LLM Agents for Visualization Generation and Vision-First Evaluation

Abstract

This report presents two LLM agents developed for the Agentic VIS Challenge 2026: a generator that produces interactive visualizations from a task description and dataset, and an evaluator that reviews visualizations through a browser. Generation separates data analysis from visualization design. An analysis stage formulates questions based on the task and writes executable code to compute their answers. It provides the visualization stage with prepared data files and a compact description of the analysis. The visualization stage uses these outputs to organize and build an interactive HTML page, with browser-based checks supporting revisions to its charts and layout. The evaluator assigns scores using browser screenshots, page information, and changes observed after interacting with the visualization. Both agents use context management, token limits, and error recovery to operate within the competition’s shared token budget. We describe the resulting design and reflect on the tradeoffs observed during its development. Example visualizations and the agent code are available online: Public Task 2, Held-out Task 2, and GitHub.