Sci-Reasoning: A Dataset Decoding AI Innovation Patterns

2026-01-08

arXiv.org

AI Summary

This work addresses the lack of structured characterization of scientific reasoning processes in current AI research, which hinders both the understanding of scientific innovation mechanisms and the development of AI research agents. To bridge this gap, we introduce Sci-Reasoning, the first dataset capturing AI research reasoning trajectories across NeurIPS, ICML, and ICLR (2023–2025), integrating large language model acceleration, human verification, and community quality signals to systematically identify and formalize 15 distinct research thinking patterns. Our analysis reveals three dominant innovation strategies—Gap-Driven Reframing, Cross-Domain Synthesis, and Representation Shift—whose combinations account for 52.7% of high-quality papers, offering a novel paradigm for quantifying scientific progress and training AI research agents.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• scientific reasoning
• AI innovation
• research synthesis
• intellectual process
• structured data

Innovation

• scientific reasoning
• AI innovation patterns
• structured reasoning dataset
• research synthesis
• LLM-accelerated pipeline