arXiv AI
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis
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ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.
arXiv AI
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From Charts to Code: A Hierarchical Benchmark for Multimodal Models
arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...
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Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?
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arXiv AI
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SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces
SCAFFOLD is a large-scale structured dataset of computer science research figures, each paired with captions, context, questions, answers, and chain-of-thought reasoning traces. It contains 157,387 figure–question pairs from 3,058 arXiv papers, with subsets of 36,797 and 12,000 pairs for medium and small-scale use. The dataset was created using layout detection, PDF parsing, and AI-assisted question generation, and was used to benchmark a vision‑language model (Qwen2.5‑VL‑3B‑Instruct).
arXiv Machine Learning
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Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing
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Hugging Face Trending Papers
Sep 8
Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing
The paper introduces LayerWiseBench, a benchmark that evaluates visual language models on layer-wise chart understanding and editing. It focuses on three core concepts—layer attribution, layer binding, and visibility ordering—by pairing rendered charts with spatially aligned per-layer RGBA assets and functional role labels. The benchmark includes 2,800 charts, 7,329 understanding questions, and 53,791 editing variants, revealing that models excel at attribution and binding but struggle with visibility ordering, especially when editing overlapping components.