Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

Priyanka Kargupta, Shuhaib Mehri, Ruozhen Yang, Dilek Hakkani-Tür, Jiawei Han

Keywords: AI4Research, automated scientific discovery, creative reasoning, human-AI collaboration

TL;DR: We introduce Idea-Catalyst, an LLM-driven framework that accelerates scientific breakthroughs by systematically surfacing and prioritizing high-impact interdisciplinary insights during the early, creative stages of research ideation.

Abstract: Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain’s opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem. Overall, Idea-Catalyst provides a structured framework for boundary-spanning research ideation, with implications for both AI-assisted human creativity and automated scientific discovery. We additionally extend this work into an "OpenResearch" platform, which allows users to explore other intermediate, cross-disciplinary ideas and spark insights to improve their ideas throughout their iterative ideation process.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 166