# MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization

arXiv: https://arxiv.org/abs/2601.18320 · HTML: https://mlsanity.com/paper/2601.18320/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 25 of 25)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows](https://mlsanity.com/paper/2411.07763/) (2024)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [ReAcTable: Enhancing ReAct for Table Question Answering](https://mlsanity.com/paper/2310.00815/) (2023)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [Is GPT-4 a Good Data Analyst?](https://mlsanity.com/paper/2305.15038/) (2023)
- [ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering](https://mlsanity.com/paper/2405.07001/) (2024)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations](https://mlsanity.com/paper/2110.00680/) (2021)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [Towards Natural Language-Based Visualization Authoring](https://mlsanity.com/paper/2208.10947/) (2022)
- [Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models](https://mlsanity.com/paper/2309.10245/) (2023)
- [Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System](https://mlsanity.com/paper/1604.03583/) (2016)
- [InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices](https://mlsanity.com/paper/2001.06423/) (2020)
- [Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows](https://mlsanity.com/paper/2105.00121/) (2021)
- [PI2: End-to-end Interactive Visualization Interface Generation from Queries](https://mlsanity.com/paper/2107.08203/) (2021)
- [Quda: Natural Language Queries for Visual Data Analytics](https://mlsanity.com/paper/2005.03257/) (2020)
- [Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://mlsanity.com/paper/2307.16013/) (2023)
- [TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents](https://mlsanity.com/paper/2510.24014/) (2025)
- [Towards Robustness of Text-to-Visualization Translation against Lexical and Phrasal Variability](https://mlsanity.com/paper/2404.07135/) (2024)

## Built on by (most cited first, 3 of 3)

- [VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing](https://mlsanity.com/paper/2605.30117/) (2026)
- [Agentic Authoring of Interactive Multiview Visualizations in Genomics](https://mlsanity.com/paper/2606.00370/) (2026)
- [VisInteract: Towards Dynamic Interactive Text-to-Visualization under Imperfect Queries](https://mlsanity.com/paper/2609.15182/) (2026)

## Abstract (arXiv)

Real-world visualization tasks involve complex, multi-modal requirements that extend beyond simple text-to-chart generation, requiring reference images, code examples, and iterative refinement. Current systems exhibit fundamental limitations: single-modality input, one-shot generation, and rigid workflows. While LLM-based approaches show potential for these complex requirements, they introduce reliability challenges including catastrophic failures and infinite loop susceptibility. To address this gap, we propose MultiVis-Agent, a logic rule-enhanced multi-agent framework for reliable multi-modal and multi-scenario visualization generation. Our approach introduces a four-layer logic rule framework that provides mathematical guarantees for system reliability while maintaining flexibility. Unlike traditional rule-based systems, our logic rules are mathematical constraints that guide LLM reasoning rather than replacing it. We formalize the MultiVis task spanning four scenarios from basic generation to iterative refinement, and develop MultiVis-Bench, a benchmark with over 1,000 cases for multi-modal visualization evaluation. Extensive experiments demonstrate that our approach achieves 75.63% visualization score on challenging tasks, significantly outperforming baselines (57.54-62.79%), with task completion rates of 99.58% and code execution success rates of 94.56% (vs. 74.48% and 65.10% without logic rules), successfully addressing both complexity and reliability challenges in automated visualization generation.