Scideator: Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation

Abstract

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, where users select facets from retrieved papers and the system generates ideas by finding analogies across these facets 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%.