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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/2504.18765 |
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| _version_ | 1866908456542273536 |
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| author | Liu, Chengwei Wang, Chong Cao, Jiayue Ge, Jingquan Wang, Kun Zhang, Lyuye Cheng, Ming-Ming Zhao, Penghai Li, Tianlin Jia, Xiaojun Li, Xiang Li, Xingshuai Liu, Yang Feng, Yebo Huang, Yihao Xu, Yijia Sun, Yuqiang Zhou, Zhenhong Xu, Zhengzi |
| author_facet | Liu, Chengwei Wang, Chong Cao, Jiayue Ge, Jingquan Wang, Kun Zhang, Lyuye Cheng, Ming-Ming Zhao, Penghai Li, Tianlin Jia, Xiaojun Li, Xiang Li, Xingshuai Liu, Yang Feng, Yebo Huang, Yihao Xu, Yijia Sun, Yuqiang Zhou, Zhenhong Xu, Zhengzi |
| contents | This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18765 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Vision for Auto Research with LLM Agents Liu, Chengwei Wang, Chong Cao, Jiayue Ge, Jingquan Wang, Kun Zhang, Lyuye Cheng, Ming-Ming Zhao, Penghai Li, Tianlin Jia, Xiaojun Li, Xiang Li, Xingshuai Liu, Yang Feng, Yebo Huang, Yihao Xu, Yijia Sun, Yuqiang Zhou, Zhenhong Xu, Zhengzi Artificial Intelligence This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes. |
| title | A Vision for Auto Research with LLM Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.18765 |