OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

arXiv cs.AI / 4/30/2026

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Key Points

  • The paper introduces OMEGA, an end-to-end framework intended to automate parts of AI research from idea generation through executable code output.
  • OMEGA combines structured meta-prompt engineering with executable code generation to produce new machine-learning classifier algorithms.
  • Using OMEGA, the authors generated multiple novel classifiers that reportedly outperform scikit-learn baselines across 20 benchmark datasets.
  • The work is positioned as practically usable via a Python package (pip install omega-models) that provides models from the paper and additional ones.

Abstract

In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code. Our system combines structured meta-prompt engineering with executable code generation to create new ML classifiers. The OMEGA framework has been utilized to generate several novel algorithms that outperform scikit-learn baselines across a robust selection of 20 benchmark datasets (infinity-bench). You can access models discussed in this paper and more in the python package: pip install omega-models.