Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched. A multimodal large language model (MLLM), shown the same pair, can articulate those attributes and use them to predict whether the images share a class. We propose \textbf{SAGA}, a framework that turns this language-grounded, attribute-aware perception into a training signal for the encoder itself. Specifically, we use Group Relative Policy Optimization (GRPO) to reward the MLLM for correct predictions on the vision encoder's tokens. Since correct predictions require those tokens to expose the specific attributes that differ or match between the pair, the gradient pushes the encoder to encode them, replacing the uniform pair-level scalar with attribute-resolved supervision. An auxiliary attention-distillation loss anchors the encoder's embedding to tokens the MLLM attended to, and a standard metric-learning loss shapes the embedding geometry for nearest-neighbour retrieval. The MLLM is frozen throughout and discarded at inference, matching the deployment cost of a metric-learning baseline. SAGA improves Recall@1 by 3 to 6 points over state-of-the-art baselines on CUB-200-2011, Cars-196, FGVC-Aircraft, and iNaturalist Aves on zero-shot image retrieval.
From What to Which: Decoding Modifier Grounding in Frozen MLLMs
AI for Good (AIGO)（人工智能促进美好（AIGO））；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Siena（锡耶纳大学）；University of Genoa（热那亚大学）；University of Verona（维罗纳大学）
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Nanjing University（南京大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs
Shenzhen University（深圳大学）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）；Sun Yat-sen University（中山大学）
Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Princeton Center for Information and Technology Policy（普林斯顿信息与技术政策中心）
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）
VAP-Diffusion: Enriching Descriptions with MLLMs for Enhanced Medical Image Generation
College of Biomedical Engineering, Fudan University, Shanghai 200433, China（复旦大学生物医学工程学院）；Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200032, China（上海医学影像计算与计算机辅助干预重点实验室）
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Baidu Inc.（百度公司）；The University of Sydney（悉尼大学）；Amazon（亚马逊公司）；The Chinese University of Hong Kong（香港中文大学）
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tencent（腾讯）；Ningbo Global Innovation Center, Zhejiang University（宁波全球创新中心，浙江大学）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字智能服务技术重点实验室）
CoVFT: Context-aware Visual Fine-tuning for Multimodal Large Language Models
State Key Laboratory of Complex and Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
School of Traffic and Transportation, Beijing Jiaotong University（交通与运输学院，北京交通大学）；Foundation Modal Research Center, Institute of Automation, Chinese Academy of Sciences（基础模态研究中心，自动化研究所）；Queen Mary School Hainan, Beijing University of Posts and Telecommunications（海南女王学院，北京邮电大学）；Department of Computer Science, NUS School of Computing（计算机科学系，国立大学计算机学院）
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）
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Al-Ahliyya Amman University（阿赫利耶阿曼大学）；Queensland University of Technology（昆士兰科技大学）
CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
Hefei University of Technology（合肥工业大学）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China（民族语言智能分析与安全治理国家重点实验室，中央民族大学）；School of Information Engineering, Minzu University of China（中央民族大学信息工程学院）
Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Saarland University（萨尔兰大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Cambridge（剑桥大学）
