OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 29 Apr 2026.
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References · 11
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2014OpenML: networked science in machine learningcited by 523
- 2024Evolutionary Optimization of Model Merging Recipescited by 204
- 2020AutoML-Zero: Evolving Machine Learning Algorithms From Scratchcited by 137
- 2023Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extractioncited by 32
- 2023Explanations of Black-Box Models based on Directional Feature Interactionscited by 15
- 2025Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimizationcited by 15
- 2023Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Codecited by 9
- 2025Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspectivecited by 7
- 2025RefleXGen:The unexamined code is not worth usingcited by 4
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