用文献反馈迭代优化研究想法，让构思更深入
IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback
- 将研究想法拆解为问题、方案等模块，在画布上逐步迭代
- 实验显示用户探索的想法更多，且细节更丰富
- 适合从初稿到修改各阶段的研究者使用
研究构思需要广泛探索与深度打磨，均需深入文献。现有工具多聚焦想法生成，缺乏对迭代细化与评估的支持。为此，我们提出IdeaSynth，一个基于大模型的科研构思系统，通过文献驱动反馈，帮助研究者明确研究问题、解决方案、评估方式与贡献点。系统将这些构思要素表示为画布上的节点，支持创建变体、组合和反复优化。实验室研究（N=20）显示，使用IdeaSynth的参与者比强基线模型多探索更多备选想法，并更详细地扩展初始构思。部署研究（N=7）表明，用户在真实研究项目中成功应用于不同构思阶段，从初步构想到成熟论文的框架修订，证明其可融入研究工作流。
原文摘要 · Abstract (English)
Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on idea broad generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and composing them. Our lab study (N=20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (N=7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher's workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。