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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A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
May 19, 2026Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3Multi-View Consistency Game Theory
1Microsoft Research India · 2Indian Institute of Technology Hyderabad
AffectVerse: Emotional World Models for Multimodal Affective Computing
May 19, 2026Bo Zhao, Fanghua Ye, Yixin Ji +3Cross-Modal Learning Multimodal Large Language Models
1Great Bay University · 2Tencent · 3Tsinghua University +1
Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning
May 19, 2026Qinghe Ma, Zhen Zhao, Yiming Wu +3Multimodal Large Language Models Multimodal Reasoning
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Shanghai Artificial Intelligence Laboratory +2
LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue
May 19, 2026Chaoyue Li, Yongxue Xu, Jie Feng +1Spatiotemporal Reasoning Multimodal Large Language Models
Huazhong University of Science and Technology · Sun Yat-sen University · Beihang University +1
MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems
May 19, 2026Quanxing Xu, Yuhao Tian, Ling Zhou +4Visual Question Answering Automated Software Testing
School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR 999078, China · Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China · State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China +3
Modality-Decoupled Online Recursive Editing
May 19, 2026Siyuan Li, Youyuan Zhang, Fangming Liu +1Continual Learning for LLMs Multimodal Large Language Models
Harbin Institute of Technology, Shenzhen, China. · Peng Cheng Laboratory, China. · Huazhong University of Science and Technology, China.
Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
May 19, 2026Jinrui Jiang, Zhangtai Wu, Zhen Wu +1Hallucination in Language Models Multimodal Large Language Models
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation
May 18, 2026Qianhao Yuan, Jie Lou, Xing Yu +4Multimodal Large Language Models On-Policy Distillation
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Xiaohongshu Inc.
CrossView Suite: Harnessing Cross-view Spatial Intelligence of MLLMs with Dataset, Model and Benchmark
May 18, 2026Wei Wang, Yuqian Yuan, Tianwei Lin +4VLM Evaluation Multi-View Consistency
Zhejiang University China
Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models
May 18, 2026Kunyu Peng, Zhikun Zhou, Kailun Yang +9Visual Spatial Reasoning Multi-Robot Systems
Karlsruhe Institute of Technology · INSAIT, Sofia University “St. Kliment Ohridski” · Hunan University +4
RAVE: Re-Allocating Visual Attention in Large Multimodal Models
May 18, 2026Xi Leng, Xinhong Ma, Ziqiang Dong +4Visual Attention Self-Attention
Qwen Business Unit of Alibaba · The Chinese University of Hong Kong, Shenzhen · Beijing Institute of Technology
Beyond the Cartesian Illusion: Testing Two-Stage Multi-Modal Theory of Mind under Perceptual Bottlenecks
May 18, 2026Yajing Zhou, Xiangyu KongAudio-Visual Understanding Visual Spatial Reasoning
College of Computer Science, Beijing Information Science and Technology University, No.55 Taihang Road, Changping District, Beijing, 102206, China
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
May 18, 2026Jun-Yu Pan, Yansen Wang, Enze Zhang +3EEG Decoding EEG-Based Visual Decoding
Shanghai Jiao Tong University Shanghai, China · Microsoft Research Asia Shanghai, China
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
May 18, 2026Xinpeng Dong, Min Zhang, Kairong Han +3Object Hallucination in VLMs Multimodal Large Language Models
Zhejiang University · East China Normal University · Zhejiang University of Science and Technology
Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correction
May 18, 2026Jiahe Guo, Xiangran Guo, Jiaxuan Chen +6Multimodal Large Language Models LLM Safety
Harbin Institute of Technology · Huawei Technologies Co., Ltd
OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models
May 18, 2026Morunliu Yang, Ruotao Xu, Le Li +6Efficient Multimodal Inference Multimodal Large Language Models
1Soochow University · Peking University
A More Word-like Image Tokenization for MLLMs
May 18, 2026Hyun Lee, Hyemin Jeong, Yejin Kim +4Image Tokenization Efficient VLM Inference
Seoul National University · Ewha Womans University
DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
May 18, 2026Wenzhuo Xu, Zhipeng Wei, Zonghao Ying +4Multimodal Large Language Models LLM Safety
AI Security Lab, Beijing, China · International Computer Science Institute, CA, USA · UC Berkeley, CA, USA +1
Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction
May 17, 2026Chaoqun He, Mingyang Xiang, Yingjing Xu +5Multimodal Large Language Models Multimodal Agents
Tsinghua University · Shanghai Qi Zhi Institute · ModelBest Inc.
EgoIntrospect: An Egocentric Dataset and Benchmark for User-Centric Internal State Reasoning
May 17, 2026Zeyu Wang, Chang Liu, Eduardus Tjitrahardja +22Multimodal Large Language Models Egocentric Video Understanding
Tsinghua University · Shanghai Qi Zhi Institute · Tongji University +5
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
May 17, 2026Yanjie Li, Jian Xu, Xu-Yao Zhang +4Multimodal Large Language Models Materials Property Prediction
AnnLab, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China. · Zhongguancun Academy, Zhongguancun Academy, Beijing, China. · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China. +3
Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
May 16, 2026Firoj Alam, Shammur Absar Chowdhury, Enamul Hoque PrinceCross-Modal Alignment Multimodal Large Language Models
Qatar Computing Research Institute, HBKU, Qatar · York University, Canada
Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition
May 16, 2026Zeheng Wang, Bo Zhao, Yijie Zhu +6Multimodal RAG Hierarchical Classification
Great Bay University Guangdong, Dongguan, China · Tencent Youtu Lab Shanghai, China · CUHK, shenzhen Guangdong, Shenzhen, China
EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers
May 16, 2026Zongyuan Yang, Mingjing Yi, Wanli Ma +83D Mesh Generation 3D Shape Editing
SeeleAI Team
MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models
May 15, 2026Piyush Tiwary, Utkarsh Ahuja, Depanshu Sani +5Remote Sensing Image Segmentation Agricultural Remote Sensing
Google DeepMind · Indian Institute of Science · Google
Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models
May 15, 2026Cheng Zhang, Yuer Liu, Zhiyu Zhou +2Feature Attribution Multimodal Large Language Models
Department of Computer Science and Technology, Jilin University, Changchun, China · Department of Computer Science, National Taiwan University, Taipei, Taiwan
ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
May 15, 2026Jiahui Guang, Haiyan Wang, Yingjie Zhu +4VLM Unlearning Multimodal Large Language Models
Harbin Institute of Technology, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China · The Hong Kong Polytechnic University, Hong Kong, China +1
MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models
May 14, 2026Tianwei Chen, Takuya Furusawa, Yuki Hirakawa +3Multi-Label Classification Multimodal Large Language Models
ZOZO NEXT Inc.
OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
May 14, 2026Yeo Jeong Park, Hyemi Jang, Minseo Choi +3Efficient Multimodal Inference Multimodal Large Language Models
Samsung Research
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
May 13, 2026Trung Nguyen Quang, Yiming Gao, Fanyi Pu +3Multimodal Grounding Multimodal Large Language Models
Nanyang Technological University · LMMs-Lab Team · Johns Hopkins University

May 19, 2026, cs.CVJ/K move · Enter open · S save

A Nash Equilibrium Framework For Training-Free Multimodal Step Verification

Rohit Sinha, Kunal Tilaganji, Tanuja Ganu, Nagarajan Natarajan+2

1Microsoft Research India · 2Indian Institute of Technology Hyderabad

Multi-View Consistency Game Theory Multimodal Large Language Models Multimodal Reasoning Reasoning Verification

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Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations. Learned critics need extensive labeled data and show inconsistent performance across different tasks. Meanwhile, existing training-free methods simply average scores from different sources, missing a key insight: when these scores disagree, that disagreement itself carries important information about whether a reasoning step is truly valid or not. We propose a training-free verification approach that treats step-wise verification as a coordination problem among specialized judges. We formalize these judges' interaction as a Nash equilibrium game where agreement signals valid steps while disagreement reveals instability. Our method computes equilibrium scores through a closed-form solution, enabling both disagreement-aware filtering and stability-conscious ranking of reasoning steps. Evaluated across six benchmarks, our approach achieves consistent improvements of 2.4% to 5.2% over baseline models and shows competitive performance against learned critics, demonstrating that cross-modal agreement (not just average confidence) provides robust verification signals without task-specific adaptation.

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