arXiv AI
Aug 21
CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
arXiv:2604. 02794v2 Announce Type: replace Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data.
Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework
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.
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Hugging Face Trending Papers
Aug 4
ChartAnno: Evaluating MLLMs for Chart Annotation Generation
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 AI
Aug 5
ChartAnno: Evaluating MLLMs for Chart Annotation Generation
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.
arXiv AI
3d ago
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.
arXiv AI
3d ago
From Charts to Code: A Hierarchical Benchmark for Multimodal Models
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 Computation and Language
Sep 23
Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?
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 AI
Sep 15
ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
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 AI
Aug 28
DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
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 Machine Learning
Aug 28
Chart2SVG: Editable SVG Generation from Raster Chart Images
Chart2SVG is a multimodal large language model that transforms static raster chart images into editable SVGs enriched with semantic structure. By embedding chart‑specific semantic tokens into a vision‑language framework and training on the Beagle+ dataset of 33K distilled chart samples, the model captures both geometric primitives and their functional roles. The resulting SVGs are visually accurate and structurally consistent, and the accompanying Chart Structure Graph (CSG) exposes visual dependencies for interactive exploration, chart repurposing, and layout reuse.
arXiv AI
Jun 11
MLaGA: Multimodal Large Language and Graph Assistant
arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.
arXiv AI
Jun 10
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.
arXiv AI
Aug 12
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
arXiv:2506. 03922v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains.