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.
arXiv:2606. 29808v1 Announce Type: cross Abstract: Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign.
arXiv:2604. 02794v2 Announce Type: replace Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data.
arXiv:2610.00374v1 Announce Type: cross Abstract: Analytical charts in multimodal deep research encode quantitative claims, requiring every visualized value to be faithfully grounded in supporting ev...
arXiv:2607. 15176v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis).
DEEPCHART is a new benchmark that evaluates large language models (LLMs) on faithful data‑science chart generation. It contains 1,482 expert‑annotated instances from scientific papers, financial filings, and ecosystem reports, and assesses chart creation through an Extract–Reason–Visualize pipeline. Experiments show that while LLMs can produce visually plausible charts, they frequently hallucinate data at the extraction and reasoning stages, especially in long, noisy, and multimodal contexts.
arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...
arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...
arXiv:2608. 03464v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored.
Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart semantics, and place appropriate textual or graphical elements.
arXiv:2609.26208v1 Announce Type: new Abstract: Data visualization is central to analytical reasoning, but real-world analysis increasingly requires language-driven interactive interfaces rather than...
arXiv:2607. 27084v1 Announce Type: cross Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers.
Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers. However, existing image quality assessment (IQA) methods are predominantly designed for natural photographs or AI-generated content, which cannot be directly applied to scientific papers.