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arXiv · 2604.21134

Beyond Pixels: Introspective and Interactive Grounding for Visualization Agents

Yiyang Lu·Woong Shin·Ahmad Maroof Karimi·Feiyi Wang·Jie Ren·Evgenia Smirni
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Abstract

Vision-Language Models (VLMs) frequently misread values, hallucinate details, and confuse overlapping elements in charts. Current approaches rely solely on pixel interpretation, creating a Pixel-Only Bottleneck: agents treat interactive charts as static images, losing access to the structured specification that encodes exact values. We introduce Introspective and Interactive Visual Grounding (IVG), a framework that combines (1) spec-grounded introspection, which queries the underlying specification for deterministic evidence, with (2) view-grounded interaction, which manipulates the view to resolve visual ambiguity. To enable evaluation without VLM bias, we present iPlotBench, a benchmark of 500 interactive Plotly figures with 6,706 binary questions and ground-truth specifications. Experiments show that introspection improves data reconstruction fidelity, while the combination with interaction achieves the highest QA accuracy (0.81), with +6.7 % gains on overlapping geometries. We further demonstrate IVG in deployed agents that explore data autonomously and collaborate with human users in real time.

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Yiyang Lu, Woong Shin, Ahmad Maroof Karimi, Feiyi Wang, Jie Ren, Evgenia Smirni. 2026-04-22. Beyond Pixels: Introspective and Interactive Grounding for Visualization Agents. https://arxiv.org/abs/2604.21134

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