Despite significant costs from retrieving and processing high-fidelity visual inputs, most multimodal vision-language systems operate at fixed fidelity levels. We introduce VOILA, a framework for Value-Of-Information-driven adaptive fidelity selection in Visual Question Answering (VQA) that optimizes what information to retrieve before model execution. Given a query, VOILA uses a two-stage pipeline: a gradient-boosted regressor estimates correctness likelihood at each fidelity from question features alone, then an isotonic calibrator refines these probabilities for reliable decision-making. The system selects the minimum-cost fidelity maximizing expected utility given predicted accuracy and retrieval costs. We evaluate VOILA across three deployment scenarios using five datasets (VQA-v2, GQA, TextVQA, LoCoMo, FloodNet) and six Vision-Language Models (VLMs) with 7B-235B parameters. VOILA consistently achieves 50-60% cost reductions while retaining 90-95% of full-resolution accuracy across diverse query types and model architectures, demonstrating that pre-retrieval fidelity selection is vital to optimize multimodal inference under resource constraints.
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Lanzhou University（兰州大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Jinan University（暨南大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）
Parameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering
Computer Science Department, Morgan State University（莫尔甘州大学计算机科学系）；International Organization for Migration (IOM)（国际移民组织）；Electrical & Computer Engineering Department, Morgan State University（莫尔甘州大学电气与计算机工程系）
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
School of Computer Science, Peking University（北京大学计算机学院）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）；National Research and Engineering Center of Software Engineering, Peking University（北京大学国家软件工程研究与工程中心）
CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（广东省新型安全智能技术重点实验室，网络空间安全研究院，计算机科学与技术学院，哈尔滨工业大学（深圳））
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Yale University（耶鲁大学）；Korea University（韩国大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；The University of Queensland（昆士兰大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；University of Washington（华盛顿大学）；Microsoft Research（微软研究院）
Variational Fusion for Multimodal Sentiment Analysis
A*STAR AI Initiative, Institute of High Performance Computing（A*STAR人工智能计划、高性能计算研究院）；Adobe Research（奥多比研究院）；University of Michigan（密歇根大学）；Centro de Investigación en Computación, Instituto Politécnico Nacional（国立理工学院计算研究中心）；SUTD（新加坡科技设计大学）
Taming a Retrieval Framework to Read Images in Humanlike Manner for Augmenting Generation of MLLMs
Emory University（埃默里大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Illinois Chicago（伊利诺伊大学香槟分校）；The Hong Kong Polytechnic University（香港理工大学）
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital（慕尼黑技术大学医院）；German Heart Center TUM University Hospital（慕尼黑技术大学医院德国心脏中心）；Department of Radiology（放射科）；Klinikum rechts der Isar TUM University Hospital（慕尼黑技术大学医院右岸诊所）；Uniklinik RWTH Aachen（亚琛工业大学医院）；HOPPR IL USA（HOPPR美国）；University of Oxford（牛津大学）；Imperial College London（伦敦帝国学院）
Scene Graph-Guided Generative AI Framework for Synthesizing and Evaluating Industrial Hazard Scenarios
Ph.D. Student, Dept. of Civil Eng., University of Texas at Arlington. E-mail；Assistant Professor, Dept. of Computer Sci. \& Eng., University of Texas at Arlington. E-mail；Assistant Professor, Dept. of Civil Eng., University of Texas at Arlington. E-mail
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
Massachusetts Institute of Technology（麻省理工学院）；Havard Univeristy（哈佛大学）；LG CNS；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Google（谷歌）；Stanford University（斯坦福大学）
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Beijing Institute of Technology, Beijing, China（北京理工大学）；National University of Singapore, Singapore（新加坡国立大学）；Tsinghua University, Beijing, China（清华大学）；The Hong Kong Polytechnic University, Hong Kong（香港理工大学）
Purdue University（普渡大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California Los Angeles（加州大学洛杉矶分校）；University of Connecticut（康涅狄格大学）；University of California Berkeley（加州大学伯克利分校）
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Capital One（第一资本）；Stanford University（斯坦福大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；KAIST（韩国科学技术院）；Brown University（布朗大学）；NAIST（奈良先端科学技术大学院大学）；UC Berkeley（加州大学伯克利分校）；SAS Posos；Oracle（甲骨文公司）；ITB（万隆理工学院）；The Hong Kong Polytechnic University（香港理工大学）
