The scientific ideation process often involves blending facets of existing papers to create new ideas. We contribute Scideator, the first human-LLM system for facet-based scientific ideation. Starting from user-provided papers, Scideator extracts key facets -- purposes, mechanisms, and evaluations -- from these and related papers, allowing users to interactively recombine facets to synthesize 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 ranging from the same topic to entirely different areas to provide a spectrum of directions; and (3) facet-based novelty verification via the Idea Novelty Checker module, a retrieve-then-rerank pipeline that helps users to evaluate 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. Ablations further show that the facets benefit the novelty checker: facet-based retrieve-then-rerank surfaces more relevant papers than standard retrieval and re-ranking, and a facet-grounded novelty classifier outperforms classifiers that reason over unstructured ideas and papers.

翻译：科学构思过程通常涉及组合现有论文的多个方面以产生新想法。我们提出了Scideator，这是首个面向基于方面的科学构思的人机大语言模型系统。基于用户提供的论文，Scideator从这些论文及相关文献中提取关键方面——目的、机制和评估——使用户能够通过交互式重组方面来综合新想法。Scideator的设计基于三个选择：（1）人在回路中的方面重组：用户从检索到的论文中选择方面，系统通过基于方面的想法生成器模块跨论文寻找类比，从而生成想法；（2）通过类比论文方面查找器模块实现距离可控的检索：该模块能呈现从同主题到完全不同领域的论文，提供多样化的研究方向谱系；（3）通过想法新颖性检测器模块实现基于方面的新颖性验证：这是一个先检索后重排序的流水线，帮助用户利用方面评估想法原创性。在以计算机科学研究者为对象的用户研究中，与使用相同主干大语言模型但未集成基于方面模块的基准系统相比，Scideator在创意探索与表达力方面提供了显著更强的创造力支持。消融实验进一步表明，方面设计能提升新颖性检测器性能：基于方面的检索-重排流程比标准检索与重排能召回更多相关论文，且基于方面的新颖性分类器在处理非结构化想法和论文时的表现优于同类系统。