GraphiMind: LLM-centric Interface for Information Graphics Design
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Abstract (arXiv)
Information graphics are pivotal in effective information dissemination and storytelling. However, creating such graphics is extremely challenging for non-professionals, since the design process requires multifaceted skills and comprehensive knowledge. Thus, despite the many available authoring tools, a significant gap remains in enabling non-experts to produce compelling information graphics seamlessly, especially from scratch. Recent breakthroughs show that Large Language Models (LLMs), especially when tool-augmented, can autonomously engage with external tools, making them promising candidates for enabling innovative graphic design applications. In this work, we propose a LLM-centric interface with the agent GraphiMind for automatic generation, recommendation, and composition of information graphics design resources, based on user intent expressed through natural language. Our GraphiMind integrates a Textual Conversational Interface, powered by tool-augmented LLM, with a traditional Graphical Manipulation Interface, streamlining the entire design process from raw resource curation to composition and refinement. Extensive evaluations highlight our tool's proficiency in simplifying the design process, opening avenues for its use by non-professional users. Moreover, we spotlight the potential of LLMs in reshaping the domain of information graphics design, offering a blend of automation, versatility, and user-centric interactivity.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflowcited by 13
- 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11
- 2025From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysiscited by 9
- 2025NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understandingcited by 2
- 2025NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understanding20 Aug 2025
- 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agents14 Mar 2025
- 2025From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis17 Feb 2025
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow15 Feb 2024
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- 2026Figures as Interfaces: Toward LLM-Native Artifacts for Scientific Discovery
- 2025CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
- 2025Automated Visualization Makeovers with LLMs
- 2025Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback