首个解析人工智能突破性创新思维模式的数据集，揭示科研背后的15种推理策略。
Sci-Reasoning: A Dataset Decoding AI Innovation Patterns
- 通过大模型辅助+人工验证，追踪顶会论文的前序研究与逻辑关联
- 发现3种主导策略占52.7%，其中缺口重构占比最高达24.2%
- 提供可量化的科研思维路径，助力训练下一代智能研究助手
尽管人工智能创新加速发展，但其背后的研究者如何识别研究空白、整合已有成果并生成新见解的智力过程仍不清晰。缺乏结构化科学推理数据，限制了对科研进展的系统分析和智能研究代理的开发。我们提出Sci-Reasoning，首个捕捉高质量人工智能研究智力合成过程的数据集。基于社区验证的质量信号和大语言模型加速、人工验证的流程，我们追溯了NeurIPS、ICML、ICLR（2023–2025）中Oral与Spotlight论文与其关键前序工作的关联，以结构化格式呈现具体推理链路。分析揭示出15种不同思考模式，其中三种主导策略共占52.7%：缺口驱动重构（24.2%）、跨领域融合（18.0%）和表示转换（10.5%）。最有效的创新组合为：缺口驱动重构 + 表示转换、跨领域融合 + 表示转换、缺口驱动重构 + 跨领域融合。该数据集支持对科学进步的量化研究，并为下一代人工智能研究代理提供结构化推理轨迹。
原文摘要 · Abstract (English)
While AI innovation accelerates rapidly, the intellectual process behind breakthroughs -- how researchers identify gaps, synthesize prior work, and generate insights -- remains poorly understood. The lack of structured data on scientific reasoning hinders systematic analysis and development of AI research agents. We introduce Sci-Reasoning, the first dataset capturing the intellectual synthesis behind high-quality AI research. Using community-validated quality signals and an LLM-accelerated, human-verified pipeline, we trace Oral and Spotlight papers across NeurIPS, ICML, and ICLR (2023-2025) to its key predecessors, articulating specific reasoning links in a structured format. Our analysis identifies 15 distinct thinking patterns, with three dominant strategies accounting for 52.7%: Gap-Driven Reframing (24.2%), Cross-Domain Synthesis (18.0%), and Representation Shift (10.5%). The most powerful innovation recipes combine multiple patterns: Gap-Driven Reframing + Representation Shift, Cross-Domain Synthesis + Representation Shift, and Gap-Driven Reframing + Cross-Domain Synthesis. This dataset enables quantitative studies of scientific progress and provides structured reasoning trajectories for training the next generation AI research agents.
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