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SCAFFOLD dataset pairs 157,000 CS paper diagrams with reasoning traces

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Computer science papers lean heavily on diagrams: architecture drawings, system flowcharts, and pipeline schematics that often carry more information than the surrounding text. According to the authors, no public dataset currently pairs this specific kind of figure with captions, context, questions, answers, and step-by-step reasoning, which is exactly what is needed to train a vision-language model to read them properly. To fill that gap, the authors built SCAFFOLD, a large-scale structured dataset built from arXiv computer science papers. Each entry is a tuple of image, caption, context, question-answer pair, and chain-of-thought reasoning trace. The tuples were produced by running layout detection and PDF parsing over the source papers, followed by an AI-assisted step that generated the questions. The dataset ships in three sizes. The largest, SCAFFOLD-157K, spans 3,058 papers with 29,887 figures and 157,387 tuples. A medium version, SCAFFOLD-37K, holds 36,797 tuples, and a small version, SCAFFOLD-12K, holds 12,000 tuples. The authors used the small SCAFFOLD-12K set to run baseline experiments on the vision-language model Qwen2.5-VL-3B-Instruct, though no accuracy or other results from those experiments are reported in the text. The paper does not name its authors or their institutions, does not give a release date or license, and does not compare SCAFFOLD to any other existing dataset beyond stating that no comparable public dataset exists. A GitHub repository is linked in a footnote.

Key facts

  • SCAFFOLD pairs computer science research figures (architecture drawings, flowcharts, pipeline schematics) with captions, context, question-answer pairs, and chain-of-thought reasoning traces.
  • The full SCAFFOLD-157K dataset spans 3,058 papers, 29,887 figures, and 157,387 tuples.
  • Two smaller variants are also released: SCAFFOLD-37K with 36,797 tuples and SCAFFOLD-12K with 12,000 tuples.
  • The dataset was built with layout detection, PDF parsing, and an AI-assisted question-generation step.
  • The small SCAFFOLD-12K set was used for baseline experiments on Qwen2.5-VL-3B-Instruct, though the paper does not report the results of those experiments.

Why it matters

Diagrams in computer science papers, including architecture drawings, flowcharts, and pipeline schematics, often carry more information than the text around them, but according to the authors there was no public dataset that paired this kind of figure with captions, context, questions, answers, and step-by-step reasoning. That gap matters because such data is exactly what is needed to train a vision-language model to actually understand a research diagram rather than treat it as a generic image. SCAFFOLD is built specifically to close that gap, at a scale (157,387 tuples drawn from 3,058 papers) large enough to plausibly move the needle on that training task.

Who it affects

The dataset targets researchers and engineers building or fine-tuning vision-language models, particularly ones meant to parse scientific literature, scholarly search tools, and any system that needs to answer questions about figures rather than only about running text. Qwen2.5-VL-3B-Instruct is named as the model used in the paper's own baseline experiments.

How to use it

SCAFFOLD comes in three sizes so a team can pick the tradeoff between coverage and cost: SCAFFOLD-157K (3,058 papers, 29,887 figures, 157,387 tuples) for full-scale training, SCAFFOLD-37K (36,797 tuples) as a medium option, and SCAFFOLD-12K (12,000 tuples), which the authors themselves used for baseline experiments on Qwen2.5-VL-3B-Instruct. A GitHub repository is linked in a footnote of the paper. The text gives no release date or license, so anyone wanting to use the data will need to check the repository directly for those terms.

How solid is it

The dataset's construction pipeline (layout detection and PDF parsing over arXiv CS papers, followed by AI-assisted question generation) is described at a high level, and the three dataset sizes and their tuple counts are stated precisely. What is missing from the text is any accuracy or performance figure from the baseline experiments on Qwen2.5-VL-3B-Instruct, so there is no reported measure of how well a model actually learns from the data. The paper also does not name its authors or their affiliated institutions.

Risks and caveats

The paper does not compare SCAFFOLD to any other existing dataset, diagram-focused or otherwise, beyond the general claim that no comparable public dataset exists; that claim is not independently verified here. Because the QA pairs and reasoning traces were generated with AI assistance rather than fully by hand, their quality and correctness are not established one way or the other by anything reported in the text. With no baseline results, no license, and no release date given, it is too early to judge how useful or how ready for adoption this dataset is.

“Computer science papers rely heavily on diagrams: architecture drawings, system flowcharts, and pipeline schematics that often carry more information than the text around them.”

— SCAFFOLD paper