用智能代理优化科学实验，减少试错次数和实验成本。
Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery
- 结合领域知识与历史数据，智能规划高信息量实验。
- 通过低成本测量预测高成本数据，降低每轮实验开销。
- 适合生物与材料领域科研自动化，加速发现进程。
科学领域的智能代理可自动完成设想、规划与分析，但最终验证仍依赖真实实验。自驱动实验室（SDL）能执行这些实验，但流程中存在两大物理瓶颈：代理可能在低价值实验上耗费过多轮次，或每轮实验成本过高。本文提出单一智能体解决这两个问题：首先，基于先验知识的智能实验设计（DOE）循环利用领域知识与过往结果，提出可行且高信息量的下一阶段实验，减少达到目标所需的实验次数；其次，成本感知的代理模型通过低成本、低分辨率测量预测高成本、高分辨率数据，并根据预测不确定性动态选择测量方式。研究分别在生物学和材料学领域验证了上述方法。两者协同作用下，该智能体可同时降低实验循环次数与单次实验成本，显著加速自驱动实验室的探索效率。
原文摘要 · Abstract (English)
Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments. A self-driving lab (SDL) can execute those experiments, yet the loop still has bottlenecks: the agent may spend too many rounds on low-value experiments, or each round may require a high-cost experiment. We target these two physical bottlenecks with one agent. First, a prior-aware agentic DOE loop uses domain knowledge and past results to propose feasible and informative next experiments, reducing trials-to-target. Second, a cost-aware surrogate agent predicts high-cost, high-resolution measurements from low-cost, low-resolution measurements. It chooses between a high- and a low-cost measurement based on the predicted uncertainty. We examine these directions in the biology and materials domains, respectively. Together, under a single agent, these components aim to accelerate the SDL loop by reducing both the number of loops and the cost per experiment.
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