激发科学创造力：基于大语言模型的跨学科灵感启迪

摘要

尽管跨学科研究能带来更广泛和更长远的影响，但大多数学术工作仍局限于单一学科领域。近期基于人工智能的科学发现方法虽展现出跨学科研究的潜力，但多数方案侧重于快速设计实验与解决方案，绕过了驱动创造性跨学科突破所需的探索性协作推理过程。这导致现有研究主要聚焦于自动化科学发现，而非增强科学突破背后的推理能力。我们提出"创意催化剂"新框架，通过系统识别跨学科洞见来支持人类与大型语言模型的创造性推理。该框架从抽象研究目标出发，专门辅助头脑风暴阶段，明确避免过早锚定具体解决方案。其体现了跨学科推理的三个元认知特征：（a）界定与评估研究目标；（b）洞察学科领域的机遇与未解难题；（c）基于潜在影响力对跨学科思想进行策略性探索。具体而言，该框架将抽象目标（如"提升人机协作效能"）分解为核心研究问题，以此指导目标领域的进展分析与挑战定位；继而将这些挑战转化为领域无关的概念性问题，从而能从外部学科（如心理学、社会学）检索同类问题的解决方案。通过将跨学科洞见重新语境化并整合至目标领域，框架可依跨学科潜力对源领域进行排序。实证表明，这种定向整合策略在保持研究问题锚定性的同时，能将创新性平均提升21%，启发性提升16%。

English

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.

激发科学创造力：基于大语言模型的跨学科灵感启迪

Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

摘要

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