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.

翻译：尽管跨学科研究能带来更广泛且更长远的影响，但大多数研究工作仍局限于单一领域的学术孤岛之中。近期基于人工智能的科学发现方法在跨学科研究方面展现出潜力，但许多方法优先考虑快速设计实验和解决方案，绕过了驱动创造性跨学科突破的探索性、协作性推理过程。因此，先前的工作主要侧重于自动化科学发现，而非增强构成科学颠覆基础的推理过程。我们提出了Idea-Catalyst，这是一个新颖的框架，旨在系统性地识别跨学科见解，以支持人类和大语言模型的创造性推理。从一个抽象的研究目标出发，Idea-Catalyst被设计用于辅助头脑风暴阶段，明确避免过早地锚定于特定解决方案。该框架体现了跨学科推理的关键元认知特征：(a) 定义和评估研究目标，(b) 对领域内机遇与未解挑战的认知，以及 (c) 基于潜在影响对跨学科想法进行战略性探索。具体而言，Idea-Catalyst将一个抽象目标（例如，改进人机协作）分解为核心目标领域的研究问题，这些问题指导对该领域内进展和开放挑战的分析。这些挑战被重新表述为与领域无关的概念性问题，从而能够从处理类似问题的外部学科（例如心理学、社会学）中检索信息。通过综合这些领域的见解并将其重新情境化到目标领域，Idea-Catalyst根据其跨学科潜力对源领域进行排序。实证结果表明，这种有针对性的整合将平均新颖性提高了21%，洞察力提高了16%，同时仍立足于原始研究问题。