arXiv Machine Learning

Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding

arXiv:2505. 05026v5 Announce Type: replace-cross Abstract: User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept.

arXiv AI

Jun 12

Reasoning for Mobile User Experience with Multimodal LLMs: Task, Benchmark, and Approach

arXiv:2606. 13192v1 Announce Type: new Abstract: User experience (UX) centered on usability, perceived consistency, and functional clarity is fundamental to real-world user interfaces (UI).

By Ruichao Mao, Zhou Fang, Teng Guo, Hao Yang, Yaping Li, Shaohua Peng, Maji Huang, Xiaoyu Lin, Shuoyang Liu, Xuepeng Li, Yuyu Zhang, Hai Rao

llms agents efficiency multimodal benchmarks

arXiv AI

Jun 2

What's Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning

arXiv:2604. 06995v2 Announce Type: replace Abstract: Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding.

By Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

llms multimodal benchmarks safety

arXiv AI

Jun 10

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

arXiv:2606. 10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world.

By Zhixin Ma, Yutong Zhou, Yongqi Li, Chong-Wah Ngo, Wenjie Li

llms agents robotics multimodal benchmarks

Hugging Face Trending Papers

Jun 9

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world. In such embodied settings, a central capability is the use of physical tools, which underpins MLLMs' ability to assist humans in real-world tasks.

llms agents robotics multimodal benchmarks

arXiv AI

6d ago

Do You See What You Draw? A Semantic Closed-Loop Framework for Holistic Evaluation of Unified Multimodal Models

arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.

By Hao Zhang, Jiaxin Qi, Zhijiang Tang, Jianqiang Huang

llms multimodal benchmarks

arXiv Machine Learning

Jul 2

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).

By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jun 2

Benchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages

arXiv:2606. 00154v1 Announce Type: cross Abstract: Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-end development.

By Fan Wu, Lishuai Dong, Cuiyun Gao, Yujia Chen, Yiming Huang, Yang Xiao, Qing Liao

llms multimodal benchmarks

Hugging Face Trending Papers

Aug 12

Do You See What You Draw? A Semantic Closed-Loop Framework for Holistic Evaluation of Unified Multimodal Models

As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge. Current evaluation protocols predominantly treat generative and discriminative capabilities as separate tasks, leaving a gap in system-level evaluation for unified multimodal models (UMMs).

llms multimodal benchmarks

arXiv AI

Jun 16

Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning

arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.

By Zhengbo Zhang, Changtao Miao, Jinbo Su, Zhaowen Zhou, Chunxia Zhang, Xukai Wang, Ruiqi Liu, Kaiyuan Zheng, Jiansheng Cai, Bo Zhang, Zhe Li, Shiming Xiang, Ying Yan

llms agents multimodal benchmarks data-engineering

arXiv AI

Jul 17

Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

arXiv:2607. 15176v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis).

By Patrick Phuoc Do, Chau M. Ta, Chaoli Wang

llms multimodal benchmarks

arXiv AI

Jun 26

What We are Missing in Multimodal LLM Evaluation?

arXiv:2606. 26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.

By Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu

llms multimodal benchmarks

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