arXiv:2607.04727v1 Announce Type: cross Abstract: Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.g., clicking and filtering), and generate code that reproduces the target dashboard. To support comprehensive evaluation, we present Dash
The rapid advancement of multi-modal large language models (MLLMs) combined with increasing enterprise demand for dynamic data insights is driving innovation in automated data visualization.
This development indicates a significant step towards more autonomous AI agents capable of understanding user intent, interacting with complex systems, and generating functional code for dynamic data exploration, impacting productivity and software development paradigms.
AI models are no longer limited to static chart generation but are evolving to proactively interact with and reproduce complex, interactive dashboards, moving closer to automating full data analysis workflows.
- · AI service providers
- · Data visualization platforms
- · Enterprise software
- · Software developers
- · Manual dashboard designers
- · Traditional business intelligence firms lagging in AI adoption
The ability of AI to generate interactive dashboards will significantly reduce development time and democratize advanced data visualization.
This will lead to more widespread adoption of AI-generated analytics, potentially increasing the demand for explainable AI to ensure data integrity and interpretation.
The automation of dashboard creation could enable a new class of proactive AI agents that not only analyze data but also iteratively refine their visual outputs based on inferred user needs and data exploration patterns.
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Read at arXiv cs.AI