The scientific ideation process often involves blending salient aspects of existing papers to create new ideas, and facet-based ideation is an established framework for idea generation. To see how large language models (LLMs) might assist in this process, we contribute a novel mixed-initiative ideation tool called Scideator. 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 and providing explanations. 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, which participants considered the most important factor for idea generation.
View on arXiv@article{radensky2025_2409.14634,
title={ Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination },
author={ Marissa Radensky and Simra Shahid and Raymond Fok and Pao Siangliulue and Tom Hope and Daniel S. Weld },
journal={arXiv preprint arXiv:2409.14634},
year={ 2025 }
}