Scientific progress depends on the continual generation of innovative re-search ideas. However, the rapid growth of scientific literature has greatly increased the cost of knowledge filtering, making it harder for researchers to identify novel directions. Although existing large language model (LLM)-based methods show promise in research idea generation, the ideas they produce are often repetitive and lack depth. To address this issue, this study proposes a multi-agent iterative planning search strategy inspired by com-binatorial innovation theory. The framework combines iterative knowledge search with an LLM-based multi-agent system to generate, evaluate, and re-fine research ideas through repeated interaction, with the goal of improving idea diversity and novelty. Experiments in the natural language processing domain show that the proposed method outperforms state-of-the-art base-lines in both diversity and novelty. Further comparison with ideas derived from top-tier machine learning conference papers indicates that the quality of the generated ideas falls between that of accepted and rejected papers. These results suggest that the proposed framework is a promising approach for supporting high-quality research idea generation. The source code and dataset used in this paper are publicly available on Github repository: https://github.com/ChenShuai00/MAGenIdeas. The demo is available at https://huggingface.co/spaces/cshuai20/MAGenIdeas.

翻译：科学进步依赖于创新研究思路的持续生成。然而，科学文献的快速增长极大地增加了知识筛选的成本，使研究者更难以识别新颖的研究方向。尽管现有的基于大语言模型（LLM）的方法在研究思路生成方面显示出潜力，但其生成的思路往往重复性强且缺乏深度。为解决这一问题，本研究受组合创新理论启发，提出了一种多智能体迭代规划搜索策略。该框架将迭代知识搜索与基于LLM的多智能体系统相结合，通过反复交互来生成、评估和优化研究思路，旨在提升思路的多样性和新颖性。在自然语言处理领域的实验表明，所提方法在多样性和新颖性方面均优于现有最先进的基线方法。进一步与顶级机器学习会议论文中的思路对比发现，生成思路的质量介于录用论文与拒稿论文之间。这些结果表明，所提框架是支持高质量研究思路生成的一种有前途的方法。本文使用的源代码和数据集已公开于Github仓库：https://github.com/ChenShuai00/MAGenIdeas，演示地址为：https://huggingface.co/spaces/cshuai20/MAGenIdeas。