LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
arXiv:2605.22054v1 Announce Type: new Abstract: 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
The increasing computational cost and data scarcity in scientific discovery are driving the need for more efficient optimization methods, making LLM integration timely.
This development could significantly accelerate scientific research and industrial innovation by making high-cost experiments more efficient and less resource-intensive.
Traditional Bayesian Optimization, which often relies on costly real-world experiments, can now be augmented by LLMs to explore broader possibilities at a lower initial cost.
- · AI/ML researchers
- · R&D intensive industries
- · Drug discovery companies
- · Materials science
Scientific discovery processes become more efficient and faster due to reduced experimental costs and broader exploration.
Industries reliant on experimental data, such as pharmaceuticals and new material development, will see accelerated innovation cycles, potentially increasing competition.
The democratization of advanced research capabilities may increase the number of breakthroughs and reduce the dominance of large, well-funded research institutions.
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Read at arXiv cs.LG