Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination

The scientific ideation process often involves blending salient aspects of existing papers to create new ideas -- a framework known as facet-based ideation. To see how large language models (LLMs) might assist in this process, we contribute Scideator, the first human-LLM interface 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 also helps users gauge idea originality by searching the literature for overlaps, assessing idea novelty based on an explicit facet-based definition. To support these tasks, Scideator introduces three LLM-powered retrieval-augmented generation (RAG) modules: Analogous Paper Facet Finder, Faceted Idea Generator, and Idea Novelty Checker. In a within-subjects user study (N=22) with computer-science researchers comparing Scideator to a strong baseline, our tool provided significantly more creativity support, particularly with respect to exploration and expressiveness.