PACE: Unleashing the Power of Code Embeddings to Boost AutoML Agents

Abstract

Large Language Model (LLM)-driven AutoML agents have shown strong capabilities in constructing end-to-end machine learning pipelines. However, their effectiveness is limited by costly execution-based feedback, which can make the search for high-quality solutions inefficient under restricted computational budgets. We propose PACE (Pre-execution Admission via Code Embeddings), an online-adaptive admission control framework that improves budgeted sample efficiency by estimating candidate utility prior to execution from within-run execution history, without training a separate offline predictor. The core idea of PACE is to leverage latent structure in the solution space as a within-run admission signal. It projects candidate solutions into multi-view semantic embeddings, dynamically organizes executed candidates into clusters, and estimates new candidates by their proximity to historical elite regions. Moreover, PACE aggregates multi-view embeddings via an adaptive reweighting strategy that prioritizes views with higher discriminative power. This enables PACE to bias the agent toward high-potential regions under a limited computational budget while retaining exploration when local structure is weak. We demonstrate that PACE operates as a plug-and-play admission layer for AutoML agents, serving as either an execution gate or a search prior. In the tested settings, it improves the density of elite solutions found within fixed budgets without modifying the underlying agent architecture. Code, configurations, and prompt templates are publicly available at https://github.com/fendss/PACE.