Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

Ricardo Luna Gutierrez; Sahand Ghorbanpour; Ejaz Rahman; Varchas Gopalaswamy; Riccardo Betti; Vineet Gundecha; Aarne Lees; Soumyendu Sarkar

cs.LGApr 30, 2026

Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

Authors: Ricardo Luna Gutierrez, Sahand Ghorbanpour, Ejaz Rahman, Varchas Gopalaswamy, Riccardo Betti, Vineet Gundecha, Aarne Lees, Soumyendu Sarkar

Organizations: 1Hewlett Packard Enterprise · University of Rochester

Abstract

Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experimental opportunities. This paper presents Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework that integrates expert knowledge with few-shot, uncertainty-aware machine learning to accelerate discovery in data-scarce, high-stakes scientific domains. HL-MBO introduces a meta-learned surrogate model with an expert-informed acquisition function to recommend candidate experiments. To foster trust and enable informed decisions, HL-MBO also provides interpretable explanations of its suggestions. We show HL-MBO outperforms current BO methods on ICF energy yield optimization, as well as benchmarks in molecular optimization and critical temperature maximization for superconducting materials.

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May 21, 2026 · cs.LGJ/K move · Enter open · S save

LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation

Zhuo Chen, Xinzhe Yuan, Jianshu Zhang, Jinzong Dong+7

equal contribution · Shanghai Artificial Intelligence Laboratory, Shanghai, China · School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · School of Automation, Central South University, Changsha, China · College of New Energy and Materials, China University of Petroleum, Beijing, China

The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). However, existing approaches typically embed LLMs directly into the sampling or surrogate modeling pipeline, without fully leveraging their significantly lower evaluation cost compared to real-world experiments. To address this limitation, we propose LLM-Accelerated Bayesian Optimization (LABO), a framework that combines LLM predictions with experimental observations within a single BO loop. LABO employs a gating criterion to dynamically balance the reliance on LLM predictions versus actual experiments. By leveraging inexpensive LLM evaluations to broadly explore the search space and reserving costly real experiments only for regions with high uncertainty, LABO achieves more sample-efficient optimization. We provide a theoretical analysis with a cumulative regret bound that formalizes this efficiency gain. Empirical results across diverse scientific tasks demonstrate that LABO consistently outperforms existing methods under identical experimental budgets. Our results suggest that LABO offers a practical and theoretically grounded approach for integrating LLMs into scientific discovery workflows.

Bayesian Optimization Bayesian Autonomous Exploration Strategies Surrogate Models

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Abstract

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