Multimodal Large Language Models

Also known as MLLM

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67 papers in the last four weeks, up 91% on the four weeks before. 0.7% of all new papers.

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Group Preference Collapse in Personalized Multimodal Large Language Models
Jun 12, 2026Fan Lyu, Wenqi Zhang, Joost van de WeijerMultimodal Large Language Models Personalized Language Models
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain, 08193
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Jun 12, 2026Haonan Qi, Jin Cao, Yongqi Zhang +9Multimodal Large Language Models Multimodal Model Evaluation
Multimodal and Industrial AI TeamTaobao&Tmall, Alibaba Group
When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models
Jun 12, 2026Yinfeng Wang, Zhiyuan Yao, Zheren Fu +2VLM Robustness Multimodal Large Language Models
University of Science and Technology of China Hefei, China
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
Jun 11, 2026He Li, Haoang Chi, Qizhou Wang +6VLM Unlearning Multimodal Large Language Models
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · TMLR Group, Hong Kong Baptist University, Hong Kong, China · School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai, China +2
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
Jun 10, 2026Garvita Allabadi, Matteo Sodano, Roberto Estevão +4Multimodal ICL Multimodal Large Language Models
University of Illinois Urbana Champaign · University of Bonn · Microsoft
Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening
Jun 10, 2026Qingfeng Zhang, Yuanxiong Guo, Yanmin GongAlzheimer's Disease Multimodal Large Language Models
Dept. of Information Systems and Cybersecurity The University of Texas at San Antonio San Antonio, TX, USA · School of Engineering Medicine Texas A&M University Houston, TX, USA
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Jun 10, 2026Yifan Gao, Yanmin Gong, Yun Shi +1Type 1 Diabetes Time Series Forecasting
Department of Information Systems and Cybersecurity The University of Texas at San Antonio San Antonio, USA · School of Engineering Medicine Texas A&M University Houston, USA · Department of Family and Community Medicine The University of Texas at San Antonio San Antonio, USA
How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology
Jun 10, 2026Kian R. Weihrauch, Thomas A. Buckley, William Lotter +1Multimodal Large Language Models Histopathology Image Classification
Massachusetts Institute of Technology, Cambridge, MA · Harvard Medical School, Boston, MA · Dana-Farber Cancer Institute, Boston, MA
MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification
Jun 10, 2026Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang +3Multimodal Large Language Models
Tiktok Singapore, Singapore · Tiktok National University of Singapore School of Computing Singapore, Singapore · Tiktok Content Intelligence San Jose, CA, USA +1
Q-Fold: Query-Aware Focus-Context Spatio-Temporal Folding for Long Video Understanding
Jun 10, 2026Biao Tang, Xu Chen, Shuxiang Gou +3Multimodal Large Language Models Long-Video Understanding
Shenzhen Campus of Sun Yat-sen University · Harbin Institute of Technology, Shenzhen · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression
Jun 10, 2026Tianhao Chen, Yuheng Wu, Kelu Yao +3KV Caching Multimodal Large Language Models
KAIST · Zhejiang Laboratory · The Chinese University of Hong Kong +1
Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training
Jun 10, 2026Michal Chudoba, Sergey Alyaev, Petra Galuscakova +1LLM Fine-Tuning Multimodal Large Language Models
University of Stavanger · Stavanger, Norway · NORCE Research +1
Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning
Jun 10, 2026Zhirui Chen, Ziwei Chen, Ling ShaoMemory-Augmented Language Models Multimodal ICL
UCAS-Terminus AI Lab, University of Chinese Academy of Sciences, China · ByteDance Inc.
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
Jun 10, 2026Enhan Zhao, Wei Wu, Yuanrui Zhang +23D Spatial Reasoning Synthetic Data Generation
1Peking University · 2Ant International · 3The University of Hong Kong
DeceptionX: Explainable Deception Detection with Multimodal Large Language Models
Jun 9, 2026Jiayu Zhang, Shuo Ye, Jiajian Huang +7Multimodal Large Language Models Multimodal Reasoning
Great Bay University Dongguan, Guangdong, China · Hong Kong Polytechnic University HongKong, China
Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
Jun 9, 2026Peiqi Jia, Haonan Jia, Ziqi Miao +3VLM Evaluation Personality Modeling in Language Models
Xi’an Jiaotong University · Beihang University
Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
Jun 9, 2026Miaoxin Cai, Guanqun Wang, Wei Zhang +6Remote Sensing Image Understanding Multimodal Grounding
National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP), Beijing Institute of Technology, Beijing 100081, China · Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China · School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China +5
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
Jun 9, 2026Zhiyuan Zhu, Yixuan Chen, Yiwen Shao +13Multimodal Large Language Models Audio Understanding
Zhejiang University · Tencent Hunyuan
CoCoSI: Collaborative Cognitive Map Construction for Spatial Intelligence
Jun 9, 2026Yiming Zhang, Ruoxuan Cao, Zhihang ZhongMultimodal Large Language Models Multi-Agent LLMs
2Cornell University · 1Shanghai Jiao Tong University
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
Jun 8, 2026Wish Suharitdamrong, Muhammad Awais, Xiatian Zhu +1Efficient Multimodal Inference Multimodal Large Language Models
Surrey Institute for People-Centred AI (PAI), University of Surrey, UK · Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK
Reason Twice: Segmentation via Candidate Discovery and Comparative Reasoning
Jun 8, 2026Xinyan Gao, Haoran Hao, Xiangyu YueImage Segmentation Text-Guided Image Segmentation
MMLab, The Chinese University of Hong Kong · Nanjing University
Symbolic and Abstractive Reasoning with Complex Visual Queries
Jun 8, 2026Yichi Zhang, Jingdian Lu, Zhuo Chen +4Visual Reasoning Synthetic Data Generation
Zhejiang University · Nanjing University · Ant Group
Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation
Jun 8, 2026Siyuan Liu, Jinyang WuEfficient Multimodal Inference Multimodal Large Language Models
aSchool of Mechanics and Engineering Science, Peking University, China · bDepartment of Automation, Tsinghua University, China
Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges
Jun 8, 2026Tiejin Chen, Pingzhi Li, Kaixiong Zhou +2Multimodal Large Language Models Privacy Leakage in Language Models
Arizona State University · University of North Carolina at Chapel Hill · North Carolina State University
CRANE: Knowledge Editing for Reasoning MLLMs
Jun 8, 2026Han Huang, Hao Wang, Mengqi Zhang +3Knowledge Editing Knowledge Conflicts in Language Models
University of Chinese Academy of Sciences, Beijing 101408, China, and also with the New Laboratory of Pattern Recognition (NLPR), CASIA, Beijing 100045, China · Harbin Institute of Technology, Weihai 264209, China · School of Computer Science and Technology, Shandong University, Qingdao 266237, China
NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis
Jun 8, 2026Runze Yan, Minxiao Wang, Jiaying Lu +3Multimodal Large Language Models
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Rd NE, Atlanta, 30322, GA, United States. · Rollins School of Public Health, Emory University, 1518 Clifton Rd2026 NE, Atlanta, 30322, GA, United States.
Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs
Jun 7, 2026Jie Ma, Zhike Qiu, Jiayi Ji +2Efficient Multimodal Inference Multimodal Large Language Models
Xiamen University
TVI-CoT: Text-Visual Interleaved Chain-of-Thought Reasoning for Multimodal Understanding
Jun 7, 2026Lianyu Hu, Xiaoyu Ma, Zeqin Liao +1Multimodal Large Language Models Multimodal Reasoning
College of Computing and Data Science of Nanyang Technological University, Singapore.
When Correct Decisions Hide Internal Stress: Decision-State Probing in Multimodal Language Models
Jun 7, 2026Haoran Zhao, Soyeon Caren Han, Eduard HovyMultimodal Robustness Multimodal Large Language Models
1The University of Melbourne
When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding
Jun 6, 2026Yiheng Wang, Yueqian Lin, Lichen Zhu +3Multimodal Large Language Models Question Answering
Duke University, Durham, North Carolina, USA

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Group Preference Collapse in Personalized Multimodal Large Language Models

Fan Lyu, Wenqi Zhang, Joost van de Weijer

Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain, 08193

Multimodal Large Language Models Personalized Language Models User Preference Modeling Text Generation

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Personalized multimodal large language models (MLLMs) aim to generate user-specific responses, but existing methods mainly rely on profile-level information and overlook diverse user preferences. We identify group preference collapse, where multi-user personalized MLLMs become insensitive to individual preferences and drift toward dominant population-level choices due to suppressed preference signals and unreliable preference use during generation. We propose PrefMoE, a preference-centric framework that separates stable profile information from preference-related representations. PrefMoE decomposes preferences into shared prototypes and personalized residuals, preserves individualized residuals with imbalance-aware learning, counterfactual pseudo-user augmentation, and residual decorrelation, and routes profile and preference factors through separate LoRA adaptation paths. Experiments across multiple MLLM backbones show that PrefMoE improves preference-sensitive personalization while substantially reducing preference collapse. Project page: https://prefmoe.github.io/.

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Latest papers 676