mlsanityData to 7 Oct 2026

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

arXiv 2512.22101 ↗First posted 26 Dec 2025cs.LG cs.AI cs.CLcited by 2

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 9 references that are in the map. First posted on arXiv 26 Dec 2025.

Builds on

7 of 9 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution8 Nov

Other references · 6

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoningcited by 119
  3. 2025HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generationcited by 34
  4. 2024LEVA: Using Large Language Models to Enhance Visual Analyticscited by 24
  5. 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Datasetcited by 9
  6. 2024GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysiscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Composable Agentic System for Automated Visual Data Reporting
  2. 2026Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
  3. 2025Multi-Agent Data Visualization and Narrative Generation
  4. 2025CoDA: Agentic Systems for Collaborative Data Visualization
  5. 2026Explainable Iterative Data Visualisation Refinement via an LLM Agent
  6. 2026TVIR: Building Deep Research Agents Towards Text-Visual Interleaved Report Generation
  7. 2025PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback
  8. 2026SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents