SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature

2026-08-30

AI Summary

为解决高质量科学推理数据缺乏的问题，提出SPARK框架，通过提取论文的论证结构生成多样化推理任务，构建了更具挑战性的科学推理数据集Spark-234K。

Abstract

Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets are often dominated by factual recall or formulaic problem solving, with limited emphasis on mechanism understanding, evidence-grounded reasoning, and hypothesis evaluation. To address this, we introduce SPARK (Scientific Paper Abstracted Reasoning sKeleton), a paper-oriented synthesis framework built on Sci-Base, a large-scale corpus of research papers spanning 10 scientific disciplines. Instead of directly converting papers into question-answer pairs, SPARK treats the claim-evidence-derivation structure of a paper as the fundamental unit of reasoning synthesis. Specifically, SPARK (1) distills each paper into a compact reasoning skeleton capturing its central claims and supporting evidence, enabling self-contained question generation, and (2) synthesizes reasoning tasks from four scientific perspectives: mechanistic reasoning, hypothesis falsification, quantitative derivation, and boundary calibration. A final consistency verification stage further removes unsupported or contradictory outputs. Using this framework, we construct Spark-234K, a scientific reasoning dataset with substantially higher difficulty and diversity than existing resources. Experiments show that Spark-234K consistently outperforms existing scientific reasoning datasets while achieving stronger performance with significantly fewer training samples.

Problem

• scientific reasoning
• high-quality data
• mechanism understanding
• evidence-grounded reasoning
• hypothesis evaluation

Innovation

• reasoning synthesis
• claim-evidence-derivation structure
• mechanistic reasoning
• hypothesis falsification