Sci-Reasoning: A Dataset Decoding AI Innovation Patterns
- URL: http://arxiv.org/abs/2601.04577v1
- Date: Thu, 08 Jan 2026 04:12:47 GMT
- Title: Sci-Reasoning: A Dataset Decoding AI Innovation Patterns
- Authors: Jiachen Liu, Maestro Harmon, Zechen Zhang,
- Abstract summary: Sci-Reasoning is the first dataset capturing the intellectual synthesis behind high-quality AI research.<n>Our analysis identifies 15 distinct thinking patterns, with three dominant strategies accounting for 52.7%.<n>This dataset enables quantitative studies of scientific progress and provides structured reasoning trajectories for training the next generation AI research agents.
- Score: 14.720475159371361
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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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