OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

• URL: http://arxiv.org/abs/2604.26211v1
• Date: Wed, 29 Apr 2026 01:30:08 GMT
• Title: OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
• Abstract summary: We introduce a full, end-to-end framework, that starts at idea generation and ends with executable code.<n>The OMEGA framework has been utilized to generate several novel algorithms that outperform scikit-learn baselines.<n>You can access models discussed in this paper and more in the python package: pip install omega-models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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