@inproceedings{hu-etal-2025-nova,
    title = "{NOVA}: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models",
    author = "Hu, Xiang  and
      Fu, Hongyu  and
      Wang, Jinge  and
      Wang, Yifeng  and
      Li, Zhikun  and
      Xu, Renjun  and
      Lu, Yu  and
      Jin, Yaochu  and
      Pan, Lili  and
      Lan, Zhenzhong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1099/",
    pages = "21330--21359",
    ISBN = "979-8-89176-256-5",
    abstract = "Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments demonstrates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. Our code is available at https://github.com/hflyzju/Nova"
}