The scientific ideation process often involves blending salient aspects of existing papers to create new ideas - a framework known as facet-based ideation. We contribute Scideator, the first human-LLM system for facet-based scientific ideation. Starting from a user-provided set of scientific papers, Scideator extracts key facets -- purposes, mechanisms, and evaluations -- from these and related papers, allowing users to explore the idea space by interactively recombining facets to synthesize inventive ideas. Scideator is driven by three design choices: (1) human-in-the-loop facet recombination, in which users select facets from retrieved papers and the system generates ideas by finding analogies across them via the Faceted Idea Generator module; (2) distance-controlled retrieval via the Analogous Paper Facet Finder module, which surfaces papers from the same topic to entirely different subareas to provide a spectrum of creative directions; and (3) facet-based novelty verification via the Idea Novelty Checker module, a retrieve-then-rerank pipeline that evaluates idea originality using facets. In a user study with computer science researchers, Scideator provided significantly more creativity support than a baseline using the same backbone LLM without our facet-based modules, particularly in idea exploration and expressiveness. Participants' favorite ideas more often included facets selected by themselves rather than the LLM, and participants used fewer free-text instructions with Scideator, indicating a preference for facet-level steering over prompting. Finally, re-ranking papers by facet matching rather than general relevance improved novelty classification accuracy from 13.79% to 89.66%.

翻译：科学构思过程通常涉及融合现有论文的显著方面以产生新思想——这一框架被称为基于面的构思。我们提出了Scideator，首个用于基于面科学构思的人机协同系统。从用户提供的一组科学论文出发，Scideator从中提取关键面（目的、机制和评估），并允许用户通过交互式重组这些面来合成创新思想，从而探索创意空间。Scideator由三个设计决策驱动：（1）人在回路的面重组机制，用户从检索到的论文中选择面，系统通过面化创意生成器模块发现跨面类比来生成创意；（2）通过类比论文面发现模块实现距离可控检索，该模块从相同主题到完全不同的子领域检索论文，提供多层次的创意方向；（3）通过创意新颖性检查器模块进行基于面的新颖性验证，这是一个检索-重排序流程，利用面对创意的原创性进行评估。在针对计算机科学研究者的用户研究中，与使用相同骨干大语言模型但未配备我们基于面模块的基线系统相比，Scideator在创意探索和表达性方面提供了显著更强的创造力支持。参与者更青睐包含自主选择面而非大语言模型选择面的创意，且使用Scideator时所需的自由文本指令更少，这表明用户更倾向于面层级的引导而非传统提示方式。最后，通过面匹配而非通用相关性对论文进行重排序，将新颖性分类准确率从13.79%提升至89.66%。