IdeaAnchor：训练大模型从文献中生成研究新想法
IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
新加坡国立和 collaborators 出的一篇论文，教模型从一堆论文里提炼新研究点子，训练加检索两条路分开验证，做科研 AI 的可以看看思路。
一篇 arXiv 论文提出 IdeaAnchor，用结构化规格作为监督信号来训练大模型做文献驱动的研究构思。每个训练实例标注输入论文的功能角色、相互关系和目标综合标准，实例从已发表论文中挖掘而来。训练流程结合示范学习、自我蒸馏和强化学习，推理阶段再加入检索。实验显示构思质量持续提升，分析发现锚点训练负责创意综合、检索负责细节展开，两者结合效果最佳。
IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
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.
