Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery
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 h...
| Authors | Kyunghoon Hur, Chihun Lee |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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