通过多智能体对话提升科研创意的多样性与可行性。
Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
- 设计不同角色、数量和深度的多智能体对话框架。
- 增加智能体数量与角色差异可显著提升创意多样性。
- 批判者侧多样性提升使最终方案更可行，适合科研辅助场景。
大语言模型（LLMs）在支持科研创意生成等创造性任务中日益普及。尽管已有研究证明，结构化多智能体对话能提升创意的新颖性与可行性，但最优交互设计仍不明确。本文对用于科学创意生成的多智能体对话设计进行全面分析，比较了代理角色、数量及对话深度的不同配置，探究其对创意新颖性与可行性的影响。实验设置包括一个生成者与一个批评者之间的迭代互动，实现创意优化。结果表明：扩大智能体群体、加深交互深度、拓宽角色异质性均能增强创意多样性；尤其在创意-批评-修订循环中，提升批评方的多样性可进一步提高最终提案的可行性。研究为构建高效科研创意生成的多智能体系统提供了实用指导。代码已开源：https://github.com/g6000/MultiAgent-Research-Ideator。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to understand how these factors influence the novelty and feasibility of generated ideas. Our experimental setup includes settings where one agent generates ideas and another critiques them, enabling iterative improvement. Our results show that enlarging the agent cohort, deepening the interaction depth, and broadening agent persona heterogeneity each enrich the diversity of generated ideas. Moreover, specifically increasing critic-side diversity within the ideation-critique-revision loop further boosts the feasibility of the final proposals. Our findings offer practical guidelines for building effective multi-agent LLM systems for scientific ideation. Our code is available at https://github.com/g6000/MultiAgent-Research-Ideator.
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