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

arXiv: https://arxiv.org/abs/2605.22054 · HTML: https://mlsanity.com/paper/2605.22054/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 16 of 16)

- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Large Language Models to Enhance Bayesian Optimization](https://mlsanity.com/paper/2402.03921/) (2024)
- [HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO](https://mlsanity.com/paper/2109.06716/) (2021)
- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [A General Framework for User-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.14645/) (2023)
- [Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs](https://mlsanity.com/paper/2509.17998/) (2025)
- [LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization](https://mlsanity.com/paper/2505.14756/) (2025)
- [Reasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs](https://mlsanity.com/paper/2505.12833/) (2025)
- [Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling](https://mlsanity.com/paper/2505.23913/) (2025)
- [Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery](https://mlsanity.com/paper/2605.17976/) (2026)
- [Thompson Sampling via Fine-Tuning of LLMs](https://mlsanity.com/paper/2510.13328/) (2025)
- [ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System](https://mlsanity.com/paper/2509.08736/) (2025)
- [LLM Based Bayesian Optimization for Prompt Search](https://mlsanity.com/paper/2510.04384/) (2025)

## Built on by (most cited first, 6 of 6)

- [Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch](https://mlsanity.com/paper/2608.00316/) (2026)
- [Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization](https://mlsanity.com/paper/2608.10694/) (2026)
- [Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search](https://mlsanity.com/paper/2608.15669/) (2026)
- [Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Search](https://mlsanity.com/paper/2609.34281/) (2026)
- [Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation](https://mlsanity.com/paper/2609.36788/) (2026)
- [Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design](https://mlsanity.com/paper/2610.17832/) (2026)

## Abstract (arXiv)

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.