Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard. Focusing on short-horizon ($\le 10$) adaptive experiments, we move away from bespoke algorithms and present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures. We formulating a dynamic program based on central limit approximations, which enables the use of scalable optimization methods based on auto-differentiation and GPU parallelization. To evaluate our framework, we implement a simple heuristic planning method ("solver") and benchmark it across hundreds of problem instances involving non-stationarity, personalization, and multiple objectives & constraints. Unlike bespoke methods (e.g., Thompson sampling variants), our mathematical programming framework provides consistent gains over static randomized control trials and exhibits robust performance across problem instances.
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory（橡树岭国家实验室纳米相材料中心）；University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee（田纳西大学橡树岭创新研究所）；Department of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo（东京科学大学材料科学与工程系、材料与化学技术学院）；Department of Materials Science and Engineering, Norwegian University of Science and Technology (NTNU)（挪威科学技术大学（NTNU）材料科学与工程系）；Faculty of Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen（杜伊斯堡- Essen大学物理系和杜伊斯堡- Essen纳米集成中心（CENIDE））；Research Center Future Energy Materials and Systems, Research Alliance Ruhr（鲁尔研究联盟未来能源材料与系统研究中心）
LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）；Institute for Advanced Study in Mathematics, Harbin Institute of Technology（哈尔滨工业大学数学研究所）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；School of Automation, Central South University（中南大学自动化学院）；College of New Energy and Materials, China University of Petroleum, Beijing（中国石油大学（北京）新能源与材料学院）；College of Carbon Neutrality Future Technology, China University of Petroleum, Beijing（中国石油大学（北京）碳中和未来技术学院）；DeepVerse PTE. LTD.
