研究工作室-创意：基于机器学习会议成果的有证据支持的研究创意技能套件
ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
AI总结：
研究问题是提升研究创意，核心方法是构建包含多种技能的套件，主要贡献是揭示31个反复出现的创意子模式并整合为15个可复用模式，能将其转化为可追溯研究提案，经评估效果优于基线。
摘要：
大语言模型使研究创意更易获取，但有效创意发展需更多。我们提出ResearchStudio-Idea作为研究创意第一阶段的可复用技能套件，含多种技能。它由会议论文语料库构建，揭示多种创意模式，能将模式转化为可追溯研究提案，评估显示其优于基线。
英文摘要：
Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must ground a problem in current literature, identify meaningful bottlenecks, differentiate from existing solutions, and evaluate risks before committing to implementation. We present ResearchStudio-Idea as a reusable skill suite for this first mile of research ideation. The suite includes Paper-Search, a standalone multi-source literature search skill; Scoop-Check, a standalone prior-art collision checker for novelty claims; and IdeaSpark, the end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card rendering into one workflow. IdeaSpark is constructed from a corpus of 1,947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025, including Oral papers, a separately tracked high-citation subset, and rejected submissions. Analysis of these outcomes reveals 31 recurring ideation sub-patterns, consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research contexts, bottleneck types, differentiation strategies, supporting precedents, and common failure modes. Given a research problem and an evidence bundle, IdeaSpark evaluates evidence readiness, reconstructs the surrounding research context, identifies unresolved bottlenecks, selects relevant patterns, instantiates one candidate direction, retrieves potentially conflicting prior work, and performs outcome-informed auditing. This workflow transforms reusable ideation patterns into traceable research proposals. Blind automated-judge evaluations show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
