# OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

arXiv: https://arxiv.org/abs/2604.26211 · HTML: https://mlsanity.com/paper/2604.26211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Evolutionary Optimization of Model Merging Recipes](https://mlsanity.com/paper/2403.13187/) (2024)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization](https://mlsanity.com/paper/2502.04295/) (2025)
- [Explanations of Black-Box Models based on Directional Feature Interactions](https://mlsanity.com/paper/2304.07670/) (2023)
- [Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code](https://mlsanity.com/paper/2310.10508/) (2023)
- [Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective](https://mlsanity.com/paper/2508.01443/) (2025)
- [RefleXGen:The unexamined code is not worth using](https://mlsanity.com/paper/2510.23674/) (2025)

## 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.