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Agentic scientific visualization generation and caption semantic alignment.

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Title: Agentic scientific visualization generation and caption semantic alignment.
Authors: Lu, Xuyi1,2 (AUTHOR), Li, Guan1,2 (AUTHOR), Dong, Yu1 (AUTHOR), Peng, Ruixiao1,2 (AUTHOR), Wang, Zhe1,2 (AUTHOR), Tian, Dong1,2 (AUTHOR), Shan, Guihua1,2 (AUTHOR) sgh@cnic.cn
Source: Information Visualization. Jul2026, Vol. 25 Issue 3, p213-231. 19p.
Subjects: Scientific visualization, Multiagent systems, Scientific communication, Visual analytics
Abstract: In the era of data-intensive scientific discovery, visualization serves as a crucial cognitive tool for researchers, while captions that align with visuals are essential for accurately conveying scientific intent. However, current scientific visualization workflows face significant challenges, including high technical barriers and semantic misalignment among user intent, visual output, and textual descriptions. To address these issues, this paper proposes SciVis-AGE, a visual analytics system based on multi-agent collaboration. Its core methodologies comprise an agent-based task decomposition and operator encapsulation approach for automatic visualization generation, and a multi-agent triangular debate mechanism for semantic alignment and caption optimization. The system effectively reduces the technical burden on domain experts and, through iterative debate among Intent Guardian, Visual Verifier, and Annotation Checker agents, ensures precise alignment of generated images and captions with user intent, visual content, and highlighted features, thereby enhancing the rigor and efficiency of scientific communication. [ABSTRACT FROM AUTHOR]
Copyright of Information Visualization is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: In the era of data-intensive scientific discovery, visualization serves as a crucial cognitive tool for researchers, while captions that align with visuals are essential for accurately conveying scientific intent. However, current scientific visualization workflows face significant challenges, including high technical barriers and semantic misalignment among user intent, visual output, and textual descriptions. To address these issues, this paper proposes SciVis-AGE, a visual analytics system based on multi-agent collaboration. Its core methodologies comprise an agent-based task decomposition and operator encapsulation approach for automatic visualization generation, and a multi-agent triangular debate mechanism for semantic alignment and caption optimization. The system effectively reduces the technical burden on domain experts and, through iterative debate among Intent Guardian, Visual Verifier, and Annotation Checker agents, ensures precise alignment of generated images and captions with user intent, visual content, and highlighted features, thereby enhancing the rigor and efficiency of scientific communication. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Visualization is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/14738716261434841
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        Text: English
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              Text: Jul2026
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              Y: 2026
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