通过LLM驱动的跨学科灵感激发科学创造力
Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration
AI总结：
Idea-Catalyst通过系统识别跨学科见解，支持人类和大语言模型的创造性推理，提升科学发现的创新性和洞察力。
摘要：
尽管跨学科研究带来了更大的长期影响，但大多数工作仍局限于单一领域的学术孤岛。最近基于AI的科学发现方法在跨学科研究中显示出潜力，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，先前的努力主要集中在自动化科学发现，而不是增强导致科学颠覆的推理过程。我们提出了Idea-Catalyst，一种新的框架，系统地识别跨学科见解以支持人类和大语言模型中的创造性推理。从抽象的研究目标出发，Idea-Catalyst旨在协助头脑风暴阶段，明确避免过早锁定特定解决方案。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)意识领域的机会和未解决的挑战，(c)基于潜在影响的战略探索跨学科想法。具体而言，Idea-Catalyst将抽象目标（例如改进人机协作）分解为核心目标领域研究问题，以指导该领域内进展和开放挑战的分析。这些挑战被重新表述为领域无关的概念问题，使能够从外部学科（如心理学、社会学）检索解决类似问题。通过将这些领域的见解综合并重新置于目标领域，Idea-Catalyst根据跨学科潜力对来源领域进行排序。实验表明，这种有针对性的整合提高了平均新颖性21%和洞察力16%，同时仍基于原始研究问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
