AI帮你写论文：从会议论文中提炼研究套路
写论文最难的不是动笔，而是想出一个靠谱的研究方向。现在有研究者从近2000篇顶会论文中提炼出15种常见的研究套路，并做成了一套AI工具。你只要输入一个研究问题，它就会自动搜索相关文献、判断你的想法是否新颖、然后套用合适的套路生成完整的研究提案。测试显示，用这套工具生成的提案质量明显优于随机生成或通用AI生成。虽然它不能直接帮你写论文，但能帮你少走弯路——尤其是刚入门的研究者，可以快速了解领域内哪些方向已经被做烂了，哪些还有机会。
📄 原文摘要(英文)
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.