发表机构
Yale University; University of Illinois Urbana-Champaign; Tata Consultancy Services（耶鲁大学; 伊利诺伊大学厄巴纳-香槟分校; 塔塔咨询服务公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
IdeaAnchor通过结构化规范作为监督信号，训练大语言模型从文献中生成研究思路，结合锚点训练与检索增强，显著提升思路质量。
AI 中文摘要
科学研究通常始于综合一组相关论文中的想法，以识别研究空白并制定新方向。然而，训练语言模型执行这种基于文献的思路构思仍然具有挑战性，因为现有的基于提示或反馈的方法缺乏关于如何综合论文的结构化监督。我们引入了IdeaAnchor，一种使用结构化规范作为特权信号来训练大语言模型进行研究思路构思的范式。每个IdeaAnchor实例编码了每篇输入论文应如何被综合成一个成功的思路，包括它们的功能角色、关系和目标综合标准。我们通过从已发表论文中挖掘实例来构建这一范式，捕捉真实思路如何从先前文献中产生。然后我们通过演示、自我蒸馏和强化学习来训练模型，并在推理时通过检索进一步增强生成。实验显示思路质量的一致提升。我们的分析揭示了一种功能分解：基于锚点的训练增强了创造性综合，检索增强了细节阐述，而将两者结合则产生最佳性能。
英文摘要
Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to perform this form of literature-grounded ideation remains challenging, as existing approaches based on prompting or feedback lack structured supervision for how papers should be synthesized. We introduce IdeaAnchor, a paradigm for training LLMs to perform research ideation using structured specifications as privileged signals. Each IdeaAnchor instance encodes how each input paper should be synthesized into a successful idea, including their functional roles, relationships, and target synthesis criteria. We build this paradigm by mining instances from published papers, capturing how real ideas emerge from prior literature. We then train models via demonstration, self-distillation, and reinforcement learning, and further enhance generation with retrieval at inference time. Experiments show consistent improvements in ideation quality. Our analysis reveals a functional decomposition: anchor-based training strengthens creative synthesis, retrieval enhances detail elaboration, and combining both yields the best performance.
