SelfAI让科研探索像下棋一样有策略，自动规划实验路径。
SelfAI: A self-directed framework for long-horizon scientific discovery
- 用多智能体系统将研究目标转化为可执行实验，动态调整探索方向。
- 相比传统方法减少大量重复试验，在多个领域找到高质量解。
- 适合需要长期探索、强调效率与多样性平衡的科研团队使用。
科学发现越来越依赖对复杂假设空间的长周期探索，但现有方法多关注最终性能，缺乏对探索过程的洞察，尤其在效率与多样性权衡及可复现的人机协作流程方面能力不足。我们提出SelfAI，一种自导向的多智能体发现系统，将科学探索转化为战略性的、以轨迹为导向的决策过程。SelfAI将高层次研究意图转化为可执行实验，基于累积实验轨迹进行推理以指导后续探索，并应用自适应终止策略在闭环工作流中及时关闭低效路径，明确控制效率与多样性权衡。在涵盖机器学习到药物发现的真实实验中评估，SelfAI持续以更少冗余试验发现高质量解决方案，显著优于经典优化方法和近期基于大模型的基线。所提方法为复杂科学与工程系统中的长周期发现与自适应决策提供了通用框架。
原文摘要 · Abstract (English)
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.
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