English

A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting

Machine Learning 2025-12-29 v1 Artificial Intelligence Computation and Language

Abstract

Automating end-to-end data science pipeline with AI agents still stalls on two gaps: generating insightful, diverse visual evidence and assembling it into a coherent, professional report. We present A2P-Vis, a two-part, multi-agent pipeline that turns raw datasets into a high-quality data-visualization report. The Data Analyzer orchestrates profiling, proposes diverse visualization directions, generates and executes plotting code, filters low-quality figures with a legibility checker, and elicits candidate insights that are automatically scored for depth, correctness, specificity, depth and actionability. The Presenter then orders topics, composes chart-grounded narratives from the top-ranked insights, writes justified transitions, and revises the document for clarity and consistency, yielding a coherent, publication-ready report. Together, these agents convert raw data into curated materials (charts + vetted insights) and into a readable narrative without manual glue work. We claim that by coupling a quality-assured Analyzer with a narrative Presenter, A2P-Vis operationalizes co-analysis end-to-end, improving the real-world usefulness of automated data analysis for practitioners. For the complete dataset report, please see: https://www.visagent.org/api/output/f2a3486d-2c3b-4825-98d4-5af25a819f56.

Keywords

Cite

@article{arxiv.2512.22101,
  title  = {A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting},
  author = {Shuyu Gan and Renxiang Wang and James Mooney and Dongyeop Kang},
  journal= {arXiv preprint arXiv:2512.22101},
  year   = {2025}
}

Comments

3 pages, 3 figures; Accepted by 1st Workshop on GenAI, Agents and the Future of VIS as Mini-challenge paper and win the Honorable Mention award. Submit number is 7597 and the paper is archived on the workshop website: https://visxgenai.github.io/subs-2025/7597/7597-doc.pdf

R2 v1 2026-07-01T08:41:42.258Z