用智能代理重构科研流程，实现自动化的科学发现。
The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
- 提出双维度演化框架：智能性从静态到智能，组合性从单体到群体。
- 构建架构蓝图，推动科研流程向自主实验室演进，有望提升百倍发现效率。
- 适合关注智能科研、自动化实验系统的研究者和工程团队。
现代科学发现越来越依赖分布式设施和异构资源的协同，迫使研究人员充当手动工作流协调者，而非科学家。人工智能的发展催生了智能代理，为科学发现提供了新机遇，可将智能作为生态系统中的组件。然而，这种能力如何落地并整合到现实世界尚不明确。为此，我们提出一个概念框架，指出工作流沿两个维度演化：智能性（从静态到智能）与组合性（从单体到群体），描绘出从现有工作流管理系统迈向完全自主、分布式的科学实验室的演进路径。基于此，我们提出一个架构蓝图，助力社区迈向自主科学的新阶段，有望实现百倍加速的科学发现和变革性的科研工作流。
原文摘要 · Abstract (English)
Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous, distributed scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows.
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