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[2512.00403] SelfAI: A self-directed framework for long-horizon scientific discovery
Summary
The paper introduces SelfAI, a self-directed framework designed for long-horizon scientific discovery, emphasizing efficient exploration and adaptive decision-making in complex hypothesis spaces.
Why It Matters
SelfAI addresses the challenges of traditional scientific discovery methods by automating exploration and balancing efficiency with diversity. This framework can significantly enhance research productivity and reproducibility, making it relevant for various fields, including machine learning and drug discovery.
Key Takeaways
- SelfAI automates scientific exploration through a multi-agent system.
- It translates high-level research intents into executable experiments.
- The framework supports adaptive stopping decisions to optimize search paths.
- SelfAI has shown to discover high-quality solutions with fewer trials than traditional methods.
- This approach can be applied across various domains, enhancing long-horizon scientific discovery.
Computer Science > Machine Learning arXiv:2512.00403 (cs) [Submitted on 29 Nov 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:SelfAI: A self-directed framework for long-horizon scientific discovery Authors:Xiao Wu, Ting-Zhu Huang, Liang-Jian Deng, Xiaobing Yu, Yu Zhong, Shangqi Deng, Ufaq Khan, Jianghao Wu, Xiaofeng Liu, Imran Razzak, Xiaojun Chang, Yutong Xie View a PDF of the paper titled SelfAI: A self-directed framework for long-horizon scientific discovery, by Xiao Wu and 11 other authors View PDF HTML (experimental) Abstract: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 discove...