ResearchStudio-Idea: 基于机器学习会议成果的实证研究构思技能套件

摘要

大型语言模型使得研究构思越来越易于实现，但有效构思的形成不仅需要生成候选方向。研究者必须将问题置于当前文献中，识别关键瓶颈，与现有解决方案区分开来，并在投入实施前评估风险。我们提出ResearchStudio-Idea，作为研究构思"第一英里"的可复用技能套件。该套件包括：Paper-Search——独立的跨源文献检索技能；Scoop-Check——独立的新颖性声明在先技术冲突检测技能；以及IdeaSpark——端到端技能，它将证据支撑、模式引导生成、冲突检索、审计和想法卡片渲染整合为一个工作流。IdeaSpark基于从2021年至2025年期间从ICLR、ICML和NeurIPS收集的1,947篇机器学习会议论文构建，其中包括Oral论文、单独追踪的高被引子集以及被拒稿的论文。对这些结果的分析揭示了31种反复出现的构思子模式，并整合为15种可复用的构思模式。每种模式通过结构化卡片实现，包含研究背景、瓶颈类型、差异化策略、支持性先例以及常见失败模式。给定研究问题和证据包，IdeaSpark评估证据完备性，重构相关研究背景，识别未解决的瓶颈，选择相关模式，实例化一个候选方向，检索可能冲突的已有工作，并进行结果导向的审计。这一工作流将可复用的构思模式转化为可追溯的研究提案。盲审自动评估表明，IdeaSpark在保持竞争性新颖性的同时，持续产生比无技能和通用技能基线更强的研究提案。

English

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.