検証ボトルネックの圧縮：科学的発見のためのエージェント型セルフドライビングラボ
Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery
実験検証のボトルネックを減らすため、事前知識を活用した実験計画と、低コスト測定から高コスト測定を予測するサロゲートモデルを組み合わせたエージェントを提案し、生物学と材料科学でのループ数とコスト削減を目指す。
著者: Kyunghoon Hur, Chihun Lee
分類: cs.AI, cs.RO
原文アブストラクト
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.