Papers with machine learning engineering
Learning to Ideate for Machine Learning Engineering Agents (2026.eacl-short)
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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. |
| Approach: | They propose a framework that separates ideation from implementation that allows an implementation agent to request strategic help from a dedicated Ideator. |
| Outcome: | The proposed framework outperforms implementation-only agent baselines on MLE-Bench and can be trained with reinforcement learning to generate more effective ideas. |
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)
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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian
| Challenge: | LLM-based agents for machine learning engineering rely on tree search to rank candidates. |
| Approach: | They propose an LLM-based agent that operationalizes gradient-based optimization. |
| Outcome: | The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU. |
Demystify the Role of Memory in Machine Learning Engineering Agents (2026.findings-acl)
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Xinyu Zhao, Junpeng Wang, Yuzhong Chen, Menghai Pan, Chin-Chia Michael Yeh, Jiarui Sun, Yan Zheng, Mahashweta Das, Tianlong Chen
| Challenge: | Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success. |
| Approach: | They propose a dynamic coding memory that captures and reuses debugging experiences and integrates it into two representative agent paradigms. |
| Outcome: | The proposed agent model captures and reuses debugging experiences and integrates it into two agent paradigms. |