SPARK：基于大规模科学文献的骨架引导推理合成
SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature
- Shanghai AI Laboratory（上海人工智能实验室）
- University of Science and Technology of China（中国科学技术大学）
- East China Normal University（华东师范大学）
- Peking University（北京大学）
- Renmin University of China（中国人民大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对开源模型科学推理数据不足的问题，提出基于Sci-Base的SPARK框架构建Spark-234K科学推理数据集，该数据集难度与多样性更优，仅需更少训练样本即可实现更强性能。
AI中文摘要：
开源模型在科学推理方面仍面临挑战，主要原因是缺乏高质量的科学推理数据。现有数据集通常以事实回忆或公式化问题解决为主，对机制理解、证据支撑推理和假设评估的重视有限。为解决这一问题，我们引入SPARK（Scientific Paper Abstracted Reasoning sKeleton，即科学论文抽象推理骨架），这是一个基于Sci-Base的面向论文的合成框架，Sci-Base是涵盖10个科学学科的大规模研究论文语料库。SPARK不直接将论文转换为问答对，而是将论文的主张-证据-推导结构作为推理合成的基本单元。具体而言，SPARK（1）将每篇论文提炼为紧凑的推理骨架，捕捉其核心主张和支撑证据，从而实现自包含的问题生成；（2）从四个科学视角合成推理任务：机制推理、假设证伪、定量推导和边界校准。最后，一致性验证阶段会进一步移除无支撑或矛盾的输出。利用该框架，我们构建了Spark-234K，这是一个科学推理数据集，其难度和多样性显著高于现有资源。实验表明，Spark-234K在性能上始终优于现有科学推理数据集，且使用少得多的训练样本即可实现更强的表现。
英文摘要：
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.
