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Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

Priyanka Kargupta, Shuhaib Mehri, Dilek Hakkani-Tur, Jiawei Han

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.

Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

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.
Paper Structure (38 sections, 7 figures, 7 tables)

This paper contains 38 sections, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Interdisciplinary Research and Scientific Creativity
  4. Automated Scientific Discovery and Research Ideation
  5. Human-Centered Scientific Knowledge Discovery
  6. Methodology
  7. Preliminaries
  8. Problem Formulation
  9. Scientific Literature Snippet Retrieval
  10. Metacognition-Driven Ideation
  11. Critical Reasoning over the Target Domain
  12. Creative Reasoning Across Source Domains
  13. Cross-Domain Retrieval
  14. Target--Source Interdisciplinary Integration
  15. Idea Fragments
  16. ...and 23 more sections

Figures (7)

  • Figure 1: Interdisciplinary process of formalizing RL sutton1998reinforcement.
  • Figure 2: Idea-Catalyst is a metacognition-driven framework that (a) analyzes target-domain progress, (b) identifies unresolved challenges, (c) explores source domains for analogous insights, and (d) integrates them into interdisciplinary idea fragments. The figure is illustrated with a real case study (summarized).
  • Figure 3: Source-domain distributions (log-scale) for each method's top three ideas.
  • Figure 4: Target-source flow of interdisciplinary inspiration.
  • Figure 5: Screenshot of evaluation interface (reviewing questions/challenges).
  • ...and 2 more figures

Theorems & Definitions (4)

  • Definition 3.1: Target Domain
  • Definition 3.2: Source Domain
  • Definition 3.3: Interdisciplinary Insight
  • Definition 3.4: Interdisciplinary Potential