@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://aclanthology.org/2025.findings-acl.1099/",
doi = "10.18653/v1/2025.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"
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<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</abstract>
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%0 Conference Proceedings
%T NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models
%A Hu, Xiang
%A Fu, Hongyu
%A Wang, Jinge
%A Wang, Yifeng
%A Li, Zhikun
%A Xu, Renjun
%A Lu, Yu
%A Jin, Yaochu
%A Pan, Lili
%A Lan, Zhenzhong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F hu-etal-2025-nova
%X 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
%R 10.18653/v1/2025.findings-acl.1099
%U https://aclanthology.org/2025.findings-acl.1099/
%U https://doi.org/10.18653/v1/2025.findings-acl.1099
%P 21330-21359
Markdown (Informal)
[NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models](https://aclanthology.org/2025.findings-acl.1099/) (Hu et al., Findings 2025)
ACL
- Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, and Zhenzhong Lan. 2025. NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21330–21359, Vienna, Austria. Association for Computational Linguistics.