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New SCAFFOLD dataset trains AI on CS research figures and diagrams

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SCAFFOLD, a novel dataset designed to train vision-language models on understanding complex diagrams found in computer science research papers. This dataset includes images, captions, contextual information, questions, answers, and step-by-step reasoning traces derived from arXiv papers. SCAFFOLD is available in multiple sizes, with the largest version, SCAFFOLD-157K, containing nearly 157,000 question-answer pairs from over 3,000 papers. Initial experiments utilized the smaller SCAFFOLD-12K dataset with the Qwen2.5-VL-3B-Instruct model. AI

IMPACT Enables development of AI models capable of understanding complex visual information in scientific literature.

RANK_REASON The item is a research paper describing a new dataset for AI model training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SCAFFOLD dataset trains AI on CS research figures and diagrams

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The item is a research paper describing a new dataset for AI model training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ranjit Raut, Aarav Subedi, Sagun Rai, Sudan Jha ·

    SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces

    arXiv:2609.00018v1 Announce Type: new Abstract: Computer science papers rely heavily on diagrams: architecture drawings, system flowcharts, and pipeline schematics that often carry more information than the text around them. There is currently no public dataset that pairs this sp…

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