{"ID":5937803,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T20:46:00.777567606Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04508","arxiv_id":"2607.04508","title":"Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery","abstract":"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.","short_abstract":"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 ex...","url_abs":"https://arxiv.org/abs/2607.04508","url_pdf":"https://arxiv.org/pdf/2607.04508v1","authors":"[\"Kyunghoon Hur\",\"Chihun Lee\"]","published":"2026-07-05T21:17:45Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.RO\"]","methods":"[]","has_code":false}