超越头脑风暴：什么驱动高质量科学想法？来自多智能体协作的启示
Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
AI总结：
本文提出合作式多智能体框架，系统比较不同配置，发现多智能体讨论显著优于单独构思，认知多样性驱动质量，但专业知识是前提，领导者促进整合，为协作式AI构思系统设计提供见解。
摘要：
尽管AI智能体在科学构思方面展现出潜力，但现有的大多数框架依赖于单智能体的改进，由于知识广度和视角的局限，限制了创造力。受现实世界研究动态的启发，本文探讨了结构化的多智能体讨论是否能超越单独构思。我们提出了一个用于生成研究提案的合作式多智能体框架，并系统比较了不同配置，包括小组规模、有领导者与无领导者的结构，以及跨学科性和资历不同的团队组成。为评估想法质量，我们采用了一个综合协议，结合基于智能体的评分和人工评审，涵盖新颖性、战略愿景和整合深度等维度。我们的结果表明，多智能体讨论显著优于单独基线。指定的领导者作为催化剂，将讨论转化为更整合且更具远见的提案。值得注意的是，我们发现认知多样性是质量的主要驱动力，但专业知识是不可或缺的前提，因为缺乏资深知识基础的团队甚至无法超越一个能力相当的单一智能体。这些发现为设计协作式AI构思系统提供了可操作的见解，并揭示了团队结构如何影响创造性成果。
英文摘要：
While AI agents show potential in scientific ideation, most existing frameworks rely on single-agent refinement, limiting creativity due to bounded knowledge and perspective. Inspired by real-world research dynamics, this paper investigates whether structured multi-agent discussions can surpass solitary ideation. We propose a cooperative multi-agent framework for generating research proposals and systematically compare configurations including group size, leaderled versus leaderless structures, and team compositions varying in interdisciplinarity and seniority. To assess idea quality, we employ a comprehensive protocol with agent-based scoring and human review across dimensions such as novelty, strategic vision, and integration depth. Our results show that multi-agent discussions substantially outperform solitary baselines. A designated leader acts as a catalyst, transforming discussion into more integrated and visionary proposals. Notably, we find that cognitive diversity is a primary driver of quality, yet expertise is a non-negotiable prerequisite, as teams lacking a foundation of senior knowledge fail to surpass even a single competent agent. These findings offer actionable insights for designing collaborative AI ideation systems and shed light on how team structure influences creative outcomes.
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。
