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、更长远的影响，但大多数研究工作仍局限于单一学科领域。近期基于人工智能的科学研究方法为跨学科研究带来了希望，但许多方法侧重于快速设计实验方案，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。因此，现有研究主要优先考虑自动化科学发现，而非增强科学突破背后的推理过程。我们提出"创意催化"框架，这一新型系统能够识别跨学科见解，为人类和大型语言模型的创造性推理提供支持。该框架从抽象研究目标出发，专门用于辅助头脑风暴阶段，明确避免过早锚定具体解决方案。它体现了跨学科推理的关键元认知特征：(a)界定与评估研究目标；(b)把握领域机遇与未解挑战的认知意识；(c)基于影响潜力的跨学科理念战略探索。具体而言，该框架将抽象目标（如改进人机协作）分解为核心领域的研究问题，藉此分析该领域的进展与开放挑战。这些挑战被重新表述为领域无涉的概念性问题，从而能够从其他学科（如心理学、社会学）检索处理类似问题的方案。通过将这些领域的见解综合并重新语境化至目标领域，该框架可依据跨学科潜力对源领域进行排序。实证研究表明，这种定向整合在保持原始研究问题根基的同时，能将平均新颖度提升21%，洞察力提高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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