Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
A new framework called LLM-Guided Bayesian Optimization (LGBO) has been proposed to enhance scientific discovery through efficient optimization. This framework integrates large language models (LLMs) into the optimization process, addressing challenges like slow performance and scalability. Empirical results show that LGBO significantly outperforms existing methods in various scientific fields, achieving faster convergence in optimization tasks.
- ▪LGBO is the first preference-guided Bayesian Optimization framework that incorporates LLMs into the optimization loop.
- ▪The framework introduces a region-lifted preference mechanism that stabilizes the optimization process.
- ▪In experiments, LGBO achieved 90% of the best observed value in optimizing Fe-Cr battery electrolytes within just 6 iterations.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17976 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17976 (cs) [Submitted on 18 May 2026] Title:Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery Authors:Xinzhe Yuan, Zhuo Chen, Jianshu Zhang, Huan Xiong, Nanyang Ye, Yuqiang Li, Qinying Gu View a PDF of the paper titled Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery, by Xinzhe Yuan and 6 other authors View PDF HTML (experimental) Abstract:Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science.
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