Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

2026-03-12

AI Summary

Current research is often constrained within disciplinary silos, lacking effective mechanisms to foster creative interdisciplinary thinking. This work proposes the Idea-Catalyst framework, which introduces a metacognitive mechanism during the ideation phase to guide researchers in decomposing abstract goals into core questions, translating them into domain-agnostic concepts, and systematically exploring inspirational solutions from external disciplines through large language model–driven semantic decomposition, cross-domain analogical retrieval, and conceptual recombination. The approach deliberately avoids premature commitment to specific solutions, emphasizing goal clarification and strategic cross-domain exploration. Empirical evaluation demonstrates that, compared to baseline methods, the proposed framework increases the novelty of research ideas by 21% and their insightfulness by 16%, while maintaining strong alignment with the original problem.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Metareasoning and Metaheuristics
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• interdisciplinary research
• scientific creativity
• creative reasoning
• scientific discovery
• large language models

Innovation

• interdisciplinary reasoning
• large language models
• scientific creativity
• metacognitive framework
• idea recontextualization