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OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

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arXiv:2604.26211v1 Announce Type: new 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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Topic: OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
Source website: arxiv.org
Publication date at source: 2026-04-30
Collection date on DzCademia: 2026-05-01

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OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

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