Proteus: Shapeshifting Desktop Visualizations for Mobile via Multi-level Intelligent Adaptation

2026-04-25

AI Summary

Directly scaling desktop visualizations to mobile devices often results in unreadable text, loss of information, and broken interactions. This work proposes the first multi-granularity adaptive framework that spans topological structure, reference frames, and visual elements, coupled with a large language model–based multi-agent system to automate the transformation pipeline—from parsing and strategy prediction to mobile-ready visualization generation. User studies (N=12) and case evaluations demonstrate that the approach efficiently produces mobile visualizations with high readability and intuitive interactivity, significantly enhancing user experience.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With the rise of mobile-first consumption, users increasingly engage with data visualizations on mobile devices. However, the vast majority of existing visualizations are originally authored for desktop environments. Due to significant differences in viewport size and interaction paradigms, directly scaling desktop charts often results in illegible text, information loss, and interaction failures. To bridge this gap, we propose an automated framework to adapt desktop-based visualizations for mobile screens. By systematically categorizing the operations involved in the adaptation process, we establish a multi-level design space. This space defines evolution rules spanning from the global topology level, through the reference frame level, down to the visual elements level. Guided by this theoretical framework, we developed Proteus, a large language model-driven multi-agent system that automatically parses online visualizations, predicts optimal transformation strategies within the design space, and generates equivalent, highly readable visualizations for mobile devices. Case studies and an in-depth user study with 12 participants demonstrate the effectiveness and usability of Proteus.

Problem

• mobile visualization
• desktop-to-mobile adaptation
• data visualization
• viewport size
• interaction paradigms

Innovation

• multi-level adaptation
• mobile visualization
• large language model
• design space
• automated transformation