Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization

• URL: http://arxiv.org/abs/2603.28959v1
• Date: Mon, 30 Mar 2026 20:05:30 GMT
• Title: Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization
• Abstract summary: We show how Large Language Models (LLMs) construct and adapt exploration-exploitation strategies.<n>We propose a multi-agent framework that decomposes exploration-exploitation control into strategic policy mediation and tactical candidate generation.
• Score: 2.6954666679827137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exploration-exploitation trade-off is central to sequential decision-making and black-box optimization, yet how Large Language Models (LLMs) reason about and manage this trade-off remains poorly understood. Unlike Bayesian Optimization, where exploration and exploitation are explicitly encoded through acquisition functions, LLM-based optimization relies on implicit, prompt-based reasoning over historical evaluations, making search behavior difficult to analyze or control. In this work, we present a metric-level study of LLM-mediated search policy learning, studying how LLMs construct and adapt exploration-exploitation strategies under multiple operational definitions of exploration, including informativeness, diversity, and representativeness. We show that single-agent LLM approaches, which jointly perform strategy selection and candidate generation within a single prompt, suffer from cognitive overload, leading to unstable search dynamics and premature convergence. To address this limitation, we propose a multi-agent framework that decomposes exploration-exploitation control into strategic policy mediation and tactical candidate generation. A strategy agent assigns interpretable weights to multiple search criteria, while a generation agent produces candidates conditioned on the resulting search policy defined as weights. This decomposition renders exploration-exploitation decisions explicit, observable, and adjustable. Empirical results across various continuous optimization benchmarks indicate that separating strategic control from candidate generation substantially improves the effectiveness of LLM-mediated search.

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