Jinwei Lu

Recent advances in LLM-based agent systems enable natural language–driven, cross-modal data visualization. Yet, existing systems remain unreliable for complex, multi-modal, or iterative tasks. This demonstration introduces MultiVis-Agent, an LLM agent-based system enhanced with logic rules for reliable visualization generation. It integrates specialized agents for data querying, visualization, and validation under a central coordinator governed by a four-layer logic rule framework ensuring parameter safety, error recovery, and guaranteed termination. Built upon the MultiVis task—covering four scenarios from basic generation to iterative refinement—MultiVis-Agent produces executable visualization code from natural language, databases, and optional references. We will show that MultiVis-Agent achieves real-time generation, execution, and refinement with 99% task completion and strong cross-modal reasoning. In particular, we emphasize an end-to-end and interactive demonstration where users can upload databases, provide multi-modal references (images/code), and iteratively refine executable visualization code within the same interface. The demo video and code are available at https://github.com/Jinwei-Lu/MultiVis.

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.