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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01424 |
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| _version_ | 1866909916148531200 |
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| author | Zhou, Zekun Feng, Xiaocheng Huang, Lei Feng, Xiachong Song, Ziyun Chen, Ruihan Zhao, Liang Ma, Weitao Gu, Yuxuan Wang, Baoxin Wu, Dayong Hu, Guoping Liu, Ting Qin, Bing |
| author_facet | Zhou, Zekun Feng, Xiaocheng Huang, Lei Feng, Xiachong Song, Ziyun Chen, Ruihan Zhao, Liang Ma, Weitao Gu, Yuxuan Wang, Baoxin Wu, Dayong Hu, Guoping Liu, Ting Qin, Bing |
| contents | Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01424 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems Zhou, Zekun Feng, Xiaocheng Huang, Lei Feng, Xiachong Song, Ziyun Chen, Ruihan Zhao, Liang Ma, Weitao Gu, Yuxuan Wang, Baoxin Wu, Dayong Hu, Guoping Liu, Ting Qin, Bing Artificial Intelligence Computation and Language Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research. |
| title | From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.01424 |