Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models

• URL: http://arxiv.org/abs/2602.07904v1
• Date: Sun, 08 Feb 2026 10:53:52 GMT
• Title: Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models
• Abstract summary: LMABO is a novel framework that casts a pre-trained Large Language Model as a zero-shot, online strategist for the BO process.<n>We show that the LLM's behavior is a comprehensive strategy that adapts to real-time progress, proving its advantage stems from its ability to process and synthesize the complete optimization state into an effective, adaptive policy.
• Score: 29.41740966512592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian Optimization critically depends on the choice of acquisition function, but no single strategy is universally optimal; the best choice is non-stationary and problem-dependent. Existing adaptive portfolio methods often base their decisions on past function values while ignoring richer information like remaining budget or surrogate model characteristics. To address this, we introduce LMABO, a novel framework that casts a pre-trained Large Language Model (LLM) as a zero-shot, online strategist for the BO process. At each iteration, LMABO uses a structured state representation to prompt the LLM to select the most suitable acquisition function from a diverse portfolio. In an evaluation across 50 benchmark problems, LMABO demonstrates a significant performance improvement over strong static, adaptive portfolio, and other LLM-based baselines. We show that the LLM's behavior is a comprehensive strategy that adapts to real-time progress, proving its advantage stems from its ability to process and synthesize the complete optimization state into an effective, adaptive policy.

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