（中国科学技术大学）
SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs
University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Institute for Advanced Study（深圳先进研究 institute）；CFAR, Agency for Science, Technology and Research (A*STAR)（科技研究局（A*STAR）认知与人工智能研究中心）；IHPC, Agency for Science, Technology and Research (A*STAR)（科技研究局（A*STAR）人工智能中心）
PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences（智能机器研究所，合肥物理科学研究院，中国科学院）；University of Science and Technology of China（中国科学技术大学）；Zhongke Hefei Institute of Technology Innovation Engineering（中科合肥技术创新工程研究院）
Blink: Dynamic Visual Token Resolution for Enhanced Multimodal Understanding
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Baidu Inc（百度公司）
ControlThinker: Unveiling Latent Semantics for Controllable Image Generation through Visual Reasoning
Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University（上海智能信息处理关键实验室，复旦大学计算机学院）；Shanghai Collaborative Innovation Center on Intelligent Visual Computing（上海智能视觉计算协同创新中心）；MiniMax
