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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00481 |
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| _version_ | 1866911131738570752 |
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| author | Wolter, Anton Vidalakis, Georgios Yu, Michael Grover, Ankit Dhanoa, Vaishali |
| author_facet | Wolter, Anton Vidalakis, Georgios Yu, Michael Grover, Ankit Dhanoa, Vaishali |
| contents | Recent advancements in the field of AI agents have impacted the way we work, enabling greater automation and collaboration between humans and agents. In the data visualization field, multi-agent systems can be useful for employing agents throughout the entire data-to-communication pipeline. We present a lightweight multi-agent system that automates the data analysis workflow, from data exploration to generating coherent visual narratives for insight communication. Our approach combines a hybrid multi-agent architecture with deterministic components, strategically externalizing critical logic from LLMs to improve transparency and reliability. The system delivers granular, modular outputs that enable surgical modifications without full regeneration, supporting sustainable human-AI collaboration. We evaluated our system across 4 diverse datasets, demonstrating strong generalizability, narrative quality, and computational efficiency with minimal dependencies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Data Visualization and Narrative Generation Wolter, Anton Vidalakis, Georgios Yu, Michael Grover, Ankit Dhanoa, Vaishali Artificial Intelligence Recent advancements in the field of AI agents have impacted the way we work, enabling greater automation and collaboration between humans and agents. In the data visualization field, multi-agent systems can be useful for employing agents throughout the entire data-to-communication pipeline. We present a lightweight multi-agent system that automates the data analysis workflow, from data exploration to generating coherent visual narratives for insight communication. Our approach combines a hybrid multi-agent architecture with deterministic components, strategically externalizing critical logic from LLMs to improve transparency and reliability. The system delivers granular, modular outputs that enable surgical modifications without full regeneration, supporting sustainable human-AI collaboration. We evaluated our system across 4 diverse datasets, demonstrating strong generalizability, narrative quality, and computational efficiency with minimal dependencies. |
| title | Multi-Agent Data Visualization and Narrative Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.00481 |