Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.12226v1</id>\n    <title>Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration</title>\n    <updated>2026-03-12T17:48:34Z</updated>\n    <link href='https://arxiv.org/abs/2603.12226v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.12226v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <published>2026-03-12T17:48:34Z</published>\n    <arxiv:comment>Code and dataset provided at https://github.com/pkargupta/idea_catalyst</arxiv:comment>\n    <arxiv:primary_category term='cs.CL'/>\n    <author>\n      <name>Priyanka Kargupta</name>\n    </author>\n    <author>\n      <name>Shuhaib Mehri</name>\n    </author>\n    <author>\n      <name>Dilek Hakkani-Tur</name>\n    </author>\n    <author>\n      <name>Jiawei Han</name>\n    </author>\n  </entry>"
}