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New AutoML framework evolves executable Python pipelines using LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed LACE, a novel AutoML framework that utilizes a large language model as a variation operator to evolve complete executable pipeline programs. Unlike traditional AutoML systems that search within a predefined space of components, LACE generates and maintains a population of scikit-learn-compatible Python classes. This approach allows for direct inspection and editing of the generated pipelines. Evaluations on 68 OpenML classification tasks show LACE, powered by GPT-5.4-mini, significantly outperforms auto-sklearn, H2O, and XGBoost, and is competitive with AutoGluon. AI

IMPACT This research introduces a novel approach to AutoML by leveraging LLMs to generate editable Python code, potentially improving pipeline transparency and reusability for practitioners.

RANK_REASON The cluster contains an academic paper detailing a new framework and its evaluation.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AutoML framework evolves executable Python pipelines using LLMs

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn, Thomas B\"ack, Niki van Stein ·

    Evolving Executable Pipeline Programs for AutoML with Language Models

    arXiv:2608.16416v1 Announce Type: new Abstract: Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and tune known components, but cannot produce structure…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Niki van Stein ·

    Evolving Executable Pipeline Programs for AutoML with Language Models

    Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and tune known components, but cannot produce structure outside that space. We present LACE, an AutoML …

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