ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density

ChartDensity-Bench is a benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to reconstruct structured numerical data from scientific charts that vary in visual density. The benchmark uses charts paired with source-level ground-truth data and systematically changes the number of simultaneously presented charts (k = 1, 3, 6, 9) to assess how density affects reconstruction performance. A multi‑dimensional evaluation framework measures structural reliability, reconstruction completeness, parseability, and numerical fidelity, revealing that numerical reconstruction generally worsens as visual density increases, with varying degrees of degradation across different models.

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• llms
• nlp
• multimodal
• benchmarks