Multimodal models

Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.

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arXiv Machine Learning

Jun 8

Diagnosing Visual Ignorance in Vision-Language Models

arXiv:2606. 06890v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.

By Runyu Zhou, Qi Zhang, Qixun Wang, Yisen Wang

llms multimodal benchmarks

arXiv AI

Jun 8

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

arXiv:2605. 05225v3 Announce Type: replace-cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP) inference due to the straggler effect.

By Bo Li, Chuan Wu, Shaolin Zhu

llms multimodal benchmarks

arXiv AI

Jun 8

Latent-space Attacks for Refusal Evasion in Language Models

arXiv:2605. 21706v2 Announce Type: replace Abstract: Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations.

By Giorgio Piras, Raffaele Mura, Fabio Brau, Maura Pintor, Luca Oneto, Fabio Roli, Battista Biggio

llms multimodal benchmarks safety

arXiv AI

Jun 8

EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks

arXiv:2505. 14289v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental Injection Attacks (EIAs).

By Yijie Lu, Manman Zhao, Tianjie Ju, Zihe Yan, Xinbei Ma, Yuan Guo, Daizong Ding, Gongshen Liu, Zhuosheng Zhang

llms agents multimodal safety

arXiv AI

Jun 8

MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs

arXiv:2506. 01850v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in instruction-following tasks by integrating pretrained visual encoders with large language models (LLMs).

By Wayner Barrios, Andr\'es Villa, Juan Le\'on Alc\'azar, SouYoung Jin, Bernard Ghanem

llms rag nlp multimodal benchmarks safety

arXiv AI

Jun 8

Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models

arXiv:2606. 06534v1 Announce Type: cross Abstract: Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an image of a referred time point.

By Jialin Wu, Qianru Zhang, Georges El Fakhri, Xiaofeng Liu

llms nlp multimodal benchmarks safety

arXiv AI

Jun 8

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval

arXiv:2604. 09552v2 Announce Type: replace-cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems.

By Kiarash Naghavi Khanghah, Hoang Anh Nguyen, Anna C. Doris, Amir Mohammad Vahedi, Daniele Grandi, Faez Ahmed, Hongyi Xu

llms rag agents nlp multimodal benchmarks

arXiv AI

Jun 8

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

arXiv:2606. 06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding.

By Yifan Xu, Chao Zhang, Ruifei Ma, Fei Gao, Zhifei Yang, Jiaxing Qi, Zhipeng Chen

llms agents diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 8

Federated Foundation Models over Vehicular Networks

arXiv:2606. 06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL).

By Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali, Owen Palinski, Allan Salihovic, Dinh Nguyen, Minghui Liwang, Seyyedali Hosseinalipour

fine-tuning multimodal safety

arXiv AI

Jun 8

Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation

arXiv:2606. 06836v1 Announce Type: cross Abstract: Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navigation (VLN) benchmarks typically use discrete or coarse actions and existing UAV Vision-Language-Action (VLA) tasks focus on short, atomic maneuvers.

By Xiangyi Zheng, Xiangyu Wang, Qinan Liao, Zimu Tang, Yue Liao, Dongyue Lyu, Guodong Wang, Junjie Liu, Si Liu

llms agents diffusion multimodal benchmarks

arXiv AI

Jun 8

Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks

arXiv:2606. 06833v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on incomplete information.

By Jiani Xie, Andrew C. Cullen, Paul Montague, Benjamin I. P. Rubinstein

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 8

FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

arXiv:2606. 07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion.

By Ambuj Mehrish, Sebatiano Vascon

rag multimodal

arXiv AI

Jun 8

Watch, Remember, Reason: Human-View Video Understanding with MLLMs

arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.

By Jiahao Meng, Yue Tan, Qi Xu, Kuan Gao, Weisong Liu, Yanwei Li, Jason Li, Lingdong Kong, Haochen Wang, Qianyu Zhou, Jiangning Zhang, Guangliang Cheng, Yunhai Tong, Lu Qi, Minghsuan Yang

llms multimodal benchmarks safety

arXiv AI

Jun 8

Textual Supervision Enhances Geospatial Representations in Vision-Language Models

arXiv:2606. 07172v1 Announce Type: cross Abstract: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning.

By Marcelo Sartori Locatelli, Fernando Tonucci, Jea Kwon, Luiz Felipe Vecchietti, Bryan Nathanael Wijaya, Cheng Yaw Low, Virgilio Almeida, Meeyoung Cha

llms multimodal

arXiv AI

Jun 8

Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models

arXiv:2601. 22574v2 Announce Type: replace-cross Abstract: Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination.

By Yuansheng Gao, Jinman Zhao, Tong Zhang, Xingguo Xu, Wenbin Xing, Han Bao, Zonghui Wang, Wenzhi Chen

efficiency multimodal safety

arXiv AI

Jun 8

Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation

arXiv:2606. 06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation.

By Yunhan Wang, Yuda Wang, Zhiying Tu, Mingqiang Song, Li Song, Kun Li, Dianhui Chu, Bolin Zhang

llms multimodal safety

arXiv AI

Jun 8

Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation

arXiv:2606. 07015v1 Announce Type: cross Abstract: While song generation and singing voice conversion (SVC) have evolved significantly, they have long been developed isolated: the former lacks zero-shot speaker cloning, while the latter overlooks vocal-accompaniment synergy.

By Ziyu Zhang, Chunyu Qiang, Xiaopeng Wang, Yuxin Guo, Kang Yin, Wenjie Tian, Jingbin Hu, Tianlun Zuo, Zhao Guo, Teng Ma, Yuzhe Liang, Chen Zhang, Lei Xie

llms rag diffusion multimodal benchmarks

arXiv Machine Learning

Jun 8

Closed-Form Spectral Regularization for Multi-Task Model Merging

arXiv:2606. 07289v1 Announce Type: new Abstract: Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-development costs of large foundation models.

By Yongxian Wei, Runxi Cheng, Xingxuan Zhang, Li Shen, Chun Yuan, Peng Cui, Dacheng Tao

computer-vision fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jun 8

ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China

We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis.

llms nlp multimodal benchmarks

arXiv AI

Jun 6

HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling

arXiv:2510. 00054v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding tasks.

By Xianjie Liu, Yiman Hu, Yixiong Zou, Liang Wu, Jian Xu, Bo Zheng

llms reinforcement-learning multimodal benchmarks safety

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