MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915293669883904 |
|---|---|
| author | Fang, Haoyang Han, Boran Erickson, Nick Zhang, Xiyuan Zhou, Su Dagar, Anirudh Zhang, Jiani Turkmen, Ali Caner Hu, Cuixiong Rangwala, Huzefa Wu, Ying Nian Wang, Bernie Karypis, George |
| author_facet | Fang, Haoyang Han, Boran Erickson, Nick Zhang, Xiyuan Zhou, Su Dagar, Anirudh Zhang, Jiani Turkmen, Ali Caner Hu, Cuixiong Rangwala, Huzefa Wu, Ying Nian Wang, Bernie Karypis, George |
| contents | Existing AutoML systems have advanced the automation of machine learning (ML); however, they still require substantial manual configuration and expert input, particularly when handling multimodal data. We introduce MLZero, a novel multi-agent framework powered by Large Language Models (LLMs) that enables end-to-end ML automation across diverse data modalities with minimal human intervention. A cognitive perception module is first employed, transforming raw multimodal inputs into perceptual context that effectively guides the subsequent workflow. To address key limitations of LLMs, such as hallucinated code generation and outdated API knowledge, we enhance the iterative code generation process with semantic and episodic memory. MLZero demonstrates superior performance on MLE-Bench Lite, outperforming all competitors in both success rate and solution quality, securing six gold medals. Additionally, when evaluated on our Multimodal AutoML Agent Benchmark, which includes 25 more challenging tasks spanning diverse data modalities, MLZero outperforms the competing methods by a large margin with a success rate of 0.92 (+263.6\%) and an average rank of 2.28. Our approach maintains its robust effectiveness even with a compact 8B LLM, outperforming full-size systems from existing solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MLZero: A Multi-Agent System for End-to-end Machine Learning Automation Fang, Haoyang Han, Boran Erickson, Nick Zhang, Xiyuan Zhou, Su Dagar, Anirudh Zhang, Jiani Turkmen, Ali Caner Hu, Cuixiong Rangwala, Huzefa Wu, Ying Nian Wang, Bernie Karypis, George Multiagent Systems Artificial Intelligence Computation and Language Machine Learning Existing AutoML systems have advanced the automation of machine learning (ML); however, they still require substantial manual configuration and expert input, particularly when handling multimodal data. We introduce MLZero, a novel multi-agent framework powered by Large Language Models (LLMs) that enables end-to-end ML automation across diverse data modalities with minimal human intervention. A cognitive perception module is first employed, transforming raw multimodal inputs into perceptual context that effectively guides the subsequent workflow. To address key limitations of LLMs, such as hallucinated code generation and outdated API knowledge, we enhance the iterative code generation process with semantic and episodic memory. MLZero demonstrates superior performance on MLE-Bench Lite, outperforming all competitors in both success rate and solution quality, securing six gold medals. Additionally, when evaluated on our Multimodal AutoML Agent Benchmark, which includes 25 more challenging tasks spanning diverse data modalities, MLZero outperforms the competing methods by a large margin with a success rate of 0.92 (+263.6\%) and an average rank of 2.28. Our approach maintains its robust effectiveness even with a compact 8B LLM, outperforming full-size systems from existing solutions. |
| title | MLZero: A Multi-Agent System for End-to-end Machine Learning Automation |
| topic | Multiagent Systems Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.13941 |