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OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

Artificial Intelligence 2026-04-30 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.2604.26211,
  title  = {OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms},
  author = {Jeremy Nixon and Annika Singh},
  journal= {arXiv preprint arXiv:2604.26211},
  year   = {2026}
}

Comments

ICLR 2026: Workshop on AI with Recursive Self-Improvement