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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CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
Apr 24, 2026Lihao Zheng, Zhenwei Shao, Yu Zhou +5Multimodal Grounding Multimodal Large Language Models
School of Computer Science and Technology, Hangzhou Dianzi University · Base Model, Li Auto
MTT-Bench: Predicting Social Dominance in Mice via Multimodal Large Language Models
Apr 24, 2026Yunquan Chen, Haoyu ChenMultimodal Large Language Models Social Reasoning
Department of Communication Systems, KTH Royal Institute of Technology, Stockholm, Sweden · CMVS, University of Oulu, Finland
SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments
Apr 24, 2026Chih-Ting Liao, Xi Xiao, Chunlei Meng +6Spatial Reasoning Benchmarks Visual Spatial Reasoning
The University of New South Wales · The University of Alabama at Birmingham · Fudan University +4
CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language
Apr 24, 2026Rui Zhao, Xuewen Zhong, Xiaoyun Zheng +2Multimodal Large Language Models Sign Language Recognition
School of Informatics, Xiamen University, China · 2Key Lab of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian-Taiwan (XMU), Ministry of Culture and Tourism, China · 3National Language Resources Monitoring and Research Center for Education and Teaching Media, Xiamen University, China
T2MM: An LLM Supported Architecture For Inquiry-Based Modeling
Apr 24, 2026John Kos, Rudra Singh, Ashok GoelEducational Technology Multimodal Large Language Models
Georgia Institute of Technology, Atlanta GA 30322, USA
BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature
Apr 23, 2026Jiaxian Yan, Jintao Zhu, Yuhang Yang +8Document Information Extraction Multimodal Large Language Models
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China · Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University · Princeton University +2
Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
Apr 23, 2026Azher Ahmed Efat, Seok Hwan Song, Wallapak TavanapongData Visualization Multimodal Large Language Models
Department of Computer Science, Iowa State University, Ames, Iowa, USA
Latent Denoising Improves Visual Alignment in Large Multimodal Models
Apr 23, 2026Dhruv Parikh, Jacob Fein-Ashley, Rajgopal Kannan +1VLM Robustness Multimodal Large Language Models
University of Southern California Los Angeles, CA, USA · DEVCOM ARL Army Research Office USA
Can MLLMs "Read" What is Missing?
Apr 23, 2026Jindi Guo, Chaozheng Huang, Xi FangVLM Evaluation Document Understanding
DP Technology
Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagery
Apr 22, 2026Siyuan Yao, Siavash Ghorbany, Kuangshi Ai +6Multimodal Large Language Models
University of Notre Dame, Notre Dame, IN, 46556, USA.
Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning
Apr 22, 2026Dahun Kim, Ganesh Satish Mallya, Anelia AngelovaRemote Sensing Image Understanding Multimodal Large Language Models
Google DeepMind
Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback
Apr 22, 2026Guotao Liang, Zhangcheng Wang, Juncheng Hu +5Multimodal Large Language Models Multimodal Generation
School of Software, Beihang University, China · 3 4Paradigm, China · Department of Computer Science, The University of Hong Kong, China
SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models
Apr 22, 2026Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2Multimodal Large Language Models Reinforcement Learning with Verifiable Rewards
Max Planck Institute for Informatics, SIC · VIA Research Center · Google
Benchmarking Large Language Models for Safety Data Extraction
Apr 22, 2026Jonas Grill, Thomas Bayer, Sören BerlingerLLM Evaluation LLM Prompting
1SAP SE, Germany. · Institute for Digital Transformation, Ravensburg-Weingarten University.
HyLaR: Hybrid Latent Reasoning with Decoupled Policy Optimization
Apr 22, 2026Tao Cheng, Shi-Zhe Chen, Hao Zhang +3Multimodal Large Language Models Multimodal Reasoning
Tencent PCG, China · Tencent CSIG, China
ATIR: Towards Audio-Text Interleaved Contextual Retrieval
Apr 22, 2026Tong Zhao, Chenghao Zhang, Yutao Zhu +1Multimodal IR Multimodal Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China
SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition
Apr 22, 2026Jielong Tang, Xujie Yuan, Jiayang Liu +6Multimodal Grounding Named Entity Recognition
Sun Yat-sen University Zhuhai, China · Shandong Normal University Jinan, China · University of the Chinese Academy of Sciences Beijing, China +2
AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce
Apr 22, 2026Biao Zhang, Lixin Chen, Bin Zhang +3Multimodal Large Language Models Cross-Modal Retrieval
Taobao & Tmall Group of Alibaba
Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
Apr 21, 2026Paweł Pozorski, Jakub Muszyński, Maria GanzhaMultimodal Large Language Models Neural Network Interpretability
student record book number 327304 · student record book number 327246 · thesis supervisor
mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
Apr 21, 2026Jakub Muszyński, Paweł Pozorski, Maria GanzhaMultimodal Large Language Models Shapley Value Attribution
Warsaw University of Technology, Warsaw, Poland
ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety
Apr 21, 2026Kun Wang, Cheng Qian, Miao Yu +6LLM Backdoor Attacks Multimodal Large Language Models
3Nanyang Technological University · 2Beijing University of Aeronautics and Astronautics · University of Science and Technology of China +2
DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning
Apr 20, 2026Abrar Majeedi, Zhiyuan Ruan, Ziyi Zhao +3Cross-Modal Learning Visual Question Answering
University of Wisconsin-Madison · Amazon · Northeastern University
Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control
Apr 20, 2026Jay Jung, Ahmad Arrabi, Jax Luo +2Robotic Control Language Model-Based Control
University of Vermont, Burlington, 05405, Vermont, U.S.A. · Cleveland Clinic, Cleveland, 44195, Ohio, U.S.A.
Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models
Apr 20, 2026Yakoub Bazi, Mohamad M. Al Rahhal, Mansour Zuair +1VLM Evaluation Remote Sensing Image Understanding
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia · Applied Computer Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
Apr 20, 2026Yongrui Heng, Chaoya Jiang, Han Yang +2Synthetic Data Generation Multimodal Large Language Models
School of Control Science and Engineering, Shandong University Jinan, China · Zeekr, Geely Auto Shanghai, China
Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs
Apr 20, 2026Samuel G. Balter, Ethan Jerzak, Connor T. JerzakMultimodal Large Language Models Multimodal Reasoning
University of Texas at Austin · National University of Singapore (NUS)
MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge
Apr 20, 2026Sua Lee, Sanghee Park, Jinbae ImLLM-as-a-Judge Multimodal Large Language Models
Seoul National University · NAVER Cloud AI · KAIST AI
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
Apr 20, 2026Tingchao Fu, Wenkai Wang, Fanxiao Li +6Knowledge Editing Multimodal Large Language Models
School of Information Science and Engineering, Yunnan University · School of Software, Yunnan University · 3National University of Singapore +1
Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding
Apr 19, 2026Shaoguang Wang, Weiyu Guo, Ziyang Chen +2Multimodal Large Language Models Multimodal QA
Thrust of Artificial Intelligence, HKUST (Guangzhou) Guangzhou, China · Department of CSE, HKUST Hong Kong SAR, China
E2E-GMNER: End-to-End Generative Grounded Multimodal Named Entity Recognition
Apr 19, 2026Meng Zhang, Jinzhong Ning, Xiaolong Wu +2Multimodal Grounding Named Entity Recognition
1Dalian Maritime University · 2Dalian University of Technology

Apr 24, 2026, cs.CVJ/K move · Enter open · S save

CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding

Lihao Zheng, Zhenwei Shao, Yu Zhou, Yan Yang+4

School of Computer Science and Technology, Hangzhou Dianzi University · Base Model, Li Auto

Multimodal Grounding Multimodal Large Language Models Multimodal Contrastive Learning Multimodal Understanding

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Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failures in object constancy. In addition, existing approaches typically rely on expensive human annotations or large-scale chain-of-thought (CoT) data generation. We propose Compositional Grounded Contrast (abbr. CGC), a low-cost full framework for boosting fine-grained multi-image understanding of MLLMs. Built on existing single-image grounding annotations, CGC constructs compositional multi-image training instances through Inter-Image Contrast and Intra-Image Contrast, which introduce semantically decoupled distractor contexts for cross-image discrimination and correlated cross-view samples for object constancy, respectively. CGC further introduces a Rule-Based Spatial Reward within the GRPO framework to improve source-image attribution, spatial alignment, and structured output validity under a Think-before-Grounding paradigm. Experiments show that CGC achieves state-of-the-art results on fine-grained multi-image benchmarks, including MIG-Bench and VLM2-Bench. The learned multi-image understanding capability also transfers to broader multimodal understanding and reasoning tasks, yielding consistent gains over the Qwen3-VL-8B base model on MathVista (+2.90), MuirBench (+2.88), MMStar (+1.93), MMMU (+1.77), and BLINK (+1.69).

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Latest papers 676