Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

Idea-Catalyst tackles a real problem in AI-for-science: most tools race to generate experiments and skip the harder, slower work of exploring an idea before committing to it. Kargupta, Mehri, Hakkani-Tur, and Han's framework instead breaks a research goal into its open questions, translates those into domain-agnostic problems, and searches other fields for analogous solutions worth importing. In testing, it improved novelty by 21% and insightfulness by 16% while staying anchored to the original question — a rare case of an AI brainstorming tool with numbers to back the brainstorming claim.