arXiv AI June 2, 2026 By Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 Machine Learning

Jun 5

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.

By Jaehyun Jeon, Min Soo Kim, Jang Han Yoon, Sumin Shim, Yejin Choi, Hanbin Kim, Dae Hyun Kim, Youngjae Yu

llms multimodal benchmarks safety

arXiv AI

Jun 15

GUITrans2Act: Understanding User Operational Behaviors from Mobile GUI Interactions with Vision-Language Models

arXiv:2606. 12817v2 Announce Type: replace Abstract: Understanding the digital world on mobile devices is shifting from static UI perception to dynamic action comprehension.

By Yudong Zhang (Honor Device Co., Ltd), Lei Hu (Honor Device Co., Ltd), Daoyang Liu (The Chinese University of Hong Kong, Hong Kong, China), Jiawei Liu (Honor Device Co., Ltd), Yangfan Luo (Honor Device Co., Ltd), Zhilin Gao (Honor Device Co., Ltd), Zuojian Wang (Honor Device Co., Ltd)

llms agents multimodal benchmarks

arXiv AI

Jun 10

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

arXiv:2606. 10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reasoning remains largely unexplored.

By Syed Wasiq, Syed Mohamad Tawseeq, Yashwant Pravinrao Bangde, Debaditya Roy

llms nlp 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 10

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions

arXiv:2512. 11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space.

By Chenrui Fan, Yijun Liang, Shweta Bhardwaj, Kwesi Cobbina, Ming Li, Tianyi Zhou

llms multimodal benchmarks

Related stories

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

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

GUITrans2Act: Understanding User Operational Behaviors from Mobile GUI Interactions with Vision-Language Models

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

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

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions