SelfAI: A self-directed framework for long-horizon scientific discovery
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908848193798144 |
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| author | Wu, Xiao Huang, Ting-Zhu Deng, Liang-Jian Yu, Xiaobing Zhong, Yu Deng, Shangqi Khan, Ufaq Wu, Jianghao Liu, Xiaofeng Razzak, Imran Chang, Xiaojun Xie, Yutong |
| author_facet | Wu, Xiao Huang, Ting-Zhu Deng, Liang-Jian Yu, Xiaobing Zhong, Yu Deng, Shangqi Khan, Ufaq Wu, Jianghao Liu, Xiaofeng Razzak, Imran Chang, Xiaojun Xie, Yutong |
| contents | Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited insight into how scientific exploration unfolds over time, particularly balancing efficiency-diversity trade-offs and supporting reproducible, human-in-the-loop discovery workflows. We introduce SelfAI, a self-directed, multi-agent-enabled discovery system that automates scientific exploration as a strategic, trajectory-driven decision-making process. SelfAI translates high-level research intent into executable experiments, reasons over accumulated experimental trajectories to guide subsequent exploration, and applies adaptive stopping decisions to terminate unproductive search paths within a closed-loop workflow governed by explicit efficiency-diversity trade-offs. Evaluated using real-world experiments spanning domains from machine learning to drug discovery, SelfAI consistently discovers high-quality solutions with substantially fewer redundant trials than classical optimization and recent LLM-based baselines. The proposed methods establish a general framework for organizing long-horizon scientific discovery and adaptive decision-making in complex scientific and engineering systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SelfAI: A self-directed framework for long-horizon scientific discovery Wu, Xiao Huang, Ting-Zhu Deng, Liang-Jian Yu, Xiaobing Zhong, Yu Deng, Shangqi Khan, Ufaq Wu, Jianghao Liu, Xiaofeng Razzak, Imran Chang, Xiaojun Xie, Yutong Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited insight into how scientific exploration unfolds over time, particularly balancing efficiency-diversity trade-offs and supporting reproducible, human-in-the-loop discovery workflows. We introduce SelfAI, a self-directed, multi-agent-enabled discovery system that automates scientific exploration as a strategic, trajectory-driven decision-making process. SelfAI translates high-level research intent into executable experiments, reasons over accumulated experimental trajectories to guide subsequent exploration, and applies adaptive stopping decisions to terminate unproductive search paths within a closed-loop workflow governed by explicit efficiency-diversity trade-offs. Evaluated using real-world experiments spanning domains from machine learning to drug discovery, SelfAI consistently discovers high-quality solutions with substantially fewer redundant trials than classical optimization and recent LLM-based baselines. The proposed methods establish a general framework for organizing long-horizon scientific discovery and adaptive decision-making in complex scientific and engineering systems. |
| title | SelfAI: A self-directed framework for long-horizon scientific discovery |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.00403 |