AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
The article discusses the advancements in AI systems aimed at automating scientific research workflows. It highlights the transition from task-level assistance to more comprehensive workflow automation, while noting the challenges that remain. The authors propose a framework for evaluating these AI systems based on various dimensions of autonomy and effectiveness in research settings.
- ▪AI systems are evolving from isolated assistance to comprehensive workflow automation in scientific research.
- ▪Current AI research systems face challenges such as fragmentation, evidence preservation, and reproducibility.
- ▪The authors propose five evaluation dimensions for assessing the effectiveness of AI in research workflows.
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Computer Science > Artificial Intelligence arXiv:2605.23204 (cs) [Submitted on 22 May 2026] Title:AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery Authors:Guiyao Tie, Jiawen Shi, Dingjie Song, Yixiao Huang, Ziji Sheng, Xueyang Zhou, Daizong Liu, Pan Zhou, Yongchao Chen, Ran Xu, Lifang He, Qingsong Wen, Manling Li, Cong Lu, Shuai Li, Pengtao Xie, Yixuan Yuan, Rui Meng, Lei Xing, Lichao Sun, Caiming Xiong, Philip S. Yu, Jianfeng Gao View a PDF of the paper titled AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery, by Guiyao Tie and 22 other authors View PDF HTML (experimental) Abstract:Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature…
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