AI帮你写论文：从3000篇顶会论文中提炼创新套路
写论文最难的往往不是做实验，而是想出一个靠谱的idea。这篇论文把过去5年机器学习顶会（ICLR、ICML、NeurIPS）的1947篇论文（包括被拒的）拆解成31个创新子模式，再合并成15个可复用的套路。每个套路都告诉你：什么场景下用、瓶颈在哪、怎么跟别人不一样、容易犯什么错。然后他们做了一个AI工具，你给它一个研究方向，它先搜文献确认你没跑偏，再匹配最合适的套路，生成一个带参考文献和风险提示的提案。盲测显示，用这个工具生成的提案比随便让AI写或自己瞎想要靠谱。它不是你明天就能用的产品，但如果你在写论文或评审论文，这套思路能帮你快速判断一个idea有没有戏。
📄 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.