ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
Authors: Zhen Li
Duan Li
Yukai Guo
Xinyuan Guo
Bowen Li
Lanxi Xiao
Shenyu Qiao
Jiashu Chen
Zijian Wu
Hui Zhang
Xinhuan Shu
Shixia Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Infographic Chart
Chart Understanding
Code Generation
Chart Generation
Dataset
Primary area: datasets and benchmarks
TL;DR: ChartGalaxy is a million-scale dataset with 1,701,356 programmatically generated and 61,833 real infographic charts, covering 75 chart types, 440 chart variations, and 68 layout templates, to support automatic understanding and generation.

Abstract: Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and structural richness poses challenges for large vision-language models (LVLMs), which are typically trained on plain charts. To bridge this gap, we introduce ChartGalaxy, a million-scale dataset designed to advance the understanding and generation of infographic charts. The dataset is constructed through an inductive process that identifies 75 chart types, 440 chart variations, and 68 layout templates from real infographic charts and uses them to create synthetic ones programmatically. We showcase the utility of this dataset through: 1) improving infographic chart understanding via fine-tuning, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation. By capturing the visual and structural complexity of real design, ChartGalaxy provides a useful resource for enhancing multimodal reasoning and generation in LVLMs.

Published as a conference paper at ICLR 2026
CHARTGALAXY: A DATASET FOR INFOGRAPHIC CHART UNDERSTANDING AND GENERATION
Zhen Li"; Duan Li"; Yukai Xinyuan Guo'; Bowen ; Xiao Shenyu Qiao', Jiashu Chen', Zijian Wu', Hui Zhang', Xinhuan Shu", Shixia Liu' TTsinghua University "Newcatl University
ChartGalaxy
1,701,356 synthetic infographic charts tables
61,833 real infographic charts tables
Applications
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2.90
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Infographic Chart Understanding
Code Generation
Infographic Chart Generation
Figure 1: ChartGalaxy, a million-scale dataset of synthetic and real infographic charts with data tables, supporting applications infographic chart understanding, code generation, and chart generation.
ABSTRACT
Infographic charts are a powerful medium communicating abstract data by combining visual elements (e.g., charts, images) with textual information. How- ever. their visual and structural richness poses challenges for large vision-langee lisguu-language models (LVLMs), which are typically trained plain charts. To bridge this gap, we introduce ChartGalaxy, a million-scale dataset designed to advance the under- standing and generation of infographic charts. The dataset is constructed through an inductive process that identifies 75 chart types, 440 chart variations, and 68 layout templates from real infographic charts and uses them create synthetic ones programmatically. We showcase the utility of this dataset through: 1) improving in- fographic chart understanding via fine-tume tinettunin, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation. By capturing the visual and structural complexity of real design, Chartaalxy provides useful resource for enhancing multimodal reasoning and generation in LVLMs.
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INTRODUCTION
Infographic charts widely recognized commonly used in news media, business, and education (Cui et al. 2022 Elaldi & Çifçi 2021). By integrating imagery (e.g., icons and metaphorical graphics) alongside charts and textual information, they present abstract data in a manner that is both engaging and easy to understand, making data more accessible broad audience. Despite their effectiveness for human audiences. foundation models, such as GPT-4 (Achiam et al., 2023), Gemini (Team et al.) (2023), and LLaVA (Liu et al. 2023c), face considerable challenges in automatically understanding infographic charts. The intricate interplay between visual and textual elements, diverse layout styles, and the need for cross-modal semantic reasoning pose significant difficulties. For example, Xie et al. (2255) found that MLLMs
effective form for communicating data and
"Equal contribution. Corresponning author.

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Published as a conference paper at ICLR 2026
perform worse in understanding infographic charts than plain charts constructed from the same data, largely because the imagery elements used in infographics introduce additional reasoning challenges. Moreover, automatically generating high-quality infographic charts remains an open challenge. While human designers can create visually diverse and semantically rich infographic charts, this process is time-consuming and requires expertise. Meanwhile, Al Al-generated charts often suffer from issues such as low data fidelity, modest visual quality, limited diversity, and a lack of coherence across modalities. This highlights the critical need for comprehensive dataset of infographic charts that enables model development in both automatic understanding and generation. However, existing efforts focus on constructing datasets that are mostly limited plain charts, failing to capture the diverse range of design styles and layouts that are key characteristics of infographic charts. This limits the ability of the trained models to generalize across different real-world applications where infographic charts are commonly used.
To address this limitation, we build ChartGalaxy, a million-scale dataset of high-quality real and synthetic infographic charts to facilitate automated understanding and generation. As shown in Fig. 2 we build ChartGalaxy in two steps: 1) collecting real infographic charts; 2) programmatically creating synthetic infographic charts. The real infographic charts are collected from 18 reputable chart-rich websites, such as Visual Capitalist and Starista The synthetic infographic charts are created following an inductive structuring process (Schadewitz & Jachna 2007). Specifically,
identify design patterns grounded in real infographic charts, including 75 chart types (e.g., bar charts), 440 chart variations that reflect different visual element styles, and 68 layout templates that define spatial relationships among elements. Based on these patterns, we then programmatically generate synthetic ones. The core of the generation is human--n-the-ll pipeline that iteratively extracts and expands layout templates from real infographic charts using detection model trained on synthetic infographic charts.
The final ChartGalaxy dataset includes 1,701,356 programmatically created infographic charts and 61, 61,833 real infographic charts. It characterized by two key features. First, the high-quality infographic designs and associated templates from these reputable websites ensure a rich diversity in design styles and structural complexity. Second, each infographic chart, whether real or synthetic, is paired with its source data table, enabling a clear mapping between data and its visual representation. Together, these make ChartGalaxy well-suited for training and evaluating LVLMs for automatic infographic understanding and generation. We demonstrate the utility of ChartGalaxy through three representative applications, each highlighting a distinct aspect of its value (Fig. D.. First, to evaluate and improve the ability of foundation models understand infographic charts, we introduce a dataset for infographic chart understanding through the task of visual question answering (VQA). Second, to assess the capacity of models generate executable representations of complex visual layouts, we present a benchmark for infographic chart code generation. Third, to explore the use of ChartGalaxy in creative content generation, we develop an example- based infographic chart generation method.
The main contributions of our work include:
A pipeline for programmatically creating high-quality synthetic infographic charts based on the extracted layout templates from real designs.
comprehensive dataset comprising a large collection of representative and diverse real and synthetic infographic charts paired with tabular data.
Three applications for showcasing the utility of our dataset in infographic chart understanding, code generation, and example-base basee infographic chart generation.
RELATED WORK
Early efforts in chart dataset construction primarily focus on building collections of plain charts to support chart understanding and generation (Hu et al.) 2024 Yang et (024a). These datasets can be further categorized into three types based on their sources: synthetic datasets, web-collected datasets, and mixed datasets. Synthetic datasets are programmatically generated, using tabular data drawn from probability distributions (Kafle et al. 2018: Kahou et al. (017). collected from online data sources (Hu et al.) 2024 Methani et al. 2020 Chaudhry et al. 2020 Xu et al. 2023 Tang et al. 2023 Akhtar et al. (023), or simulated from large language models (Xu et al. 2023
Xia et al.
2024 Han al. 2023 7Zha et al. 2025). While this method enables large-scale dataset
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Real Infographic Chart Collection
Final Dataset
Data collection
Duplicate removal
Data table extraction
61,833 real infographic charts tables
statista
Chart-rich
X
websites
Google
Search engines
SIMSRANNTE
COMPANIC
Synthetic Infographic Chart Creation
75 chart lypes & 440 variations
Text
layout templates
Synthetic infographic chart
Remote Work and Leolokk Clargon
1,701,356 synthetic infographic charts + tables
Remote Work Change
Image
33%
Wark
Figure 2: Overview of our dataset construction method.
construction, the controlled generation process often results in limited diversity of chart types and visual styles, which reduces generalizability to more varied real-world scenarios. To improve diversity, web-collected datasets have been introduced, which collect charts from chart-sharing Chhoi et al.) 2019; Masry et al.] | Kantharaj et al.) 2022a Huang
websites (Savva et al.] [2011] Choi
et al. 2025b] Obeid & Hoque [2000] Kantharaj et al. 2022b] Masry et al., (2024a). charting library
galleries such as Matplotlib (Wu et al. 2024 Yang et al. 2024b), and publications in academic
al., (024). These datasets capture a broader variety of human-designed chart styles, but their overall size is often limited due the time-consuming nature of manual verification and annotation. To overcome the scale limitations while preserving diversity, mixed datasets are then introduced. These datasets merge web-collected charts with synthetically generated ones in a two-step workflow: 1) collect real charts from the internet and 2) manually synthesize additional charts that follow the same real-world design patterns (Liu et al. 2023a by Masry al. 2023 Meng et al., 2024 Yang et al. 2025). This hybrid strategy ensures real-world coverage while increasing dataset size.
repositories such ArXiv (Wang al.
2024 Hsu et al. 2021 2
In contrast, infographic charts remain underrepresented in the aforementioned datasets. This creates challenges in evaluating and developing LVLMs' capabilities on infographic charts (Masry et al.) 2025). To bridge this gap, recent efforts have focused on building specific datasets for infographic charts. Infographic VQA makes initial effort by searching "infographics" on the internet and scraping 5,485 infographics (Mathew et al. 2022). More recently, ChartQAPro provides a more chal- lenging benchmark comprising 190 infographic charts, 258 dashboards, and 893 plain charts (Masry) et al. 2025). However, these datasets are limited in scale. Moreover, synthesizing infographic charts is challenging, given the intricate interplay between visual and textual elements. To overcome these limitations, we develop an automatic infographic chart generation method that synthesizes infographic charts by leveraging the layout templates and chart variations extracted from real designs.
DATASET CONSTRUCTION METHOD
3.1 METHOD OVERVIEW
Fig. presents overview of our method, which includes two stages: real infographic chart collection and synthetic infographic chart creation.
The real infographic charts are collected from two main sources. First, we gather infographics from 18 well-known chart-rich websites that permit research use, such as Visual Capitalist and Statista The full list of these websites and their licenses is provided in Appendix Second, to further increase the diversity of infographic styles, we retrieve infographic charts via Google Images and Bing Images, following prior work (Marry al. 2024a). We apply the platforms' built-in license
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filters to retain only images available under Creative Commons licenses. The ethical consideration in the data collection process is further discussed EtEiiiss Statement] To ensure data quality, we remove duplicate images using Perceptual Hashing and CLIP similarity (Radford et al., 202I). Moreover, extract per-chart tabular data using LVLMs in a multi-step, human-in-the-loop verification pipeline. This pipeline ensures accuracy and reduces model-induced noise. Details of this verification pipeline are discussed in Appendix [ This results in 01,833 real infographic charts with corresponding tables. The synthetic infographic chart creation stage follows an inductive structuring process that extracts design patterns, such as layout templates and chart variations, from real infographic charts and then uses these patterns to programmatically create high-quality synthetic charts. It includes three main steps: 1) identifying chart types and their variations, 2) extracting layout templates, and 3) creating synthetic infographic charts.
3.2 CHART TYPE AND VARIATION IDENTIFICATION
We first summarize 75 chart types observed the collected real infographic charts, drawing on two existing taxonomies: Data Viz Project and Datylon For each chart type, we extract chart variations featuring diverse visual styles, such as element shapes and icon placement. This results in 440 chart variations in total. The full lists of chart types and variations are provided in Appendix [D.1 and D.2 We implement these chart types and variations using the expressive D3.js (Bostock et al. 2011 which supports visual features unavailable in libraries like Matplotlib or Seaborn.
3.3 LAYOUT TEMPLATE EXTRACTION
A layout template defines the spatial relationships among the text and visual elements in infographic charts. Example templates are illustrated in the bottom-lcft corner of each chart in Fig. 3] and a concrete example in JSON format is provided in Appendix [F] We adopt human-in memaan-n--th--eee in-the-loop pipeline to initialize and expand these templates from real infographic charts.
Initialization Three co-authors manually annotate the bounding boxes of the text, images, and charts in 1,500 real infographic charts sampled from two high-quality sources: [Statista] (clean, minimalist designs) and Visual Capitalist (denser and more pictorial designs). From these annotations, summarize an initial set of 55 layout templates that capture elements' relative positions (e.g.. title on the tep-left, chart on the bottom-right) and pairwise overlaps (overlapping or not).
Expansion To ensure the coverage and diversity of templates, we build detection model to analyze the unlabeled real infographic charts in ChartGalaxy and expand the layout template set. Using the initial templates, we programmatically create 120,000 synthetic infographic charts (Sec. 33.4. each with annotated bounding boxes. We then develop detection model by fine-tuning Internlmage (Wang) jet al., 2023) along with the DINO (Zhang et al. 2023) detector on these synthetic charts. We use this model to detect chart and image regions (Zhu et al. (025a) and use PP-OCRv4|e to extract text. We then compare the detected layouts with the existing templates using LTSim (Otani et al. 2024). a state-of the-art method for measuring layout similarity. Layouts with low similarity scores
flagged as potential new templates. Next, we cluster these layouts using k-means (k- = 50) and manually examine the cluster centroids to identify distinct layouts. This process yields 13 additional layout templates, expanding the set to 68 templates in total. The full list is provided in Appendix
3.4 TEMPLATE-BASED INFOGRAPHIC CHART CREATION
The creation process involves three steps: 1) curating data tables; 2) generating/reoommendng elements based on the data table; and 3) optimizing the layout based on the selected template. Fig. shows six examples of the synthetic charts. More examples are provided in Appendix G
Tabular data curation To enhance data diversity for chart generation, we build a rich repository of real and synthetic tabular data. For real data, we collect 200,085 tables from well-established sources, including VizNet (Hu et al. 2019), UN datal Our World in Datal and Papers with Code For synthetic data, we generate 98,483 tables with Semmini-2.0-Flas downstream processing, we also complement each table with a topic (e.g.. "NBA ofay-offs") extracted by Gemini-2.0 2.0-alhsh and several data facts (e.g., trends, comparisons) following Wang et al. (2020).
following Han et al. (2023). To facilitate
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Figure 3: Examples of synthetic infographic charts ChartGalaxy. The bottom-left illustration on each infographic chart shows the corresponding layout template.
For each data table, we generateerecoomend key elements of the infographic chart, including 1) text, 2) image, and 3) chart.
Element ganeration/reommendtttii
Text. The title and subtitle are generated using a retrieval-auummentdd prompting strategy. Specifically, we use Sentence- Centence-BERT (Reimers & Gurevych 2019) to retrieve the three most relevant real info- graphic charts according to the data topic and data fact. Using them as in-context references, we prompt Gemini-2.0-Flaatt to generate the title and subtitle aligned with the data table.
Image. To recommend images for infographic charts, we build repository and retrieve semantically relevant images from it using the associated data tables. Images are collected from publicly available icon resources such as Icon645 et (022)) aod/Noun Project) We apply heuristic filters to remove low-quality images, excluding those with low ink ratios, poor resolution, extreme aspect ratios. Using Gemini-2.0-Flaas we then generate descriptive keywords and captions for each image that depict its content and visual style. Images that overly literal, visually cluttered, lack symbolic clarity are discarded. This process results in a curated repository of 68 1,499 images. To retrieve relevant images for each infographic chart, compute the similarity between the image keywords and the generated chart title using "entence-BERKT embeddings (Reimers Gurevych, 2019).
Chart. Chart generation proceeds in steps: selecting suitable chart type and rendering with specific chart variation. First, based on the attributes (e.g., categorical, numerical) and characteristics (e.g., scales) of the given data table, we identify candidate chart types following the predefined data-to-chart mapping rules. For example, a data table with one categorical column and two numerical columns may be mapped to a scatter plot. When multiple chart types are suitable, we prompt Gemini-2.0-Flast to select the optimal one, considering data types, value distributions, and temporal patterns (Luo et al., 2018). full list of mapping rules and prompts provided in Appendix E Next, we choose specific variation under the selected chart type using adaptive sampling (Goodfellow et al. 2016) that favors underrepresented variations to maintain distributional balance. For variations requiring additional images (e.g., Fig. 3(a), a)), retrieve the relevant ones from our curated image repository. Finally, we apply semantically resonant color palettes for chart rendering based on the generated chart titles and subtitles. These palettes are extracted from the collected real infographic charts (Liu et al. (024). If selected palette contains fewer colors than required, supplement additional harmonic and discriminable colors (Chen et al. 2025
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Layout optimization Previous research has shown that compact layout with appropriate white space enhances both visual appeal and data clarity (Coursaris & Kripintris, 2012). Consequently, we aim to select the template with the highest ink ratios while preserving readability. To this end, we first filter out templates that are incompatible with the generated elements (e.g., unintended overlaps between elements). For each remaining template 1, we treat its spatial relationships as hard constraints during optimization. We begin by initializing element positions through rejection sampling, repeatedly drawing random candidate layouts and retaining only those that satisfy the spatial relationships specified by the template. This provides structurally coherent starting point. Next, we refine the layout by adjusting element positions and sizes to reduce unnecessary white space, while preserving all template-specifiet constraints. We measure white space using the ink ratio and select the optimized result & with the highest ink ratio as the final layout.
max 8
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S.L. g(E,) = d(des, de,) 2 P. Vitj.
Here, e is the pixel set of an element, f(e) denotes the pixel set within the tight-fitting bounding box of e, E is the set of elements, and g(E,)) is the indicator function that equals if & satisfies the spatial relationships specified by the template t. We enforce that minimum pairwise distance d between element contours de, and Oe, is larger than a given threshold p. This layout optimization is formulated constrained packing problem and solved by grid search (Lodi et al. 1999).
3.5
DATA STATISTICS
ChartGalaxy contains 1,701,356 programmatically generated infographic charts and 61,833 real ones, covering 75 chart types, 440 chart variations, and 68 layout templates. Each infographic chart in the dataset is associated with tabular data, providing rich supervision for training and evaluation tasks. Among the 75 chart types, the most frequently occurring ones are horizontal bar charts (11.7%), vertical bar charts (4.9%), and scatterplots (3.5%). Each chart type has up to 27 variations, capturing a wide range of styles element shapes, icon placement, and rendering effects. We observe differences in the frequency of template usage. For example, the template in Fig. 3 is the most common in the synthetic charts (6.9%). Differences in template usage are influenced by each template's structural flexibility, compatibility with chart types, and prevalence in real-world use cases. For more analysis on our dataset, please refer to Appendix C
EXPERIMENTS
4.1 INSTRUCTION DATASET FOR INFOGRAPHIC CHART UNDERSTANDING
In this experiment, we construct an instruction dataset with ChartGalaxy to enhance model capabilities on infographic chart understanding. We validate its usefulness by fine-tuning two open-source LVLMs, demonstrating improvements on both public benchmarks and our independent evaluation set.
Dataset construction To improve LVLMs' data comprehension and visual understanding of info- graphic charts, we construct an instruction dataset comprising 443,455 question-answer pairs based on 70,248 charts sampled from ChartGilaxy, ensuring balanced coverage of diverse chart types for better fine-tuning performance. The questions are classified into three types: 1) Text-based reasoning. We incorporate well-established question types from prior work, including open-form questions (Masry et al. 2025) and template-based questions (Meng et al., 2024). These questions cover data identification (DI), data comparison (DC), data extraction with condition (DEC), and fact checking (FC). 2) essul-clementtbbase reasoning. We extend beyond purely text-based reasoning questions by incorporating visual elements from charts, such as icons (e.g., "What was the wine export value of in 2024?"). These questions require models to associate visual elements with their corresponding data values, thus testing their ability to conduct more complex cross-modal reasoning (Xie et al.] 2025). 3) Visual understanding. This type includes style detection (SD), visual encoding analysis (VEA. e.g., "What data dimension is encoded using different colors in this infographic chart?"), and chart classification (CC). The three types of questions evaluate a model's s ability to interpret visual elements and underlying structural representations of the data. Detailed prompts and methods for generating question-anse answerr pairs are provided in Appendix L.T Moreover, construct an independent, human-verified evaluation set containing 2, 176 synthetic charts with

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Published as a conference paper at ICLR 2026
4.975 question-answer pairs. We focus on synthetic charts here, as they provide bounding-bol box annotations that enable visual-element- based reasoning questions.
Experimental setup We fine-tune two representative open-source LVLMs, ILtern (Zhu et al. 2025b) and Swen2.5-VL-77H (Bai et al. 2025). Training details are reported in Appendix H.1
Our evaluation benchmark consists of two parts: 1) public benchmarks including Infograph- icVQA (Mathew et al.] (022), which focuses on general infographics with only a subset being infographic charts, and ChartQAPro (Masry et al. 2025), which covers various chart types; and 2) the aforementioned independent evaluation set of 2,176 charts with 4,975 question-ans specifically targeting infographic charts. For the evaluation metrics, we follow previous work on chart question answering (Masry al. 2025), , using relaxed accuracy with 5% margin for numerical answers, ANLS for textual answers, and exact matching for multiple-choice questions.
pairs
Results and analysis Tables and and show the evaluation results on the public benchmarks and our evaluation set. After fine-tuning with ChartGalaxy, both models demon- strate improved performance gains across all question types. On the public benchmarks (Table []. Intern improves performance by 3.80% on InfographicVOA and 5.98% on ChartQAPro, while Qwen2.5- VL. shows a 4.44% gain on Infographic VQA and 3.59% improvement on ChartQAPro. On our evaluation set (T both models show consistent improvements across all question types, with overall gains of +26.87% for InternVL3 and +23.85%
Table 1: Performance on public bench-
marks w/ and w/o ChartGalaxy.
Model
InfographicVQA
ChariQAPro
VL
76.19
38.15
ChartGalaxy
79.99
44.13
(+3.80)
(+5.98)
Qwen2,5-VL-7B
78.59
37.97
ChartGalaxy
83.03
41.56
(+)
(+4,44)
(+3,59)
for Qwen2.5-VL. The most notable improvements are observed in the visual understanding questions, with increases of up to +60.49% for style detection and +40.78% for visual encoding analysis. These results indicate that existing pre-training routines may underrepresent questions involving chart visual styles and data encoding, an area our dataset helps to supplement. Performance also improves across the text-based and iisual-elemenntbased reasoning questions. Qwen2.5-VL performs well on text-based reasoning, while Intern VL3 shows relatively stronger gains on visual-element-base reasoning. Additional results, including ablation studies real/synthetic charts and evaluation smaller model (Qwen2.5-VL-3B), are provided in Appendix H.I
Table 2: Performance on our independent evaluation set w/ and w/o ChartGalaxy,
Tex: Based Reasening
Visual- Element- Based Reasoning
Visual Undersianding
Model
DI
DC
DEC
PC
D1
DC
DEC
PC
SD
VEA
CC
Ovenall
IntemVL3-SB
55.21
51.66
33.32
61.58
50.57
ChurtGaaay
91.67
74.39
75.14
89.26
69.12
42.79
58.57
80.23
91.05
91.35
99.39
80.07
(+)
(+6.31)
(+19.15)
(+23.48)
(+13 (6)
(+35.80)
(+23.88)
(+20.95)
(+18.65)
(+60-49)
(+40.78)
(+26. 36)
(+36.87)
Qwen2.5-VL-7g 5-VL-7B
87:45
66.32
64.44
78.53
40.76
30.65
46.00
53.95
28.70
50.08
70.91
56.50
ChhnnCilaxy
93.28
86.31
87.34
66.15
72.38
79.38
87.65
90.86
80.35
(e)
(15.85)
(114.66)
(+21,87)
(18.81)
(x25.39)
(+9.15)
(126.38)
(425.43)
(+58.95)
(140.78)
(+27.27)
(x23.85)
4.2 BENCHMARKING INFOGRAPHIC CHART CODE GENERATION
This experiment presents a benchmark to assess LVLMs' code generation for infographic charts.
Benchmark construction The benchmark is designed to evaluate the Direct Mimic task (Yang al. 2025), where an LVLM is prompted generate the D3.js code for given infographic chart image. Due to variation in coding styles and implementation strategies (Si et al. 2025). directly comparing code quality is challenging. Therefore, we evaluate the rendered output instead of the code itself. Specifically, we render the output as both an SVG and PNG: the SVG enables fine-grained analysis, as it contains precise information about visual and textual elements (e.g., positions, colors), while the PNG supports direct visual comparison. To support this task, we randomly sampled 500 synthetic infographic charts, with explicit coverage of all chart types, variations, and layout templates. Each chart is paired with ground-truth triplet: PNG image, an SVG, and the corresponding tabular data. Benchmark details are provided in Appendix H.2
Following previous benchmarks (i et al. 2025 Yang al. 2025). we measure the similarity between the ground-trun -ruth chart and the one rendered by the generated code at two levels: high-level score (overall visual similarity judged by GPT-4o with the PNG images) and low-level score (average similarity across fine- grained SVG elements). To compute the low-level score, we parse the SVG elements from the rendered chart and the ground-truth one and match them based attributes such
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Table 3: Performance comparison of 17 LVLMMs our proposed code generation benchmark, reporting the code execution success rate (Exec. Rate), low-level. high-level, and overall scores.
Exec.
LeweLevel
|High-Level
Model
Overall
Area
Text
Image Color Position
Size Avg. GPT-4o
Proprietary
Gemini-2.5-Pro
100.00
90.72
95.69
86.37
87.67
89.23
69.05
86.45
83.97
85.21
GPT-4.1
100.00
90.58
91.58
86.53
87.52
87.13
55.61
83.16
76.84
80.00
Blaude-3.7-Some
100.00
88.96 92.39
77.90
84.78
87.57
67.29
83.15
76.66
79,91
GPT-4.1-mine
99.60
88.21 88.31
79.32
86.43
85.61
62.85
81.79
77.59
79.69
98.80
83.13 79.26
67.53
83.93
84.95
64.07
77.14
74.79
75.97
Demini-2.5-Flash
96.40
84.52 87.02
73.21
77.81
83.01
62.29
77.98
73.12
75,55
OpenAI-ol
97.20
85.01
78.07
80.28
81.70
82.35
60.76
78.03
71.35
74,69
OpenAI-o3
92.40
83.84
82.43
72.15
78.79
81.63
63.44
77.05
71.38
74.22
GPT-4o
99.00
74,86
63.54
34.12
76.60
80.12
56.27
64.25
67.10
65.67
GPT-4.1 -nnao
100.00
74.97
59.94
36.06
69.26
75.10
47.69
60.50
59.62
60.06
Doubao-1 1. 1.5- Vision-Pro
97.20
59.05
48.08
36.63
60.09
66.02
40.00
51.65
42.58
47.11
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92.60
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61.96
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95.60
73.36
47.98
62.19
65.76
70.35
43.74
60.56
49.58
55.07
Llama-4-Scout-178
98.20
71.08
51.81
45.27
63.48
69.95
42.28
57.31
46.50
51.91
Qwen2.5-V1-3225
81.60
60.37
49.37
28.19
54.37
61.37
37.60
48.55
44.42
46.48
as tag types and positions. This matching is formulated as a linear assignment problem and solved using the Jonker- Volken--ll algorithm (Crouse 2016 Sii et al. 2025 Chen al. 2024). Based on the matching results, we compute low-level score by averaging six metrics: area, text, image, color, position, and size. The area metric captures the ratio of matched element area the total element area. The text and image metrics assess the similarity of generated text and image elements, respectively. The color, position, and size metrics evaluate visual consistency in these attributes among matched elements. Details of the evaluation metrics are provided in Appendix H..2 Following Yang et al. (2025), we also calculate the overall score as the average the high-level and low-level scores, ranging from 0 to 100. Notably, if the code fails to render the chart, both scores set to 0.
Experimental setup We benchmark 17 widely used LVLMs, including 12 proprictary ones and 5 open- open-source ones, as shown Table Model configurations and detailed prompts are provided in Appendix H.2 and [.2] respectively.
presents the results of the 17 LVLMs on our benchmark. We highlight key findings below. First, among proprietary models, Gemini-2.5-Pro achieves the highest overall score of 85.21. Among open-source models, Llama-4-Maverick-177 17B performs the best with a score of 61. 19, outperforming the proprietary GPT-4.1-nand (60.06). However, it still lags behind top- performing proprietary models. Within the GPT-4.1 series, GPT-4.1 and GPT-4. -mini achieve nearly identical overall scores (80.00 vs. 79.69). However, GPT-4.1 consistently outperforms GPT-4.1-min across all individual low-level metrics, except the size metric, where it scores notably lower (55.61 vs. 62.85). This drop suggests that GPT-4. may struggle to preserve element size accurately, highlight- ing the need for further investigation. Additional results and analysis are provided in Appendix H.2
Results and analysis
4.3 EXAMPLE-BASED INFOGRAPHIC CHART GENERATION
This experiment demonstrates how ChartGalaxy can be used to support the generation of infographic charts through layout and style adaptation.
We develop an example-based method that transforms providdovided tabular data into an infographic chart, aligning with the layout and visual style of given example infographic chart. This example is either provided by the user or automatically retrieved from the ChartGalaxy dataset by selecting the chart most relevant to the user- user-provided tabular data and its column descriptions. The key feature of this method is its ability to generate visually coherent infographic charts by reusing the layout of well-designed examples and leveraging powerful detection and vision-lang lision-language models. To enable this capability, we first use the detection model descrihed in Sec. 3.3 to detect key
Method
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Reference
1B GPT 1mage-1
Ours
AI at Work ACROSS GENERATIONS
Average Commute Time Students in Amsterdam
Fastest Way Class
Aenage dl Sid for stae cousing
0
linel y Ammterrtn by messpestionnno
85
Bicycle
78%
73x
Public
27.2
Pher/Wened
Walking
32.1
Scooter
22,8
25.8
Sicycle
Public
24,9
2A Reference
2B
2C Ours
3A Reference
3B GPT-Image-1
3C Ours
AI QUAUITY
BREATHE
EÚ EMIGRANTS
ENERGY
I
IK
Three examples of infographic charts used in Sec. chart, and C are generated by GPT-Image-1 and our method, respectively, using the same data.
Figure 4:
4.3 In each example, A is the reference
Table 4:
Performance comparison between our method and GPT-Image-1 (Mean, [95% CI)).
Method
Fidelity
Aesthetics
Creativity
Ours
4.63, [4.51, 4.75) 4.14, [3.95, 4.33] 3,95, [3.77, 4.13]
Image--
2.10, [1.71. 2.50] 2.90, [2.48. 3.33) 2.65, [2.28. 3.03]
elements (e.g., icons, text blocks) in the example infographic chart. We then generate/recommend new elements from the provided tabular data (Sec. (3.4), initialize their layout using the extracted positional information from the example, and refine the arrangement through the layout optimization module (Sec. 3.4).
User study setup We conducted a user study with 16 experts in design visualization to evaluate the quality of the generated infographic charts using our method and GPT-Image-I| a state-of-the-art image generation model. The evaluation focused on three key metrics: fidelity (how accurately the data is represented), aesthetics (how appealing the infographic chart is), and creativity (how innovative the design is). Experts were asked to rate 30 pairs of infographic charts generated by our method and GPT--mage-1 based on the same tabular data and reference infographic chart, using five-point Likert scale (I=poor, 5=excellent) for each metric. Fig. shows examples of the generated infographic charts and the reference. The prompt used for GPT-Image-I is provided in Appendix L.3
Results and analysis T shows that our method outperforms GPT-Image- across all three metrics based on the Wilcoxon signed-rank test on the user rating data (p 0.01): fidelity (average: 4.63 vs. 2.10), aesthetics (4.14 vs. 2.90), and creativity (3.95 vs. 2.65). Particularly, our method achieves high fidelity by accurately representing data through carefully implemented chart variations. Some scores are slightly lower due to expert preferences for alternative chart types in certain cases. In contrast, GPT-Image-I often exhibits serious fidelity-related issues, such as incorrect labels, disproportionate representations, and mismatched data elements. In terms of aesthetics and creativity, our method benefits from accurately extracting layout templates from reference infographic charts and supporting wider variety of chart types beyond basic chart types. such as bar charts and line charts. By comparison, GPT-Image-- tends to use basic chart types with limited variations, leading to outputs that are visually simple and monotonous. The detailed analysis is provided in Appendix H..

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Published as a conference paper at ICLR 2026
5 CONCLUSION
We echo the growing interest in multimodal understanding and generation in LVLMs by introducing ChartGalaxy, a million-scale, high-quality dataset of 61,833 real and 1,701,356 synthetic infographic charts. Grounded in real designs, our structured synthesis pipeline enables the scalable creation of diverse infographic charts. By providing aligned data-chart pairs, extracted layout templates, and three representative applications, we aim to advance the development of foundation models capable of interpreting, reasoning, and generating complex infographic charts.
At the same time, we acknowledge that the current ChartGalaxy dataset primarily focuses on single- chart infographics. limiting its ability to capture the complexity of multi-chart narratives. As result, future work should explore the generation and analysis of multi-chart infographics, which emphasize storytelling through coordinated visual elements. Additionally, enriching the interplay between text and visuals could further enhance models' capacity for nuanced multimodal understanding.
ACKNOWLEDGMENTS
This work was supported by National NSF of China (No. U2IA20469). The authors would like to thank J. Zhu, T. Xie, M. Lin, Z. Wang, and Z. Shen for their contributions to the dataset construction.
ETHICS STATEMENT
We strictly comply with the terms of use of the original sources and standard research ethics in dataset construction and evaluation. 1. Real infographic charts. We collect infographic charts from publicly accessible sources with identifiable copyright/lienning terms: i) 18 chart-rich websites (e.g.. Visual Capitalist, Statista), each of which provides clear copyright or license terms (see Table for the full source list and licensing links); ii) Google Images and Bing Images, where we apply built-in license filters to retain only charts explicitly marked under Creative Commons licenses. To further reduce copyright risks, we release only the URLs of real charts, following prior work (Masry et al.) 2024a; Schuhmann et al. 2022). Prior to releasing our dataset, we consulted a licensed IP attorney, who reviewed the licensing pages of all 18 websites (accessed on 2025-09-16 16 and re-checked on 2025-12-02) as well as our dataset release format. The legal assessment is that our release does not violate the rights of these websites, because it does not republish, redistribute, or reproduce third-party charts and we do not host or distribute any chart images, including thumbnails or cached copies. Any downstream access must comply with each source's terms. This legal assessment informed the design of our dataset and release protocol. We provide additional details in Appendix A 2. Synthetic infographic charts. The synthetic infographic charts are generated using resources that are permitted for academic use, including tabular data from openly licensed repositories: UN data (Public domain), Our World in Data (CC BY), VizNet (Hu et al. 2019) (CC BY-NC-SA), and Papers with Code (CC BY-SA); as well as images from publicly available resources with explicit reuse permissions: Icon645 (Lu et al. (02T) (CC BY-NC-SA), |Flticon (Royalty-Free License), Iconshock (Royalty-Free License). Heroicons (MIT License), and Google Material Icons (Apache License 2.0). Sensitive content. To mitigate the risk of exposing sensitive and personal content, ensure that all real infographic charts, tabular data, and image resources sourced exclusively from reputable public platforms or collected via search engines with built-in safe-search functionality, both of which generally employ safeguards. We further applied (emiin-220--Flass filtering exclude sensitive topics such as religious conflicts and individually identifiable medical records. 4. Human subjects. The user study (Sec. (..p was approved by the Institutional Review Board of the first author's 's university. Each participant was compensated with 30 USD for their participation. The study did not involve exposure to emotionally charged, political, or misleading content. Participants were shown infographic charts on neutral topics (e.g., bird population growth, energy sources) and asked to evaluate their quality. No sensitive or personally identifiable data was collected during the study. Participants were fully informed of their rights and were free to withdraw at any time.
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Chengyue Wu, Yixiao Ge, Qiushan Guo, Jiahao Wang. Zhixuan Liang, Zeyu Lu, Ying Shan, and large language
Ping Luo. Plot2Code: A comprehensive benchmark for evaluating models in code generation from scientific plots. arXiv preprint arXiv:2405.0 07990, 2024.
Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Peng Ye, Min Dou, Botian Shi, et al. ChartX & ChartVLM: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402.121885 2024.
Tianchi Xie, Minzhi Lin, Mengchen Liu, Yilin Ye, Changjian Chen, and Shixia Liu. In- fochartqa: A benchmark for multimodal question answering on infographic charts. arXiv preprint arXiv:2505. 19028, 2025.
Zhengzhuo Xu, Sinan Du, Yiyan Qi, Chengjin Xu, Chun Yuan, and Jian Guo. ChartBench: A benchmark for complex visual reasoning in charts. arXiv preprint 1521v:2312. 2023.
Cheng Yang, Chufan Shi, Yaxin Liu, Bo Shui, Junjie Wang. Mohan Jing, Linran XU, Xinyu Zhu, Siheng Li, Yuxiang Zhang, Gongye Liu, Xiaomei Nie, Deng Cai, and Yujiu Yang. ChartMimic: Evaluating LMM's cross-modal reasoning capability via chart-to-code generation. In The Thir- feenth International Conference Learning Representations, 2025.
Weikai Yang, Mengehen Liu, Zheng Wang, and Shixia Liu. Foundation models meet visualizations: Challenges and opportunities. Computational Visual Media, 1043):399-424 2024a.
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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, and Maosong Sun. MatPlotAgent: Method and evaluation for LLM-based agentic scientific data visualization. In Findings of the Association for Computational Linguistics, pp. 11789-1 1804, 2024b.
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung- Yeung Shum. DINO: DETR with improved denoising anchor boxes for end-to-end object detection. In The International Conference on Learning Representations, 2023.
Zhehao Zhang, Weicheng Ma, and Soroush Vosoughi. Is GPT-4V (ision) all need for automat- ing academic data visualization? exploring vision-language models' capability in reproducing academic charts. In Findings of the Association for Computational Linguistics: EMNLP, pp.
8271-8288, 2024.
Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Wanxiang Che, Zhiyuan Liu, and Maosong Sun. ChartCoder: Advancing multimodal large language model for chart-to-code generation. arXiv preprint arXiv:2501.0655 2025.
Jiangning Zhu, Yuxing Zhou, Zheng Wang, Juntao Yao, Yima Gu, Yuhui Yuan, and Shixia Liu. Infodet: A dataset for infographic element detection. arXiv preprint arXiv:2505. 17473,, 2025a.
Jinguo Zhu, Weiyun Wang. Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, al. InternVL3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504. 10479, 2025b.
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A DATA SOURCES AND USE POLICY DETAILS
Table lists the 18 chart-rich websites in our corpus together with links to their publicly available As stated in the Ethics Statement) ChartGalaxy is released a URL-only format and does not include, host, cache, or redistribute any chart/image files (including thumbnails or cached copies).
terms/licensing pages (accessed on 2025-09-16 and re-checked
Table 5: Chart-rich websites and their licenses.
Name
URL
License
Statista
statista.com
CC BY-ND 4.0 Customized
Visual Capitalist
visualcapitalist.com
World Statistics
world-statistics.org
BC SA-3.0
Our World in Data
ourworldindata.or
BY
OECD
oecd.org
CC BY 4.0
Openverse
openverse.org
CC
The Conversation
theconversation.com
CC BY-ND
Kaiser Family Foundation
kit.org
CC BY-NC-ND 4.0
World Economic Forum
wetorum.org
BC-CC- 4.0
Office for National Statistics
ons.gov.uk
OGL
Information is Beautiful
informationisbeattffll..tt
Customized
Pew Research Center MarketingCharts
pewresearch.org
Customized
marketingcharts.com
Customized
Chit Chart
chitchart.com
Customized
Wikimedia
wikimediatoundation.com
Customized
Hikaku Sitatter
hikaku-sitatter.com
Customized
Eurostat
ec.europa.eu europarl.europa.eu
Customized
European Parliament
Customized
Attorney review and scope Prior to releasing the dataset, we consulted a licensed Intellectual Property (IP) attorney. The attorney reviewed (i) the terms and licensing/usage pages of all 18 websites, and (ii) our dataset release format. The attorney's assessment that, under this URL-only release format, our dataset does not constitute republishing, redistribution, or reproduction of the underlying charts, and therefore does not violate the reviewed website terms. Users of this dataset responsible for ensuring that any downstream access via these URLs complies with each source's terms.
Source-specific conditions Based on the attorney's review of the 18 sources, the relevant conditions can be summarized as follows:
Attribution and copyright notice requirements (6 sources). Chil Charl requires that any shared use clearly indicate its copyright ownership and retain the required trademark/copyrigh notice. Marketing Charts requires attribution to the source website and acknowledgment of the original data source(s) associated with the chart. Eurostaf authorizes reuse provided that the source is acknowledged. European Parliament permits further dissemination provided that the entire item is reproduced and the source is acknowledged. Pew Research Center requires that copies display the full copyright notice. Kaiser Family Foundation permits citation or reprinting of specific charts, and notes that its original content remains copyrighted.
Use limited to non-commercial or personal purposes (3 sources). Hikaku Sitatter permits use only for not-for-profit purposes. Visual Capitalist permits use only for personal, non- commercial purposes and prohibits resale or redistribution. World Economic Forum permits sharing or redistribution only for non-commercial purposes with proper attribution and prohibits distributing modified versions.
No additional source-specific constraints (9 sources). For the remaining 9 sources listed in Table the posted terms reviewed by the IP attorney do not specify additional source-specific constraints that would restrict our URL-only release format beyond standard attribution and copyright notice requirements.
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B PIPELINE FOR DATA TABLE EXTRACTION
For real infographic charts, we extract a per-chart data table using LVLMs in a multi-step verification pipeline, where humans are involved in resolving inconsistent tabular results. Specifically, we first run Gemini-2.0-Flash and GPT-4o-mini in parallel and retain tables where both models produce consistent outputs. Two tables are considered consistent if they have 1) the same number of data points. 2) identical column names and categorical values, and 3) numerical values differing by more than 5%. The threshold of 5% is set by following the common practices (Methani et al. 2020) Masry et al.] 2022 2025). If the two tables are consistent but not exactly matched, we randomly select one for use. We also reviewed 200 randomly selected exact matches and found that all of them were correct. In contrast, if the two tables are inconsistent, we perform an additional verification step where GPT-4o also extracts table. If GPT-4o's output agrees with either of the models, we accept it. Otherwise, trained annotators from the service provider verify and correct the table. The success rates of this pipeline are:
43.6% of charts accepted directly when GPT-44-mini and Gemini-2.0-Flaa agree.
Among the remaining 56.4%, GPT-4o resolves 76.2% of the inconsistencies, resulting 86.6% of charts being automatically processed by LVLMs
The remaining 13.4% are manually verified and corrected.
This pipeline greatly reduces the need for manual annotation while ensuring reliable outputs.
C DATASET DIVERSITY
Geographic diversity We evaluated the geographic diversity of the whole dataset using Gemini- 2.5-Flash to extract country and region references based on the UN geoscheme. Infographic charts with geographic information account for 59.4% of the total, with the most frequently referenced regions being North America (33.0%), Europe (23.1%), Asia (19.3%), and Africa (10.6%). While the dataset spans a broad range of regions, there is a moderate skew toward North America and Europe, reflecting the distribution of original sources (c.g., Visual Capitalist, Statista).
Domain diversity Likewise, we analyzed domain diversity in our dataset by prompting Gemini- 2.5-Flash to assign each chart topic based on the IPTC Media Topics taxonomy, a widely used hierarchical ontology for classifying news content. The result shows that the infographic charts in Chantalaay span 16 out of 17 top-level categories (excluding a sensitive category on religion conflict) and 99 out of 120 topics defined in the taxonomy. The most frequently occurring topics include public health (7.3%), business enterprise (6.0%), and environmental conservation (3.9%). These findings indicate that our dataset captures wide and balanced distribution of real-world domains.
Linguistic diversity Following ChartQAPro Masry al. 2025). we measured linguistic diversity using the average pairwise cosine distance between Sentence-Bia BERT embeddings extracted by the Mll-MiniLMM----- M-L6-v2 model (Reimers & Gurevych] 2019). Our dataset achieves a linguistic diversity score of 0.8754, higher than ChartQAPro (Masry et al. (025) (0.8439) and Chartxiv (Wang et al.) 2024) (0.7831). This result indicates that ChartGalaxy exhibits greater linguistic diversity than existing datasets. The real infographic charts predominantly use English, with additional content in languages such as Spanish, German, and French, reflecting the linguistic diversity of online data. All synthetic data in our dataset is in English, which can be easily translated into other languages upon request, offering flexibility to meet users' diverse linguistic needs.
Representativeness of synthetic infographic charts To evaluate the distributional alignment of our synthetic infographic charts with real infographic charts, we conducted an additional quantitative analysis. Specifically, we extracted DreamSim (Fu et al., (023) features (dimension = 1792) from both real and synthetic infographics, applied UMAP for projection with r_neeghhorss = 100, and measured grid-based coverage over a 20x20 spatial grid. We define coverage as the percentage of grid cells occupied by real infographic charts that also covered by synthetic ones. The result shows that our synthetic charts cover 97.62% of the feature space occupied by real infographic charts, demonstrating that synthetic infographies are highly representative of real infographics.
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D CHART TYPES AND VARIATIONS
We provide a full list 75 chart types and 440 chart variations.
D.1 CHART TYPES
We summarize chart types observed in the collected real infographic charts, drawing on two existing taxonomies: Data Viz Project and Datylon, The diversity of chart types ensures that our synthetic infographic charts can adapt to a wider range of scenarios and data representations, making them valuable for model training and evaluation. The full list of 75 chart types is in Figs. 20
D.2 CHART VARIATIONS
For each chart type, we include multiple stylistic variations, designed along the following dimensions:
Element shapes, such as rounded bars and curved bars.
Icon placement, such as positioned above data elements and beside labels.
Rendering effects, such as hand-drawn style and 3DD style.
Element alignment, such center-aligned layout and edge-aligned layout.
Gridline and axis design, ranging from minimalist to detailed ticks and axes.
These variations were observed from real-world infographics and verified by design experts, who also added complementary styles to ensure coverage and diversity. This results in variation space that reflects real-world design patterns and supports diverse chart generation. We provide illustrations of the variations under each chart type (Figs. 20)).
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Type 1: Vertical Bar Chart (with 27 variations)
3
3
3
2
2
2
C
D
A
B
C
D
A
B
C
D
A
Plain chart #1
Plain chart #2
Plain chart #3
Plain chart M4
2
D
A
B
C
D
A
B
D
Plain chart #5
3D style
style
Replace bars with pictograms
(2 variations)
10
11
12
3
AA
B
C
D
A
8
C
D
Add icons next to the labels variations)
Add Icans to the bars (4 variations)
Hand-drawn style (4 variations)
Add grid lines and Y-axis (4 variations)
Type 2: Vertical Stacked Bar Chart (with 10 variations)
2.5
2.5
2
2.5
1.5
1.5
1
1
0.5
1.5
0.5
1
1.5
1.5
0.5
1.5
1.5
1
1.5
2
0.5
0.5
0.5
A
B
C
D
Plain chart #1
Plain chart #2
3D
style
Add Icons next to the labels
variations)
2.5
1.5
0.5
1.5
1.5
0.5
1.5
0.5
0.5
B
A
D
Add grid lines and Y-axis veriations)
Hand-drawn style (2 varistions)
Figure 5: 75 chart types and 440 chart variations (Part 1).
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Type Vertical Grouped Bar Chart (with 24 variations)
3
A
B
A
B
Plain chart #1
Plain chart #2
Plain chart
Plain chart
6
3
3
3
3
1
A
A
B
2.5D style
3D style
Hand-drawe drawn style (4 variations)
Add icons next to the labels variations)
10
3
Add icons to the bars variations)
Add grid linas and Y-axis (4 variations)
Type 4: Horizontal Bar Chart (with variations)
A
20
2
B
2
B
2
40
C
C
3
C
3
60
D
D
D
80
Plain chart
chart
Plain
2
2
B
3
Replace bars with pictograms variations)
Plain chart #5
Plain chart #6
Plain chart #7
Figure 6: 75 chart types and 440 chart variations (Part 2).
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10
11
12
A
B
A
2
8
0
3
0
C
0
A
Hand-drawn style variations)
grid variations)
(7 varistions)
[5 veriations)
Type 5: Horizontal Stacked Bar Chart (with 10 variations)
4
0.5
0.5
0.50
5
0.50.
A
8
1.5
1.5
C
1.5
1.5
1.5
1.5
1.5
1.5
1.5
2.5
1.5
2.5
1.5
5
1.5
2.5
Plain chart
Plain chart
Add ioons next the labels varlations)
Add grid lines X-axls variations)
0.5
0
13
Hand drawn style (4 variations]
Type Horizontal Grouped Bar Chart (with 14 variations)
Plain chart 41
Plain chart #2
Add icons next to the labels (2 variations)
Add icons to the bars (2 variations)
A
B
Hand-drawwn style (4 variations)
Add grid lines and X-axis variations)
Figure 7: 75 chart types and 440 chart variations (Part 3).
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Type 7: Radial Bar Chart (with variations)
Plain chart #1
Plain chart W2
Add loons next the labels variations)
Add icons to the end of the bars
(2 variations)
Type 8: Radial Stacked Bar Chart (with variations)
Plain chart #1
Add
Add the end the bars
Type 9: Radial Grouped Bar Chart (with variations)
A
Plain chart #1
Plain chart #2
Plain chart #3
Add icons to the labels variations)
Type 10: Circular Bar Chart (with variations)
chart
Plain chart #2
Icons top variations)
Hand-drawn (3 varlations)
Type Circular Stacked Bar Chart (with variations)
Type 12: Circular Grouped Bar Chart (with 2 variations)
B Plain chart #1
Plain chart
Add
Figure 8: 75 chart types and 440 chart variations (Part 4).
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Type 13: Pictorial Percentage Bar Chart (with variation)
Plain chart #1
Type 14: Histogram (with variations)
3
3
2
2
02'
D1'
DZ'
style
Replace
Type 15: Lollipop Chart (with variations)
Plain chart
Plain chart
Add icons to lollipops [2 variations)
Type 16: Dot Chart (with 3 variations)
D
3
2
3
chart
Replace Icons
icons labels
Figure 9: 75 chart types and 440 chart variations (Part 5).
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Type 17: Diverging Bar Chart (with 26 variations)
-1
A
-1
-2
2
8
2
B
2
D
Plain chart #1
Plain chart #2
Plain chart #3
Plain chart #4
8
A
III|III||
-2
3
||
3
D
Hand-drawn style variations)
Add icons next to the labels variations)
Plain
Piain
10
A
-1
Add Icons to the bars variations]
Add grid lines and X-axis variations)
Type 18: Vertical Bar Chart With Circle (with 10 variations)
5
2
A
A
D
A
B
Plain chart #1
Plain #2
labels (2 variations)
Add icons to the bers (2 variations)
Hand-drawn style (4 variations!
Figure 10: 75 chart types and 440 chart variations (Part 6).
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Type 19: Horizontal Bar Chart With Circle (with 10 variations)
5
Plain chart #1
Plain chart
Add icons variations)
Hand-dras ford--drwm style (4 varistions)
Type 20: Vertical Dot Bar Chart (with 3 varlations)
Plain chart #1
Plaii chart
Add icons neat to the labels
Type 21: Horizontal Dot Bar Chart (with variations)
|||||
Plain chart #1
Plain chart #2
Add icons next to the labels
Type 22: Dumbbell Plot (with 2 variations)
Type 23: Span Chart (with 2 variations)
?
A
B
A
B
C
Plain charta1
Plain chart #2
Plain chart 411
Plain chart 42
Type 24: Bump Chart (with 6 variations)
2
2
2
3
chart
Plain chert
Pain chart
Add Icons next the labels (3 variations)
Figure 11: 75 chart types and 440 chart variations (Part 7).
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Type 25: Line Graph (with 15 varlations)
C2'
08'
00
01'
02'
Plain chart I1
Plain chart
Plain chart #3
Add circles with proportions encoding variations)
AA
00'
01'
02'
03'
Add icans the lines varlations)
Add to labels varlations)
Type 26: Spline Graph (with variations)
06'
03'
00'
03'
Plain chart #1
Plain chart #2
Plain chart A3
Add icons next the lsbels
Type 27: Stepped Line Graph (with variations)
01'
02'
03'
00
02'
03'
Plain chart #1
Plain chart #2
Add icons next labels
Type 28: Slope Chart (with 2 variations)
3
00
07'
02'
03'
Plain chart m1
Add icons next to the labels
Figure 12: 75 chart types and 440 chart variations (Part 8).
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Type 29: Small Multiples of Line Graphs (with 2 variations)
Type 30: Small Multiples of Spline Graphs (with variations)
pe on pe
pa
A
M
Plain chart
Add icons next to labets
Add
labels
Type 31: Small Multiples of Stepped Line Graphs (with 2 variations)
Plain chart
Add Icons next to the labels
Type 32: Area Chart (with variations)
Plain chart #1
Plain #2
Plain chart #3
Plain chart nd
U3'
Put the Icon of the largest group in the background
Type 33: Spline Area Chart (with variations)
Type 34: Layered Area Chart (with variations)
3
01'
03'
00'
01'
D2
03'
D0
DT'
02'
03'
00'
01"
02
00'
Plain chart #1
Add icons next the labels
Figure 13: 75 chart types and 440 chart variations (Part 9).
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Type 35: Layered Spline Area Chart (with 2 variations)
Type 36: Range Area Chart (with variations)
00
01'
07'
03'
01'
03'
00
02'
Plain chart #1
Add icons next to the labels
Plain chart m1
Add icons on the endpoints of the lines
Type 37: Stacked Area Chart (with variations)
00
01'
02'
08'
00
01'
02
08'
03
01'
02'
DB'
Piain #h
Piain chart #2
Add icons the areas
Type 38: Radial Area Chart (with 2 variations)
Type 39: Radial Spline Area Chart (with 2 variations)
B
H
2
F
Plain chart #1
Add grid lines and tick values
Plain chart #1
Add grid lines and tick values
Type 40: Radial Layered Area Chart (with 2 variations)
Type 41: Radial Layered Spline Area Chart (with 2 variations)
H
H
C
Plain chart #1
Add grid liee and tick values
Plain chart #1
Add grid lines and tick values
Type 42: Radial Range Area Chart (with variations)
Type 43: Radial Stacked Area Chart (with variations)
Plain chart #1
Add grid lines and tick values
Plsin chart #1
Add grid lines and tick values
Figure 14: 75 chart types and 440 chart variations (Part 10).
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Type 44: Diverging Area Chart
Type 45: Diverging Spline Area Chart
(with variations)
(with 2 variations)
02'
02'
02'
02'
68'
08'
Piain chart
Add Icons on the areas
Plain chart
Add Icons on the areas
Type 46: Small Multiples of Area Charts (with 3 variations)
07
0T'
02
03
0C'
o1'
02
03'
00
on
02'
US
Piain chart
Add icons next to the labals
Add icons on the areas
Type 47: Small Multiples of Spline Area Charts (with variations)
D0'
01
02'
03'
00
01
02'
03'
00
DT
02
09'
Type Chart (with 6 variations)
10
10
FA
40.0%
20 D1
50.0%
edge
Hand-drawn style (3 variations)
pies
Type 49: Donut Chart (with 6 variations)
10.0%
1O DYS.
10.0%
20.0%
40.0%
0%L
Plain chart
Add icons on the edge of sectors
Add Icons in the center of sectors
Hand-drewn veriations)
Figure 15: 75 chart types and 440 chart variations (Part 11).
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Type 50: Semicircle Pie Chart (with 6 variations)
60.0% 0%
60 0%
60.0%
Addiconns the cage pies Type Semicircle Donut Chart (with variations)
Plain chart #1
Add icens the center pies
Hand-drown style (3 variztions)
30,0 0%
20 0%
10.0%
NO
Plain chart #11
Add edge
Add Icons the center sectors
Add icons on the edge of sectors #2
Hand-drawn style (4 veristions)
Type 52: Rose Chart (with variations)
Plain chart #1
icons Hand-drawn style veriations)
Types 53-57: Small Multiples of Pie Charts, Donut Charts, Semicircle Pie Charts, Semicircle Donut Charts, and Rose Charts (with total 32 variations)
JOC3
PARTS
AUS
MN
C/
PAES
LECS
Small varistions)
Small multples of semicircte donut charts (8 variations]
variations)
pie variations)
Small multiples of rose charts (6 variations)
Figure 16: 75 chart types and 440 chart variations (Part 12).
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Type 58: Radar Line Chart (with 2 variations)
Type 59: Radar Spline Chart (with 2 variations)
B
B
C
F
F
chart
Add grid lines and tick values
Plain chart #1
Add grid lines and tick values
Type 60: Small Multiples of Radar Line Charts (with 2 variations)
B
G
G
2
F
Group
Group
Plain chart #1
Add grid lines and tick values
Type 61: Small Multiples of Radar Spline Charts (with 2 variations)
2
D
Group
Group 2
Group
Plain chart #1
Add grid lines and tick values
Type 62: Proportional Area Chart (with 19 varlations)
B
B
B
7%
7%
7%
7%
E 135
13%
13%
5%
13%
5%
D
C
C
9%
9%
9%
chart
Plain
style variations)
5
7%
E
7%
13%
5%
E 13%
A
C
9%
D
11%
11%
9%
15%
Hand-drawn variations)
icons varlations]
Replace
Figure 17: 75 chart types and 440 chart variations (Part 13).
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Type 63: Scatterplot (with variations)
Q
Plain chart #1
Plain chart #2
Replace scatter points with icons variations)
Type 64: Grouped Scatterplot (with 6 variations)
Plain chart
Replace scatter points with icons (3 variations]
Type 65: Bubble Chart (with variations)
Type Heatmap (with variation)
?
Plain chart #1
Replace bubbles with
Plain #1
Type 67: Waffle Chart (with variations)
Plain chart (horizontal)
Plain chart #2 [vertical)
Hand-drawn style (2 variations]
Type 68: Small Multiples Waffle Charts (with 4 variations)
Plain chart #1
Hand-drawn style
Add icons next to the labels (2 variations)
Figure 18: 75 chart types and 440 chart variations (Part 14).
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Type 69: Alluvial Diagram (with 4 variations)
O
Plain chart #1
Plain chart #2
Add ioons next to labels (2 variations)
Type 70: Gauge Chart (with 2 variations)
2
2
Hland-drawn-o style
Type 71: Small Multiples of Gauge Charts (with 12 variations)
)
Plain chart
Plain chart
Plain chart #3
Plain chart #4
2
A
2
B
Hond-drawn style (4 veriations)
Add icons next the labels (4 varialions)
Type 72: Funnel Chart (with variations)
Plain chart
Plain chart
Add icons next to the labels (2 variations)
Hond-drawn style
Figure 19: 75 chart types and 440 chart variations (Part 15).
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Type 73: Pyramid Chart (with 7 variations)
c
Plain chart #t
Plain chart
Add icons next to the labels variations)
Hand drawn style variations)
3D
Type Treemap (with variations)
A
style
style
6
Add icons next to the labels (4 variations)
Add icons in the centers of areas 14 variations)
Type Voronoi Treemap variations)
Plain
drawn style 12 variations)
(3 variations)
Figure 20: 75 chart types and 440 chart variations (Part 16).
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E CHART TYPE SELECTION
We determine chart types by analyzing the data attributes and their characteristics. First, we apply rule-based mapping ( that identifies candidate chart types based on data attribute combinations. If multiple candidates remain, we instruct Gemini-2.0-Flaat to select the best fit by evaluating the compatibility between the data and these chart types. The selection prompt shown below provides candidate chart types, their descriptions, and key data statistics to inform the decision.
Table 6: Mapping rules defining attribute combinations for chart type selection. C, N, and 'T' are abbreviations for Categorical, Numeric, and Temporal attributes, respectively. The notation X & indicates distinct attributes of type X. When symbol is specified (e.g., for the Diverging Bar Chart), it indicates that the first categorical attribute must contain exactly two distinct values.
Chart Type
Attribute Combinations
Vertical Bar Chart
Cx x
Vertical Stacked Bar Chart
Cx2+N X
Vertical Grouped Bar Chart
Cx2+N
Horizontal Bar Chart
C
Horizontal Stacked Bar Chart
C
Horizontal Grouped Bar Chart
C
Radial Bar Chart
C X
Radial Stacked Bar Chart
C
Radial Grouped Bar Chart
Cx
Circular Bar Chart
Cx X
Circular Stacked Bar Chart
Cx X
Circular Grouped Bar Chart
C
Pictorial Percentage Bar Chart
Cx
Histogram
C I
Lollipop Chart
CxI+NxI
Dot chart
Cx 1,C22+XX
Diverging Bar Chart
C X
Vertical Bar Chart With Cirele
C X
Horizontal Bar Chart With Circle
CxI+N
Vertical Dot Bar Chart
Cx1+N
Horizontal Dot Bar Chart
CXI+N X
Dumbbell Plot
TXI+N ++C1
Span Chart
Cx X
Bump Chart
Tx T
Line Graph
Tx X X I +N X C
Spline Graph
T I X T N I+C
Stepped Line Graph
T N XX N
Slope Chart
T N 1
Small Multiples of Line Graphs
T X
Small Multiples of Spline Graphs
TX I
Small Multiples of Stepped Line Graphs
T
Area Chart
TX I I
Spline Area Chart
T 1 + X L,X I N
Layered Area Chart
T I +N X I
Layered Spline Area Chart
T I N I T I N X 1+CX
Range Area Chart
Stacked Area Chart
T + X I
Radial Area Chart
T I + X 1
Radial Spline Area Chart
T 1 + X X
Radial Layered Area Chart
T 1 + C
Radial Layered Spline Area Chart
T 1 N C
Radial Range Area Chart
T K + X
Radial Stacked Area Chart
T I N X + C X
Diverging Area Chart
T I N I C C
Diverging Spline Area Chart
T I N
Small Multiples Area Charts
T N X X
Small Multiples of Spline Area Charts
T I X
Pie Chart
Cx
(Continued on next page)
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Table 6: (Continued): Mapping rules defining attribute combinations for chart type selection.
Chart Type
Attribute Combinations
Donut Chart
Cx
Semicirele Pie Chart
C + N X
Semicircle Donut Chart
C 1 + X
Rose Chart
Cx
Small Multiples of Pie Charts
C I
Small Multiples Donut Charts
2
Small Multiples of Semicircle Pie Charts
C X
Small Multiples of Semicircle Donut Charts
C X
Small Multiples of Rose Charts
C
Radar Line Chart
Cx Cx1+N X
Radar Spline Chart
Small Multiples of Radar Line Charts
C X
Small Multiples of Radar Spline Charts
C C X
Proportional Area Chart
Scatterplot
C 1 X
Grouped Scatterplot
C X
Bubble Chart
Cx1+N X
Heatmap
NX2
Waffle Chart
Nx1
Small Multiples of Waffle Charts
Cx1+Nx1
Alluvial Diagram
Cx 1
Gauge Chart
Nx1
Small Multiples of Gauge Charts
CxI+NXI
Funnel Chart
Cx1+NxI
Pyramid Chart
Cx1+Nx
Treemap
Cx2+N
Voronoi Treemap
Cx2+NXI
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INPUT
Candidate Chart Types: (Caniidate Chart Types]
Chart Descriptions: [Chart Descriptions]
Attribute Statisties: (Attribute Statistics)
Keep concise and direct.
INSTRUCTION
Role: You are an expert assistant in visualization and chart selection.
Task: Select the single most optimal chart type from (Candi date Chart Types) using their (Chart Descript ons} and AAtti Statist's cs.. Prioritize data-chart compatibility: the chart must clearly and accurately represent key data insights. Instructions Selection:
1. Analyze all provided inputs.
Leverage your expertise interpret how (Attribute Statistics) (e.g.. categorical car- dinality, number of temporal points, numerical value ranges, cumulative nature of data) im- pact the effectiveness and clarity of each candidate chart type, considering their (Chart Descriptions).
Evaluate chart types based on descriptions, common visualization best practices, and your terpretation of (Attribute statistics) to identify the most insightful and unambiguous visualization.
Primary goal: maximize data-chart compatibility.
Output Format: Return only the name of the single selected chart type.
# EXAMPLE OF TASK EXECUTION
Input:
Candidate Chart Types: ("Line Graph", "Area Chart", "Spline Graph"]
Chart Descriptions: "Line Graph: Emphasizes trends and rate of change over time; best for non-oumulative data with sufficient peints.
Area Chart: Shows trends and volure/magnituuee over time; good for cumulative data or showing part-to-whole over time. Spline Graph: A Line Graph with smoothed curves, visually softens trends, suitable for data with many points or where less angular look is desired. Attribute Statisties: { "temporal points": 30, "numeric min value": 15,
"numeric_max_value"" 450 }
Output:
Line Graph
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F LAYOUT TEMPLATES
We summarize 68 layout templates from real infographic charts. We represent the layout template using the ISON format, which describes existence elements (chart, image, text), (2) their pairwise positional relationships, and (3) overlap relationships between their bounding boxes. A concrete example is shown below.
+ Element Existence "tit "title": "yes", "image": "yes", "chart": "yes",
"yes" or "ne"
# Positicn Relationships "title=to-chartt"; "left-top", top, bottom, and 7 other options "image-to-chart"", "right-top", "title-to-image"; "left",
# Overlap Relationships Between Bounding Boxes
"charttttitlee-overllap. "no", "yes" or "no" "chart-imagee-verraa"" "no", "title-image-overla""" "no"
The illustrations 68 templates are shown Figs. and 22
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TEXT
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A
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AA
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TEXT
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29
30
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TEXT
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Figure 21: 68 layout templates (Part 1).
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34
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Figure 22: 68 layout templates (Part 2).
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G EXAMPLES OF SYNTHETIC INFOGRAPHIC CHARTS
Figs. 23 and 24 consist of synthetic infographic chart examples that offer a quick preview of ChariGalaxy. To access the full dataset, please visit our dataset repository
Space Industry Revenue Accelarading Growth Path
Comeddy Dominates Among
Younger Liclanars, Newn for 45+
U.S. Exports to Cuba: Peeka and Fluctuations
Millee Tober
Us mown soure to Sus for USS
Earcky Different Countries
Transparttion Most Greethoun Can Emiesiore
Cheapest Most Expersive City
U.S. Consumer Price Index Rises from 1997 to 2017
So.th K2/M
Caker (W072007 mes ner #1M5
Liled Coptin 00
22%
Farro
21A2
Military Expenditure as Percentage of GDP by Country
Musice
[-)
Broak Cost Rankings
13%
3:2
Junen
1N
valle www.tcartty acreen nators.
1999
3762
2504
VP Tickets Command Higher
Throughout Day
VIP
Oell
Diamond Necklaces
TikTok Outperforms Other Platforms ROI
HAVE HIGHEST MATERIAL COSTS
oC%S
Figure 23: Synthetic infographic chart examples (Part 1).
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Gurdyy
silamry
THIS IS HOW APPLE MAKES MONEY
Electric Vehicle Adoption Ratan on the Rian
dephic acus 20b
DUSE SAGTIOTS 20O1
ChotoPT Al Todt Genoration Markot in 2022
20.0%
COFFEE CONSUMPTION
Buar
20
AXUCSS
MAX
M
SOVIET UNION SUFFERED
Textile Waste Diversion Rates Across U.S. Cities
HIGHEST CASUALTIES
111.00
Fampryy
Traadadiing Adkantage Technolog Trave Booking
Yertical Farming
Adoption Rates
Asia
Digital Art Dominates ART SCHOOL ENROLLMENT
Labels Dominate Music Streaming
Rocket Engine Thrust-to-Weiht Ratio Increases
Pattee 5%
DEEP NETWORK DATA RATE
Ooye are stsanayy
skbor plyletll Luder
rereurcch Frardy ond pelrs
19%.
Arai
195
NEW
SEA
15%
Figure 24: Synthetic infographic chart examples (Part 2).
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H EXTENDED EVALUATION AND RESULTS
H.1 INSTRUCTION DATASET FOR INFOGRAPHIC CHART UNDERSTANDING
Table All infographic chart understanding questions with definitions and examples included in our
test set.
Question
Description
Example
Text-Basee Reasoning
Data Identification (DI)
Identify and report specific data values from the chart based on lextual references.
Which Country has AdoptionRate closest to 29.9? Select the correct answer from the following options: A. India, B. China, C. Germany. D. South Africa
Data Comparison (DC)
Compare multiple data points and their relationships to analyze relative values and derive insights.
What is the difference between the highest and lowest MedalCount? Please provide a numerical answer.
Data Extraction with Condition (DEC)
Extract specific data points from the chart that meet certain conditions.
What is the total PollutionLevel of Beijing in the given image? Please provide numerical answer.
Fact Checking (FC)
Verify statements about data by cross-checking information against the visual representation.
Is the ApplicationRate of Spain consistently less than the ApplicationRate of Italy across all Years? Answer with exactly "Yes' or 'No'.
Essual-Element-Basee Reasoning
Data Identification (DI)
Identify data values associated with specific visual elements (c.g., icons, symbols) the chart.
What is the WaterConsumption for in the Industrial group? Please provide numerical answer.
Data Comparison (DC)
Compare data values associated with different visual elements, requiring cross-modal reasoning.
What is the difference between the AverageTeacherSalaryy of and Please provide numerical answer.
Data Extraction with Condition (DEC)
Extract data values from visual elements when specific conditions are met.
What is the total Population A iin the given image? Please provide numerical answer.
Fact Checking (FC)
Verify claims about data represented by visual elements, integrating visual and textual information.
Is InterceptionRate 2021 in less than that in di? Answer with exactly "Yes" 'No'.
Visual Understanding
Style Detection (SD)
Identify design styles and formatting choices used in the chart.
What is the alignment style of the main text content (chart title and subtitle)? Select the correct answer from the following options: A. left-aligned, B. center-aligned, C. right-aligned, D. justified
Visual Encoding Analysis (VEA)
Analyze how data dimensions encoded using visual properties (e.g.. color, size, position, icons).
What data attribute or dimension is represented by icons in the given infographic chart? Select the correct answer from the following options: A. Location, B. WinPercentage, C. Team, D. None
Chart Classification (CC)
Identify the type chart hased its visual characteristics and structure.
What types of charts are included in this infographic? Select the correct answer from the following options: A. Radial Grouped Bar Chart, B. Circular Grouped Bar Chart, C. Stacked Bar Chart, D. Diverging Bar Chart
Training details We fine-tune two M. in our experiments. The first is Intern (7hu et al. 2025b). which combines the Qwen2.5-7B language model with the Intm visual encoder. The
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second is Qwen2.5-VL-7B (Bai al. )025). which integrates the same Qwen2.5-7B language model with Vision Transformer architecture optimized by Qwen. These models are fine-tuned on our instruction dataset for 2 epochs using a global batch size of 128. For parameter-efficien tuning, we utilize LoRA (Hu et al. (022) with a rank of 8. This training process is conducted on 8 NVIDIA 4090D 48G GPUs, leveraging Deepspeed ZerO for parallelized training. For both Intern VL3-8B and Qwen2.5-VL-7B. the learning rate is set to 5 x 10 and we employ cosine learning scheduler with warm-up ratio of 0.1. The LoRA parameters, and r, both set 32.
Evaluation details Our test set is designed to evaluate chart understanding capabilities infographic charts. It includes variety of question types. which categorized into eext-based reasoning. visual- deesent-basedd reasoning. and visual understanding. Table provides a detailed breakdown of these categories, including the definition and an illustrative example for each specific question type.
Limitations of pre-trained models in visual design literacy As shown in Table fine-tuning dataset leads to notably larger improvements in visual understanding tasks, such as Style Detection (SD) and Visual Encoding Analysis (VEA), with average gains of +42.5% for InternVL3 and +42.3% for Qwen2.5-V2 In contrast, the improvements on the remaining tasks more modest (+20.2% for InternVL3 and +17.2% for Qwen2.5-V1.). This gap indicates that existing pre-training pipelines pay relatively little attention visual design literacy: understanding how visual attributes like color, style, and other encodings convey information. Our dataset helps compensate for this missing capability by providing tasks that require such understanding. Incorporating such tasks and data into pre-training could meaningfully strengthen models' visual design literacy.
Analysis of performance gap between two benchmarks As shown in Table there is performance gap of nearly 40 percent between InfographicVQA and ChartQAPro. As we can only infer from the design characteristics of the two benchmarks, we attribute the performance gap between Infographic VQQ [2] and ChartQAPro [3] differences in task and question design. InfographicVQAA mainly focuses visual extraction tasks, while CharlQAPro emphasizes "complex analytical reasoning." introducing more challenging question types such as "hypothetical questions." "conversational questions," and "unanswerable questions" that require reasoning beyond simple data retrieval. This distinction is also reflected in human performance reported in their original papers: InfographicVO) reaches 70% accuracy, while ChartQAPro only achieves 85.02%. Overall, these results suggest that current models handle recognition well, analytical reasoning remains a major gap.
Ablation results of fine-tuning with different datasets We conducted additional fine-tuning experi- ments two datasets, CogAlign (Huang et al. 2025a) and ChartGemma (Masry al. (2024b) We fine-tuned on each dataset using the same LoRA adapters, hyperparameters, and training setup as in Sec. 4.1 trained for one epoch. We used the same two models as in Sec. 4.1 Intern and Qwen2.5-V2 V- 7B, and evaluated performance on ChartQAPro and Infographic VQA. The results are shown in Table
Table 8: Model fine-tuning results with CharlGemma, CogAlign, and ChartGalaxy on two bench- marks.
Model
ChartQAPro
InfographicVQA
Intern VL3-8B
38.15
76.19
ChartGemma
38.60
76.23
CogAlign
37.24
74.19
+ ChartGalaxy
44.13
79.99
Qwen2.5-VL-7B
37.97
78.59
ChartGemma
38.26
78.94
+ AogAlign
37.48
77.73
ChartGalaxy
41.56
83.03
Among the three datasets, ChartGalaxy yields the largest performance improvements for both models across both benchmarks.
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ChartGemma brings relatively modest improvements, likely because its design, optimized for tasks such chart summarization, chark-to-Markdown extraction, and program -ardd solving, aligns only loosely with the complex analytical reasoning required by ChartQAPro and the visual extraction required for Infographic VQA
CogAlign results in performance drop, indicating negative transfer from task discrepancies. It emphasizes low-level geometric cues typical of visual arithmetic,, and the resulting gradients likely dominate the shared LoRA updates, thereby suppressing the higher-level reasoning capabilities required for complex QA. This observation is consistent with the findings of al. (2024), who highlight that the limited trainable capacity of LoRA-based methods can hinder cross-tan task generalization under divergent task distributions.
To further understand the role of syathetic data in chart understanding. conducted ablation experiment chart understanding. Specifically, fine-tuned IntemVV3--BB Qwen2.5-VL-7B on real and synthetic infographics separately. and compared their performance on three benchmarks: the ChartGalaxy evaluation set, ChartQAPro, and Infographics VQQ.
Ablation Study on Real and Synthetic Data
As shown in Table the models fine-tuned on both real and synthetic infographics perform the best, with observable advantages over those fine-tuned solely real synthetic data. We further observe that the gains from synthetic data particularly notable infographies provide bounding-box annotations for charts, text, images, which facilitates generation of complex instructions, especially for visual-elemen based reasoning.
evaluation set. This improvement arises because synthetic
Table 9: Comparison between real, synthetic, and combined data on three benchmarks.
Model
ChartGalaxy evaluation set
CharlQAPro
InfographicsVOA
VL3-8B
53.20
38.15
76.19
Real
55.74
42.58
78.67
+ Synthetic
76.08
41.76
77.92
+ Both
80.07
44.13
79.99
Qwen2.5-VL--7B
56.50
37.97
78.59
+ Real
58.76
40.47
81.76
+ Synthetic
77.80
40.07
81.01
+ Both
80.35
41.56
83,03
Additional Analysis on Smaller Model We conducted additional experiments with a smaller model, Qwen2.5-VLL-3B. and fine-tuned on the ChartGalaxy QA dataset (Sec. 4.1). The evaluation was performed on three henchmarks: the ChartGalaxy evaluation set, ChartQAPro and InfograpieVQA.
Table 10: Evaluation of Qwen2.5-VL-3B with and without fine-tuning on ChartGalaxy QA dataset.
Model
ChartGalaxy evaluation set
ChartQAPro
Infograpic
Qwen2.5-VL-3B
52.42
30.89
74.39
ChartGalaxy
71.01
37.08
78.43
The results show consistent improvements across all benchmarks after fine-tuning. Notably, the model achieves a + +8.59 gain the ChartGalaxy evaluation set and 16.19 ChartQAPro, demonstrating that smaller models also effectively benefit from training ChartGalaxy. These findings suggest that our dataset is broadly applicable and valuable even for resource-constrained VLMs.
H.2 BENCHMARKING INFOGRAPHIC CHART CODE GENERATION
Settings Table 12 lists the 17 LVLMs along with their API names used in this experiment. Since our task involves generating executable code with relatively long outputs, we conducted small-scale pilot study to assess the basic code generation capability of each model. Based on this, excluded models that consistently failed produce meaningful or complete outputs under our task setting
example, Phi-4 due to their limited capacity or inability to handle long sequences. For illustration. we present the results of two smaller models (Qwen2.5-VL- VL-77, Phi-4-6B) on our benchmark in Table [1 We use greedy decoding (temperature across all models to ensure deterministic outputs. To maximize the chance of obtaining complete and executable code, configure each model generate many tokens as possible, setting the maximum generation length
0)
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min(16384, A), where A denotes the model's maximum generation limit. This helps mitigate the risk of incomplete outputs, which result in non-xxecutable code.
Table 11: Evaluation results of Qwen2.5-VL-7B and Phi-4-6B.
Model
Exec. Rate
Low-Level
High-LLevel
Overall
Qwen2.5-VL-7B
43.80
13.85
9.25
11.55
Phi-4-6B
1.60
0.29
0.07
0.18
Table 12: API names of the evaluated LVLMs.
Model
Type
API name
Gemini-2.5 P.5-Pro (Google 2025b
Proprietary
Gemini-2.5 2.5 Flush (roople 202.8
Proprictary
Claude-3.7
(Aillropee))
Proprictary
GPT-4.1 OpenAl 20250
Proprietury
9pt-4.1
G- OpenAT 2025g
Proprietary gpt-4.1-mini
GPT-4.1-nano (ppenA
Proprictary
gpt-4.1-nano
OpenAI-o4-mint (OpenAl 2075d
Proprietary 04-ming
OpenAI-o3 (OpenA) 20.bd
Proprictary
OpenAI-ol (OpenAl
202b1
Proprictury
GPT-4o (OpenAA SopieATT22222 2025b
Proprictary
gpt-4o-2024-11-23
Doubao- 1.3-oo----V-inon Pis Bytee 2025
Proprictary
Ibsbaeel..5-viiiin--rrr--
Moonshot-vl-Vssion Moonshot
Proprictary
Maaea---Mavrrick-111
-1rerrickk--7s- 78 Mea 2025
Open-Source
Open-Source hat.:aai/....aneeaaeoeeeee-eeeee
Llama-4-coutt1172 (Mela 2025
Instruettt
Qwen2.5-VL--228 [Ba
2075
Open-Source
Qwwen2.5VVL-3222 VL- Bai el al.
2023
Open Source 5xem://wen2255-01-228--11--
IntemVLL-78BB Zhu et al.
2025b
Open- Soen-Souree internv13-78b
Benchmark details Our benchmark includes 75 chart types and 68 layout templates in ChartGalax, The associated tabular data contains average of 15.02 data points. We also compute statistics the number of SVG elements in all infographic charts, this metric partially reflects the complexity of reproducing given chart. On average, each chart contains 77.93 SVG elements, including 28.52 text elements and 8.07 image elements. Other commonly used visual elements include rect (M = 24.36), circle (M = 6.21), path (M = 5.78), and Line (M = 3.21). Among all chart types, waffle charts are the most element-dense with an average of 677.55 elements, while funnel charts the simplest, with average of only 14.60 elements.
Evaluation Metrics We present the details of the evaluation metrics of benchmark, including the high-level score and low-level score.
the high-level score, we employ GPT-4o (OpenAl 2025b) assess the visual similarity between the PNG image rendered by the generated code and the ground-truth one. We instruct GPT-4o to evaluate the similarity along six dimensions: data element, layout, text, image, color, and validity. The model outputs score for each of the six dimensions, which then summed to produce total score ranging from to 100. The detailed prompt for this evaluation is provided in Supp. [22
The low-level score evaluates fine-grained similarity hetween SVG elements of the rendered chart and the corresponding ground-truth chart. This evaluation is conducted through three steps: 1) decomposing hoth charts into SVG elements, 2) matching elements between the two charts, and 3) computing similarity metrics based on
the matching results (Si al. 2025 Chen al. 2024). Algorithm I presents the pseudo-code for the matching procedure. The full implementation details are available in our publicly accessible code repository
?
Based on the matching results, the low-level score computed as the average six similarity metrics: area, text, image, color, position, and size. Let the parsed SVG elements of the ground-truth chart and the generated chart be denoted by G = [4), (91.92..... .........fh..................... }m and P = (p1, PPi.P...... .P.), respectively, and let the set of matching pairs between and be M. where (i,j) E M indicates that gi is matched with py. The detailed definitions and calculations of these metries are provided below.
The area metric quantifics the proportion of the matched element areas relative to the total element areas:
Easee (S(g.) + S(p,)
Area
(1)
Emt S(g:) >, S(p,)
where S(-) denotes the size clement.
httpps://ii thwp::///gtttbbcooooo httpoo/..om//aaaaaaaaaaaaaaaaaaa
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Algorithm 1 SVG Element Matching Algorithm
Require: gt gt_leo.fs: Ground truth SVG elements.
Require: pr_leafs: Predicted SVG elements.
Require: gr_matched: Array for ground truth matches (init with -1).
Require: pr_matched: Array for prediction matches (init with -1).
Ensure: Updated matching information between elements.
1: < |gl_leu,f (). +- [pr_leu.''
2: CostMatriz <- ZeroMattixx(m,
3: i 0.....
do
4: j n do
5:
CostMMatiiz((li]] <- LeafCost(gt_Leaff pr Ieee f(]])
6: end for
7: end for
8: (rows, cols) f- Hungarian (CriiihhhCCooMMMMaaa
(Cori.aatriz) A
Returns optimal row-column pairs
9: for each pair (i.j) in (rows, cols) do
10:
if (artMatriaz(]]] []] <
AND gt pt_matched]]] -1 AND pr_ pr_matched]]] -1 then
gi_matehed j
12:
pr_matched] j <
13: end
14:
end for
The text and image metrics evaluate the similarity of generated text and image elements, respectively, by averaging the similarity scores over all matched pairs text and image elements. Unmatched text and image elements in ground-truth charts are assigned similarity score of to penalize generation failures. For text elements, similarity is computed using the character-level Sprensen-Dice coefficient, defined as twice the number of overlapping characters divided by the total number of characters in the two strings (Si et al. 2025 For image elements, similarity is measured using the CLIP embedding- based similarity (Radford
2021
The color, position, and size metries assess visual consistency across matched elements with respect to their respective attributes. The color metric employs the CIEDE2000 formula (Luo et al. (200)) to measure the perceptual difference between the colors of matched elements. The position and size metrics are defined as follows:
Position
max (IX(o)) X(P))), (Y.) (lp)
(2)
JM
(K.))M
where (X(e), (e)) denotes the normalized coordinates of the center of element e,
Size
1
Σ
S(g:) S(p,) max (Sx.),,)),
(3)
JM
(L.))MM
where S(-) denotes the size of an element.
Additional Analysis on LVLM performance We present additional results and analysis of LVLM performance on our benchmark, focusing on generated code length, performance across varying levels of complexity, and qualitative outcomes from the three top-perfominng models.
Generated code length and model performance. Table 13 reports the generated code length and overall performance 17 L.LMs, with corresponding visualizations in Fig. 25 Code length is measured by token count using the GPT-2 tokenizer across all executable code segments. The statistics reveal significant variation in generated code length among different LVLMs. Among the 12 proprietary models. GPT-4. 1-mini produces the longest code average (7.656.7 71 tokens), surpassing both 1 (5,237.78 tokens) and GPT-4.1-1n I-nno (2,387.0 .55 tokens). This longer code length may partly explain GPT-4.1-mi 's comparable performance to GPT-4.1. 1. In contrast, Gemini-2.5 2.5-Pro and Gemini-2. 2.a-hlash generate code of similar average length (6,662.45 vs. 6,508 22 tokens). OpenAl's 3 o o-series models produce the shortest average code length among top performers (2.866.75 for OpenAl-o4-mini, 2,662.11 1 for OpenAl-ol, and 3,1 14.74 for OpenAl-o3), which may reflect their ability to generate more concise solutions. Among the five open-source models, Qwen2.5-VI-3321 has the longest average code length despite achieving the lowest overall score, while the remaining models exhibit comparable average lengths. These findings highlight the distinct coding styles of different LVLMs when generating extended code sequences.
Model performance across different complexity levels. To comprehensively assess L.L.M performance across varying degrees of difficulty, divide our henchmark into three splits corresponding different complexity
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Table 13:
Generated code length and overall scores of different LVL.Ms. We measure code length in terms of tokens, utilizing the GPT-2 tokenizer.
Model
Length (AVG.)
Length (STD.) Overall
Proprietary
Gemini-2.5-Pro
6,662.45
1,674.01
85.21
GPT-4.1
5,237.78
1,675.36
80.00
Claude-3.7-Soneet
5,870.50
1,552.48
79,91
GPT-4.I-min
7,656.71
2,488.46
79.69
OpenAI-o4-mon
2,886.75
735.19
75.97
Gemini-2.. Somini-2.5-FFaa 2.5-alshh
6,508.22
1,934.79
75.55
OpenAl-ol
2,662.11
1,018.11
74.69
OpenAl-o3
3,114.74
999.66
74.22
GPT-4o
2,962.72
613.42
65.67
GPT-4.
2,387.05
1,306.72
60.06
Doubao- 1.aeno--..--iii -5-Viiion
3,878.70
1,347.10
47.11
Moonshot-vl-Vee
3,087.17
791.16
44.39
Open-Source
-17B
3,460.76
856.62
61.29
Qwen2.5-VL-12
3,512.20
1,236.29
57.09
Intern VL3-78B
3,376.52
758.71
55.07
Llama-4-Scout-177
3,247.25
826.56
51.91
Qwen2.5-VL. VLL-32
4,960.82
1,264.43
46.48
Q.5ini2.5PPr
85
GPT-4.1
Clause 1eeee-3----0 7-5onnet
GPT-4.1mm mini
Openkl-o-miii Open4--01 OpenAl-ad
Gemini-2 2.5min22.P1i Flash
Overall Score
GPT-40
GPT-4
80
Interi/1..33 3-799
55
lama Soout- 178
Doubao 1.5 Vislon Pro
Given Given2 VLV-222
Moonshot vi Vision
3000
4000 Average Code Length (Tokens)
5000
6000
7000
Figure 25: The average code length and overall scores of the evaluated LVLMs.
levels. We adopt straightforward heuristic based the number of elements in infographic chart define complexity levels. While this simple approach does consider layout complexity or chart types, it provides practical and quantifiable basis for stratification, with more sophisticated measures left for future work. Based on this criterion. split the benchmark into easy (240 infographic charts). medium (169), and hard (91) subsets. Fig. 26 presents the overall LVLM performance across these splits. Gemini-2.5-PP consistently demonstrates superior performance all levels of complexity. For models, performance declines predictably difficulty
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increases. Interestingly. Gemini-2.5-Fii 2.5-Flaah and OpenAl-o3 exhibit improved results the hard split, suggesting enhanced capability in understanding complex relationships among large number elements.
90
Easy
Medium
Hard
Overall Score
70
60
8
4
40
2.5p
Claude Claude-3.7-Sonmel SoSmett
GPT-4.1
GPT44 1-mini
OpenAt-o4-mini
Gemini-2 F-Falahh
OpenAl-o1
OpenAt-o3
Llama-4 Maae----aaikk-22 17B
GPT-40
GPT-4. 1-nana
Qwen2 5-VL-72B
IntermLL3 L--788 3-788
1oma--4Scout-12- Scout-177 17B
Doubao-1 S-viion-PPro
Qwen2 5-V2--VL-- LL-22-
Moonshotvv V-Visinn
Llema-4
Figure 26: Overall scores of LVLMs across different complexity levels.
Qualitative examples. To illustrate model performance on our benchmark, we present examples in Fig. 27 showing ground-truth charts alongside rendered ones from the generated code of the three top-performing LVLMs (Gemini-2.5-PP) GPT-4.1, and Claude-3.7-Sond 7-Sonnet). These examples demonstrate the models' relatively strong ability generate code for infographic charts. Nevertheless, substantial room for improvement remains. Notably, specialized chart variations (shown in the last four rows of Fig. the models struggle to accurately reproduce spatial arrangement and data encoding.
Extended experiments We conduct two extended experiments analyze the effects of different prompting methods and the thinking budget parameter model performance. Both experiments performed randomly sampled subset of 100 infographic charts from benchmark dataset.
Prompting methods. addition to the direct prompting method, we evaluate three alternative prompting strategics following prior work (Yang ct al. 2025 Si ct al. 2025):
HintEnhanced, which guides the model layout, chart type, and data;
on key aspects the given infographic chart, such as
TableAug, which provides auxiliary tabular data the model;
SelfReftection, where the model is provided the ground boound-truth truth chart, previously generated code from direct prompting, and the rendered chart, and instructed revise generated code.
Detailed prompts are available in our code repository The results, summarized in Table 14 show that the SelfReflection method consistently achieves the best performance across models.
Thinking budget. Recent LVLMs have incorporated internal reasoning mechanisms that allow them perform intermediate "thinking" steps prior to final output generation. This pracess is governed by thinking budget parameter, which controls the token budget allocated for internal reasoning and is supported only by some
https://gii https:://gitbbb..cmmm http:///gttubhocoo
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C
Ergled Soikery Sy Tecuodep
Lndare Curtary Adsotton
Ground truth
(h) Gemini-2.5-Prro
(c) GPT-4.1
(d) Claude-3.7-Somee
STEM
Enodewers Band Derarts
2C
(e) Ground-trun
(f) Gemini-2.5-Pro
(g) GPT-4.1
(h) Claude-3.7 3.7-Sonnet
00
Sarevela bnidriee APCC
Ground truth
i Gemini-2.5-Pro
(k) GPT-4.1
()) Claude-3.7 3.7ude--.7-Soneet
(m) Ground truth
(n) Gemini-2.5-Pro
(o) GPT-4.1
(p) Claude-3.7 Claude-3.--Sonnet
(q) Ground truth
(r) Gemini-2.5-PP 2-o-PPo
(s) GPT-4.1
(t) Claude-3.7 Claude-3.7-Somee 3.7-Sonnct
Figure 27: Qualitative comparison of the generated infographic charts by Gemini-22 2.5-PPoo GPT-4.1, and Claude-3. Claude-3.7-Sonne 7SSonet with the ground-truth ones.
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Table 14: Performance comparison of LVLMs with different prompting methods.
Model
Method
Exec. Rate
Low-Level
High-Level
Overall
Area
Text
Image
Color
Position
Size
Avg.
GPT-4o
Direct
100.00
94.88
95.62
86,95
88.46
89.53
68.09
87.25
83.59
85,42
HintEnhanced
100.00
94.18
95.26
86.28
87.88
88.72
68.44
86.79
84.36
85.58
Gemini-2.5-Pro
TableAug
100.00
93,91
95.55
87.01
87.05
88.54
68.75
86.80
83.67
85,23
SelfReflection
100.00
93.89
95.25
88.68
88,51
89.45
68.65
87.40
84.19
85,80
Direct
100.00
78.24
58.51
59.53
66.35
74.00
47.74
64.06
58.42
61. 24
HintEnhanced
100.00
79.31
56.13
$2.06
67.01
75.14
48.47
63.02
55,56
59.29
Llama-4-Mo Maaeaarc---rrik----
TableAug
100.00
81.64
61.24
61.75
68.66
74.44
48.97
66.12
59.23
62.67
SelfReficction
100.00
82.44
58.93
66.19
68.11
75.45
48.93
66.67
59.46
63.06
Table 15: Performance comparison of LVLMs with different thinking budgets.
Thinking Budget
Exec. Rate
Low-Level
High-Level
Model
Overall
Area
Text
Image
Color
Position
Size
Avg.
GPT-4o
1024
100.00
92.29 94.64
78.82
85.55
87.28
66.62
84.20
76.89
80.55
Claude-3.7-Sonee
4096
100.00
92.91
93.28
82.42
86.42
88.10
67.22
85.06
76.12
80.59
8192
100.00
93.78
93.85
87.12
87.24
88.40
67.22
86.27
78.71
82.49
1024
97.00
90.18
89.60
78.51
80.57
84.88
62.13
80.98
75.69
78.34
Gemini-2.5-Flan
4096
98.00
86. 74
84.66
72.57
82.33
85.91
64.59
79.47
74.51
76.99
8192
97.00
86.45
87.19
74.05
78.31
84.09
62.82
78.82
75.55
77.19
reasoning-ente enabled models, such as Claude-3.7-Sonae and Gemini-2.55Flaas In our earlier experiments, we set the thinking budget to 1024 tokens for these models. For other reasoning models without explicit support for this paramcter, we simulate the budget constraint by instructing them to limit internal thinking to 1024 tokens via prompt design. To investigate the effect of the thinking budget in greater detail, conduct additional experiments varying this parameter for Claude-3.7-Somun increasing the thinking budget for Claude-3.7 3.7-o-ee.- leads to clear improvements in the image similarity metric and overall score. Conversely, for Gemini-2.5-Flass metries, including the area, text, and image metrics. These contrasting behaviors indicate that the impact of the thinking budget parameter on LVLM performance is model-dependent and warrants further investigation.
and Gemini-2.5-Flash As shown in Table [5]
larger thinking budget correlates with declines in multiple
Human Evaluation To evaluate the reliability of our metric, we conduct a within within-subject human study and measure the degree to which the metric's 's assessments agree with human judgments.
Participants. We recruited three participants, all graduate students in computer science with extensive experience in visualization and machine learning. Upon completion, each participant received $15 compensation.
Data. We sampled 600 pairs of generated charts and their corresponding overall scores from the dataset used in Sec. 4..2 Each pair contains two infographic charts produced by different LLMs for the same ground-truth chart. obtain representative and well-balanced sample, we examined the full distribution of model scores and score differences across all generated results. We then drew samples to ensure uniform coverage across both score levels and score- dife-diffeeence ranges.
Task Design. For each pair, participants were required to choose the chart that is more similar to the ground-truth chart, with both the ground-truth chart and the two options displayed the screen. The order of the two options was randomized, and participants were unaware of which model produced which chart. When evaluating similarity, participants were instructed to consider both the low-level and high-level aspects outlined in Sec 4.2 Each participant finished the comparison of all 600 pairs, leading to 1,800 total trials.
Result Analysis. We analyzed both the consistency hetween participants ensure the quality of human preferences and the consistency between the metric and participants validate the effectiveness of the metric. Three participants reached agreement on 537 out 600 pairs (89.5%), and Fleiss' Kappa was 0.8598 (almost perfect agreement). indicating strong consistency among participants. The agreement rales between the model and the three participants were 93.17% 17%, 93.17%. and 90.67%, respectively. The corresponding Cohen's 's Kappa values were 0.8633, 0.8633, and 0.8133 (almost perfect agreement). On average, the agreement rate between the metric and participants was 92.33%, with a Cohen's Kappa 0.847. This demonstrates that our metric aligns well with human judgment.
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H.3 EXAMPLE-BASEE BASED INFOGRAPHIC CHART GENERATION
The study was approved by the Institutional Review Board the first author's university. the user study, each participant was compensated with USD for their participation. study not involve exposure to emotionally charged, political, or misleading content. Participants were shown infographic charts on neutral topics (e.g.. bird population growth, energy sources) and asked evaluate their quality. No sensitive or personally free user study interface using specific example. Figs, [29 39000pppp present all 30 triplets of infographic charts: one reference. two infographic charts to be rated that are generated by GPT-Image-1 and our method, respectively.
identifiable data was collected during the study. Participants were fully informed of their rights and withdraw at any time. Fig. (28 28llssstss
Energy Source
Electricity Generated (TWh)
Nuclear
390.0
Wind
277.0
Coal
2/2.0
Hydro
245.0
ELECTRICITY GENERATED BY ENERGY SOURCE
ENERGY GENERATION SOURCES
1
Nuclear
390
Solar
116 TWh
Gas
320
Nacheer
Hydro
390twh
245
Wind
Total 1,620Twh
Coal
272
Twhh
Hydro
245
320Twh
Wind 277
116o
Solar
100
200
300
400
Please rate infographic chart on the left.
2
Fidelity Aesthetics
Creativity
Please rate infographic chart on the right.
2
3
5
Fidelity
Aesthetice
Creativity
Back
Next
Figure 28: Screenshot of the user study interface. Participants were asked to compare two infographic charts generated by our method and GPT-Image-1 based on the same dataset and reference infographic chart, and rate each chart on three metrics: fidelity, aesthetics, and creativity, using a 5 5-point Likert scale.
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Reference
GPT-Image
Ours
Reference
GPT-Image-1
Ours
at Work
Averge Oummaade Time
Fastest Way Class
Forcign- Born
Anerage Wead Valke
VALASSUN
MENENAL
QUALITY
BREATHE
HOW APPLE MAKES MONEY?
LEADING NATIONS
Sollia
EU EMIGRANTS
BEETISITY IOUAUT
ENERGY
The Highest
BRGALE BY COUBTRRY
Bleyck
SIZK
S3SK
Al MODELS
GLADER MELT RATES
SOB QQARTS
Unicorns
ART SDUCATION
Art
$2208
92998
!1T!!
TOP MANUFACT UIBERS
ENTISECTTIN
Healing Art
J4VEST
Al
REWARD
RAILWAY COMPANIES
SALCTIOOL STATES
POPULATION
Bird Population Growth
Gradit
Botally
DARASCIE BRAZS
WHEN WNNING
CROSSES LINE
Hydrapower Capacity
SPACT FINNNNG BRVEAL
INDIAN BROKERS
HYDROPOWES OWERR
BUELING INNOVATION
Auste Leudliiy Pay
Figure 29: Infographic charts used in the user study: 30 triplets, each comprising a reference, a GPT-generated, and chart generated by our method (Part 1).
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Reference
GPT-Image-1
Ours
Reference
GPT-Image-1
Ours
POVERTY BATES
REE TRARRI
LAND COVER
CONBESTION STT
SMERING
A STARTUP BUSINESS FAIL
STUDENT LOAN DELINOVERC??
STUDENT LOAN DILLINN LENCY
TELEERN COMPUREES
SOLM CAAECHAACEE
CA PACIITYY
Popyltttio Prodection
Walkabilis and Chacage
ETEP FROM TRANSIT
FERTILIZER
FIELD
BIRTHS
RAISE TIELD
ARTS COUNCIL
Emuplove! Students
Traditional Listeners
CELERTIIN AIPWEES
to TIE
2B
ENERGY
Happiest
NONEETT AND
SAUTBAZT
SPEOTIUM
TECH RACE
THE CCISS DIVIDE
Coear C mapecad and
PODCAST LISTENERS
PLANT
Fallint Birth Rules
BIRDS
11.4%
Ie 020
1200
WILD DLIFE SUCCESS SATE
PERSONAL SAVINGS
SIUING
Successiyas
RESIPITANIE
POWER
BERR
Figure 30: Infographic charts used in the user study: 30 triplets, each comprising a reference, GPT-generated, and chart generated by our method (Part 2).
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Wilcoron signed rank tast, p-3 0
Wilccoron signed rank tast, 0See5
Wilicoron signed test, p-O 000481
Durs
3.05e-5
3.0 3.05e-5
0.000481
GPT-Image-1
Maximum
75th percentile
3-
Median
percentile
Minimum
Ours
GPT-Image-1
Oure
GPT-iaage11
Oure
GPTHmage-1
(a) Fidelity
(b) Aesthetics
(c) Creativity
Figure 31: Performance comparison between our method and GPT-Image-I1
Fig. 31 presents the results of the Wilcoxon signed-rank test conducted on the user study data.
Fidelity. Ours significantly outperforms GPT- GPT--maae--1 in terms of fidelity. The average score for Ours is 4.63, compared to 2.10 for GPT-Image-1. The difference is statistically significant (V 136, p = 3.05e-5), indicating that our method produces infographic charts that participants perceive as more accurate and faithful to the underlying data. This result can be attributed to our use of the template-based infographic chart creation method, which ensures numerical correctness and consistency between visual encodings and underlying values. In contrast, GPT- GPT-Image--1 often exhibits delate- issues, such incorrect labels, mismatched bar heights, and duplicated or omitted data elements.
Aesthetics. terms of aesthetics, Ours receives an average score of 4.14, while GPT-Image-1 scored 2.90. The difference is statistically significant (V= 136, p= 3.05e-5). This difference is likely due to our method leveraging layout templates extracted from reference infographic charts, along with carefully designed color palettes to enhance visual harmony. In contrast, GPT--mage-1 occasionally produces unbalanced compositions or relies on overly simplistic and repetitive color palettes, reducing the overall visual appeal.
Creativity. Ours also achieves higher scores in creativity, with an average of 3.95 compared to 2.65 for GPT- Image-1. 1. The difference is statistically significant (V = 136, p= 0.000481), suggesting that our method yields designs that are seen as more original and creative. This may be explained by our efforts to incorporate creative elements, such embedding meaningful icons into the titles. and by exploring less conventional chart types beyond basic bar and line charts. In contrast, GPT-Image-1 tends to generate conventional titles and favors basic chart types, leading to lower perceived creativity.
H.4 ABLATION STUDY OF THE INFOGRAPHIC CIARRT CREATION PIPELINE
We conducted ablation study to assess the contribution of key modules in generation pipeline. We constructed four ablation variants, each removing one key module and replacing it with a simpler baseline:
Title generation: replace the RAG-based method (Sec. 3.4) with direct LLM generation.
2) Image recommendation: replace recommended images with randomly selected ones.
3) Chart rendering: replace programatically implemented charts with chart images generated by GPT- GPT-Image-1 using only the chart type and tabular data.
Layout optimization: initialize and adjust layouts without enforcing template-specified spatial constraints.
We evaluated fidelity and semantic consistency with participants 40 pairs infographics, each consisting of one output from our full pipeline and one from an ablation variant (balanced across scttings). Participants rated each infographic on a 5- 3-point Likert scale: fidelity reflects how accurately the chart represents the underlying data, while semantic consistency measures how well the visual content aligns with the intended message.
The results show full pipeline achieves high fidelity (4.82) and semantic consistency (4.50). The chart rendering module is most critical for fidelity: replacing it with GPT-Image- reduces the score 3.08 due to label errors, misalignment, and incorrect data encoding in LLM-ggeeraan affected by layout optimization, image recommendation. and title generation. with scores dropping to 3.12, 2. 2.90, and 3.66, respectively. These results confirm that all four modules play essential roles in the quality and interpretability of the synthesized infographic charts.
charts. Semantic consistency is most
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Table 16: Ablation results key modules of the infographic generation pipeline (Mean, (95% CTJ).
Method
Fidelity
Semantic consistency
Full pipeline
4.82, [4.71, 4.93]
4.50, [4.30, 4.70]
w/o Title generation
4.62, [4.46, 4.78)
3.66, [3.45, 3.87]
w/o Image recommendation
4.56, [4.40, 4.72]
2.90, [2.70,
wo Chart rendering
3.08, [2.77, 3.39]
2.58, [2.30, 2.86]
w/o Layout optimization
4.60. [4.44, 4.76]
3.12, [2.90, 3.34]
I PROMPTS FOR DATA PROCESSING
1.1 INSTRUCTION DATASET FOR INFOGRAPHIC CHART UNDERSTANDING
This section details construction of our instruction dataset for infographic chart understanding. We categorize the generated question- answer (QA) pairs into two types: prompt-based pairs and template-based pairs.
The prompt-based pairs are produced through LLM prompting. Specifically, Gemini-2.0- is provided with both the tabular data underlying the chart and the corresponding chart image to produce question-answer pairs. These pairs cover forms of reasoning: text-based reasoning and visual-element-basee reasoning. They further span several categories, including Data Identification (DI), Data Comparison (DC), with Condition (DEC), and Fact Checking (FC). For text- rext-based reasoning, the generated questions directly reference the textual data attributes in the chart. For disual-elemeng based reasoning, questions are created by first determining whether the data attributes referenced in generated question correspond to icon representations the chart image; when such icons present, the textual attributes are replaced by their respective icons.
Extraction
template-basee pairs are mainly derived by adapting templates from ChartAssistant (Meng et al. 2024]. In addition, style-related questions for Visual Understanding (e.g.. Style Detection, Visual Encoding Analysis, and Chart Classification) are also remplate-based since they are derived directly from the chart styles used in our generation process and do not rely LLM prompting.
To generate question-answer pairs for Data Identification (DI), where the goal identify and report specific data values from the chart based on textual references, the following prompt used:
DATA
(TTabular data)
Follow the data shown in the table strictly; keep answers concise and direct; avoid contradicting the table data.
# INSTRUCTIONS
Generate straightforward Factoid Questions alongside their Corresponding Answers for the given image. The questions should focus on direct identification and extraction of explicit information such as specific data values, labels, titles, axis information, or quantities directly readable from the chart or its textual components. Avoid questions requiring inference, multi-step calculation, or comparison between multiple distinct data points. The Answers should be number, text lahel, common phrase (Yes, No) found directly in the data. Respond Array of ISON objects format with the following keys: (i) Question, and (ii) Answer.
EXAMPLES
"According to the line graph, what was the 'Population' in 'New York' during '2010'?"
"What are the units indicated on the Y-axis of the 'Sales Performance' chart?"
"In the pic chart legend, which category does the color blue represent?"
For Data Comparison (DC) question-anssn answer pairs, which involve comparing different data points or trends, this prompt utilized:
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DATA
[Tabular data]
Follow the data shown in the table stricdl; keep answers concise and direct; avoid contradicting the table data.
# INSTRUCTIONS
Generate some of the most difficult Factoid Questions alongside the Corresponding Answers for the given image. The questions could be related numerical visual reasoning. These questions should focus making comparisons between different data points, categories, or time periods, and identifying significant differences or relationships between multiple elements in the data. The Answers could be a number, text label, or a common phrase (Yes, No). You should respond in an Array of JSON objects format with the following keys: (i) Question, and (ii) Answer.
# EXAMPLES
"Which year had the highest gap between the headline inflation and inflation?" the years which the line was higher than
line, which year the smallest difference
hetween the red and green lines?"
"Which country had the highest increase in the number of cases between Jun and Jul?"
"Which country had the most significant drop in its share of the global hashrate between Aug 2021 and Sep 2021?"
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For Data Extraction with Condition (DEC) question-answer pairs, which require extracting specific data points from the chart that certain conditions, the following prompt used:
DATA
(Tabular data)
Follow the data shown in the table strictly; keep answers concise and direct; avoid contradicting the table data.
# INSTRUCTIONS
Generate some of the most difficult Factoid Questions alongside the Corresponding Answers for the given image. The questions could be related to numerical or visual reasoning. These questions should focus on identifying trends, making comparisons, finding threshold crossings, analyzing patterns of change, or identifying significant events in the data. The Answers could be number, text label, or common phrase (Yes, No). You should respond in Array of JSON objects format with the following keys: (i) Question, and (ii) Answer.
EXAMPLES
"Estimate the year in which wind capacity first exceeds 100 gw based on the trend shown in the chart." "Determine the airline with the highest increase in ghg emissions from 2008 to 2014."
"How many times the retail sales growth went below the average annual percentage change from 2002 to 2010 by more than 2%?"
"Which event caused most significant drop followed by quick recovery
lines?"
The following prompt is used to facilitate Data Extraction with Condition (DEC) by generating questions that require calculations based on specific data points extracted from the chart under certain conditions:
# DATA
(Tabular data)
Follow the data shown in the table strictly; keep answers concise and direct; avoid contradicting the table data.
INSTRUCTIONS
Generate some of the most difficult Factoid Questions alongside the Corresponding Answers for the given image. The questions could be related numerical or visual reasoning. These questions should focus performing calculations based
data, such computing percentages, averages, rates of change, other mathematical operations on the values presented. The Answers could be number, text label, or a common phrase ( (Yes, No). You should respond an Array of JSON objects format with the following keys: (i) Question, and (ii) Answer.
# EXAMPLES
"What is the average growth of renewable energy capacity between 2010 and 2015?"
total investment 2019 was $100 million, how much would allocated to healtheare sector based on the percentage shown?"
"Calculate the compound annual growth rate (CAGR) of smartphone sales from 2015 to 2020."
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The following prompt is used for Data Extraction with Condition (DEC) the context of hypothetical scenarios, where questions require extrapolations based data points extracted under specific assumed conditions:
DATA
(Tabular data)
Follow the data shown in the table strictly; keep answers concise and direct; avoid contradicting the table data.
# INSTRUCTIONS
You Al that generates concise and specific hypothetical questions based chart images. Your task to analyze the chart and generate a short, data-driven hypothetical question that explores future trends, impacts, or extrapolations based on the data. Avoid adding unnecessary explanations or context like "Based on the chart data..." or "A meaningful hypothetical question could be...". Keep the question focused and directly related the chart. The question should make assumption about future trends, impacts, or extrapolations based the data.
# EXAMPLES
"If the average wealth per person in Asia increases by 50%, what will be the new average wealth per person in Asia?"
"If the Construction index had stayed flat at its 2010 level throughout 201 1-2013, would the overall Industry index likely have remained below its early 2011 peak?"
"If the Gini index continues to rise the same rate as did from 1980 to 2010, what the Gini index
in 2025!"
The following prompt designed for Fact Checking (FC), generating question-answer pairs that require verifying statements about data by cross-checking information against the visual representation the chart:
# DATA
[Tabular data
Follow the data shown in the table strictly; keep answers concise and direct; avoid contradicting the table data.
# INSTRUCTIONS
You are an that generates concise and specific factoid questions hased on chart images. Analyze the given chart image and generate 2-3 pairs of claims and verdicts about its data. Half of the claims should be supported by the chart's data, while the other half are refuted. Avoid using terms like "rows", "columns" "elements" from the data table; refer to "chart" "chart image" instead. If the claim is supported. the verdict should "True". the claim refuted, verdict should "False", followed by brief explanation. The claims should cover comparisons of values or trends, basic statistical values (maximum, minimum, mean, median, mode) without using exact numbers from the chart. Ensure diverse range of claims addressing various visual aspects of the chart, resulting in 2-3 turns of claims and verdicts. Generate the verdicts/answers without any additional explanation.
# EXAMPLES
"Hong Kong consistently has the lowest percentages least three categories compared to other East Asian countries in the chart."
"The 4th grade reading pass rate at Auburn Elementary had improvement of about 8% from year 2014 to 2017."
TToronto has the lowest average technology salary among the cities depicted in the chart."
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Published as a conference paper at ICLR 2026
1.2 BENCHMARKING INFOGRAPHIC CHART CODE GENERATION
Prompt for instructing LVLMs We instruct LVLMs generate code for the provided infographic chart figure with the following prompt.
You are expert data- visualizaziin engineer and front-end developer. Your task is take chart image and generate HTML file that, when loaded in browser, reproduces the chart exactly. The chart must be centered the viewport.
Constraints:
No Explanations: Do not include comments, reasoning, explanatory text. Output only valid HTML with JavaScript code.
# Technical Requirements
Charting library: Use D3.js to implement the chart. Write the code be clean, modular, and easy to understand and modify.
Single file output: Provide one standalone HTML. file that includes everything needed render the chart.
Chart fidelity: Replicate all visual clements-shapps, colors, axes, labels, legends, fonts, line weights, markers- exactly as in the original image.
No animations: The chart must render immediately in final state.
Aspect ratio & sizing: The chart's content area (including margins, paddings, and plot area) must match the original image's proportions precisely.
Image: Recreate any icon/image content using SVG <g> and shapes. Do not use <image>, base64 images. or links.
Text: Place text <text> nodes. Do not use <tspan> nested text structures.
# Grouping Requirements
You should following class names for SVG groups with specific semantics:
t title: The title area (may contain title, subtitle, and shapes)
image: Each individual icon/magg/ppicoogram (no annotation grouped images)
Legend: Legend area
axis: Axis area
# Output Format
Return only the following standalone HTML file:
< DOCCTYPE html>
<html lang="en">
<head>
<meta charse:="UIF-B" />
<title>Recreated Charts/title>
<!-- <style> -->
</head>
<body>
<div io"dhhart- js'
<script
</script>
<script>
<!-- Your D3 code here
</script>
</body>
</html>
Only output this file. No comments or explanation. Keep it minimal and strictly within the token limits.
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Published as a conference paper at ICLR 2026
Prompt for the high-level prompt.
We instruct GPT-4o provide high-level with the following
You are an expert evaluator of visualizations. The first image (reference) is an infographic chart rendered from ground truth HTML code, while the second image is an infographic chart rendered from Al generated HTML code. Your task is assess how well the Al-generated chart replicates the reference chart. Scoring Criteria (Total: points):
Data Element (20 points):
Does the Al-generated chart accurately replicate all visual elements that encode data (e.g.. bars, points, lines)?
The presence of extra or missing data elements that do not match the reference chart will negatively impact the score.
Are the positions and lengths/sizes of data elements consistent with the reference, such that the encoded values they represent appear similar?
2. Layout (20 points):
Does the layout of title, chart area, and images/icons in the Al Al-generaeed chart replicate the spatial arrangement of the reference chart?
alignment of the elements in the generated chart (e.g., left-aligned, right-aligned) consistent with that the original chart?
Were element positions preserved, or did significant misalignments occur?
The white space inside the generated chart should be similar the original, and the aspect ratio of the whole infographic chart should he preserved.
3. Text (15 points):
Does the Al-generated chart replicate all relevant text content accurately? This includes titles, axis labels, and annotations.
4. Image (15 points):
Does the Al-generated chart reproduce image elements from the reference infographic chart (c.g., thematic images, embedded icons)?
How visually similar are those image elements?
5. Color (10 points):
Does the Al-generated chart match the original one in terms of colors (background color, line colors, fill colors, text colors, etc.)? Minor differences due to rendering or anti-alinn anti-aliasing can be tolerated if the overall color scheme is preserved.
6. Validity (20 points):
Is the Al-generated chart clear, readable, and free of overlapping occluded elements? Are fundamental charting conventions followed? For example: Are axis ticks aligned with data, icons placed near corresponding data, colors in legends consistent with the data elements, and is axis-data correspondence preserved?
# Evaluation Output Format (in JSON):
Please provide your evaluation the following format valid JSON):
"data_elemeni element ":
"data
"score": <integer>,
"comment": "<your comment>
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Published as a conference paper at ICLR 2026
"layout"; {
"score" <integer>,
"comment": "<your comment>
"text":
"score": <integer>,
"conment"; "<your comment>*
"image":
"score": <integer>,
"conment": "<your comment>"
"color":
"score": <integer>,
"conment": "<your comment>*
"validity";
"score": <integer>,
"comment": "<your comment>
"total_score"" <aum of all scores above>
Be precise and detailed in your comments, and ensure the total equals the sum of all individual scores.
1.3 EXAMPLE- BASED INFOGRAPHIC CHART GENERATION
You are an experienced infographic chart designer. Given the following datasct in JSON format: (Tabular data) and reference infographic chart image (used as style guide) [Chart image), your task is to create new infographic chart that clcarly and creatively visualizes the data.
# INSTRUCTIONS
Maintain overall stylistic consistency with the reference infographic chart, including color scheme, typography, iconography, and visual tone, ensure coherent aesthetic. However, adapt the layout, chart types, and visual elements creatively to best suit the structure and insights of the new dataset. Prioritize effective communication of the new data over replicating the original design. Incorporate visual storytelling elements, such icons, labels, contrast, scale, to highlight key patterns or contrasts in the data.
Include a clear, well-designed title that matches the tone and aesthetic of the reference.
You may choose light or dark background - whatever best fits the visual narrative and legibility. Legends, axes, and any necessary annotations should be present and styled consistently.
# OUTPUT FORMAT
single high tigh-resolution infographic chart image (portrait or square format)
All text and numbers should be fully readable
The image should be self-explanatory
no external explanation should be needed
The style should be clean, professional, and ready for publication
Avoid any explanation outside the visual the final image should be self-explanatory and visually engaging.
THE USE OF LLMS
LLMs were used solcly to aid or polish writing; they were not used for research idcation, analysis, or any other part of the manuscript.
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Reviews:
[{"review_id": "u5dzAsfUvE", "reviewer": "Reviewer_6nvx", "summary": "This paper presents ChartGalaxy, a dataset for chart understanding and generation. It contains over 1 million infographics, some of them sourced from the real world and some of them auto-generated. Authors show three use cases with the dataset: improving infographics QA, benchmarking infographics generation, and enabling example-based infographic generation.", "questions": "- The authors say templates capture the spatial relation of chart elements, but how are templates represented? Is each template a D3 code snippet that can take in different data tables and images as input? Do you use a VLM to generate these templates? And how does layout optimization work with these templates?\n- All Chit Chart charts seem to not wrong urls. They all have https://chitchart.com/ without further specification (e.g., 00060283, 00059496). Could the authors check if this is the case please?\n- The authors say \"Moreover, we construct an independent, human-verified evaluation set containing 2,176 synthetic charts with 4,975 question-answer pairs.\" Are these a sampled and verified subset of all VLM-generated questions?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "+ The dataset size is very large, and it seems to span many chart types and designs.\n+ The dataset quality seems to be high.\n+ The use cases are in general convincing.\n+ The supplemental materials are a very helpful addition for presentation.", "weaknesses": "- I am a bit lost in reading the curation of machine-generated synthetic infographics. I can see a lot of work went into this, but I feel like it is lacking a big picture. Please see my first question. The other parts of the paper all seem pretty easy to follow.\n- Huge improvements on questions based on ChartGalaxy is probably not that unsurprising given the vast majority of infographics in the QA set are based on synthetic charts with fixed templates. Finetuning is expected to help a lot here. Also I believe the QA set is not yet published so we do cannot gauge QA set quality.\n- The code generation metric seems a bit questionable. For example, GPT-4.1-mini ranks higher than o4-mini, o1, o3, and GPT-4o. It is better to have a validation process where humans independently score each chart without seeing the model generating it and establish some sort of inter-rater reliability with scores produced by your metric.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UUyCZDEW0f", "reviewer": "Reviewer_Z9By", "summary": "This paper constructs a large-scale dataset for infographic charts.\nWhile previous infographic datasets were limited in scale, this work builds a massive dataset of 1.7 million synthetic samples, combining web-collected real data with diverse layout templates.\nUsing the constructed dataset, the authors conduct experiments on three tasks: chart understanding, code generation, and example-based infographic chart generation.", "questions": "- 1. In Section 4.2 on the infographic chart generation task, only zero-shot evaluation is conducted. Is it also possible to fine-tune the model using the synthetic data?\n- 2. In the same Section 4.2, why are the evaluation samples drawn only from the synthetic subset? Since the synthetic data itself was generated by combining chart types and variations using D3.js with LLM-based synthesis, wouldn’t using only LLM-generated data for evaluation introduce a potential bias that makes the generation task easier for the model?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- 1. Templates are carefully extracted from real data and utilized for synthetic data generation, resulting in a diverse set of samples.\n- 2. A large number of evaluation experiments are conducted in a thorough and detailed manner.\n- 3. This work provides a large-scale dataset in the infographic domain, where available data has been scarce until now.", "weaknesses": "There are some unclear points regarding the details and procedures of the experiments.\n- 1. In Section 3.3, the paper describes the extraction of layout templates. Could you clarify the format in which these templates are stored? While Figure 3 presents visual examples of the template images, it would be helpful to know whether they also contain information such as bounding boxes or other structural annotations.\n- 2. In Section 3.4, which discusses Element Generation, could you please clarify the procedure for chart generation? Specifically, are the charts produced using predefined D3.js code that is associated with each layout template, or are they dynamically generated through the use of LLMs or other generative models?\n- 3. Does the proposed method described in Section 4.3 generate charts by specifically utilizing the Element Generation/Recommendation and Layout Optimization processes presented in Section 3.4?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M9f059Pzko", "reviewer": "Reviewer_NBfT", "summary": "This manuscript introduces ChartGalaxy, a large-scale dataset designed for infographic chart understanding and generation. The dataset combines 61,833 real infographic charts collected from 18 publicly available sources with 1.7 million synthetic infographic charts generated through an inductive, template-based synthesis pipeline. Each chart is paired with a tabular dataset, enabling multimodal learning. The authors demonstrate ChartGalaxy’s utility through three applications: (1) fine-tuning LVLMs for infographic chart understanding, (2) benchmarking infographic chart code generation, and (3) example-based infographic chart generation evaluated via a user study.", "questions": "Q1: Shouldn’t the original intention and practice of infographic charts, such as adding explanatory text, icons, and descriptive narrative structures as clues, be to make it easier to understand? Is there any empirical evidence that infographic charts are more difficult for VLMs to understand?\n\nQ2: W2.\n\nQ3: W3.\n\nQ4: The setup of the GPT-Image-1 baseline in the example-based infographic chart generation experiment is insufficiently documented. How was GPT-Image-1 prompted? Any layout or structural guidance supplied?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "S1: Comprehensive and large-scale dataset. The dataset includes over 1.76 million infographic charts paired with tabular data, exceeding the scale of previous datasets. This scope enables training LVLMs for realistic infographic scenarios, supporting broad generalization. The inclusion of 75 chart types, 440 chart variations, and 68 layout templates reflects high visual and structural diversity. The dual-source construction (real + synthetic) provides both authenticity and scalability, addressing prior dataset limitations.\n\nS2: Well-defined inductive synthesis pipeline. The human-in-the-loop design for extracting and expanding layout templates demonstrates methodological soundness. The pipeline’s iterative detection and clustering stages ensure diversity. The layout optimization step formalizes the spatial packing problem, providing a principled way to ensure high visual quality. The combination of data-to-chart mapping rules, adaptive sampling for variation balance, and semantically resonant color selection enhances the realism of synthetic charts.\n\nS3: Clear evidence of downstream utility. Fine-tuning LVLMs with ChartGalaxy yields large gains on InfographicVQA and ChartQAPro and even larger gains on the authors’ evaluation set, showing measurable improvement. The code generation benchmark evaluates 17 LVLMs with reproducible metrics (SVG-level and GPT-4o-based judgments), positioning the dataset as an evaluation standard. The example-based generation experiment includes a controlled user study showing statistically significant improvements in fidelity, aesthetics, and creativity.", "weaknesses": "W1: \nThe manuscript would benefit from a clearer definition of what qualifies as an infographic chart and a more concrete explanation of how it differs from plain charts in terms of reasoning challenges. The current description in the Introduction could be made more specific, perhaps with brief examples or a sharper comparison to existing VQA or chart QA tasks to better highlight the unique difficulty of infographic chart understanding.\n\nW2: \nTable 1 shows that models achieve higher accuracy on InfographicVQA than on ChartQAPro. It would be helpful to include a short discussion on potential factors contributing to this gap, such as differences in question design, dataset composition, or task difficulty. Clarifying this would better support the manuscript’s motivation and help readers interpret the reported results.\n\nW3: \nThe synthetic infographic chart generation pipeline is a valuable contribution. To further strengthen the work, it could be beneficial to include a small quantitative or qualitative assessment of the fidelity and usefulness of the generated charts beyond the downstream fine-tuning results. Even a brief ablation on key components, such as layout optimization, would provide additional insight.\n\nW4: \nThe dataset and benchmark are timely and impactful. Adding more analysis or reflections on model behavior, enabled by this dataset, could enhance the contribution for the ICLR audience, which often values methodological insight alongside dataset and system advances.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VxgH0ZcVjc", "reviewer": "Reviewer_d34b", "summary": "This paper introduces ChartGalaxy, a large-scale dataset comprising million-scale synthetic and 61K real infographic charts paired with underlying data tables, aimed at advancing multimodal reasoning and generation in large vision-language models (LVLMs). The dataset is constructed through a two-stage process. The authors demonstrate the dataset's utility via three applications: fine-tuning LVLMs for improved infographic understanding, benchmarking 17 LVLMs on code generation for infographics (with Gemini-1.5-Pro topping), and an example-based generation method that outperforms GPT-4V in user studies on fidelity, aesthetics, and creativity.", "questions": "In section 3.4, you first mention you use gemini-2.0-flash to select templates. However, in the next paragraphs about layout optimization, you mentioned you filter templates based on some criteria. These two parts seem conflicting. Could you elaborate more?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 4, "strengths": "- The paper addresses a clear and important gap on infographic data. The community has largely focused on plain charts, while complex infographics, which are common in media, business, and education, remain a major challenge for LVLMs and no large-scale high-quality data exists.\n\n- The pipeline for generating synthetic data is a major strength. The authors incorporate D3, which offers much more visualization options than conventional approaches relying on matplotlib or seaborn. The use of template-based generation approach is quite clever and neat.\n\n- The authors do not just present a dataset; they thoroughly demonstrate its utility. All three applications are well-conceived and executed through chart understanding, code generation, and chart generation.", "weaknesses": "- The authors did not discuss the performance of the object detection model (InternImage+DINO) used to parse real charts.\n\n\n- While outperforming baselines in applications, direct comparisons to baselines are missing; e.g., no fine-tuning ablation using only prior datasets. The authors can compare relevant training datasets such as [1][2][3] as baselines. These additional experiments would enhance the soundness of the paper.\n\n\n[1] Liu et al., MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning. NAACL 2024\n\n[2] Huang et al., Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding. ACL 2025 Findings \n\n[3] Masry et al., ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild. COLING 2025 Industry Track", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Conditions for acceptance have been satisfied.
Decision: Accept (Poster)