@inproceedings{tian-etal-2026-eema,
title = "{EEMA}: Experience-Evolving Meta Agents for {A}uto{ML}",
author = "Tian, Kai and
Wang, Weizhi and
Zhao, Kaikai and
Jiang, Che and
Yang, Junlin and
Yuan, Zhenzhao and
Zhong, Jincheng and
Jia, Guoli and
Wang, Yuru and
Long, Xinwei and
Ding, Ning and
Zhou, Bowen and
Zhang, Kaiyan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.873/",
pages = "20188--20212",
ISBN = "979-8-89176-459-0",
abstract = "Open machine learning engineering tasks require agents to interpret data, build models, debug code, and produce valid competition submissions. Although current coding agents can generate runnable programs, they often rediscover solutions across tasks and repeat engineering mistakes across feedback rounds. We propose EEMA, an experience-evolving meta-agent framework that organizes accumulated experience by purpose and routes it during multi-round solving. Its static component distills public competition material and historical failure traces into compact Skills: task-level modeling priors for improving solution quality and failure-prevention contracts for maintaining execution validity. Its dynamic component, Experience Routed Search (ERS), operates above a lower-level coding agent. At each round, ERS selects a draft, debug, or improve action, routes the corresponding Skill, and updates task memory, allowing cross-task experience to guide code generation while task-level feedback shapes later decisions. On MLE-bench Lite (22 tasks), EEMA achieves a 77.3{\%} medal rate, 20.1{\%} average leaderboard Grade, and 96.0{\%} execution success, outperforming all baselines including GPT-5.5-xhigh. Ablations show that task Skills improve solution quality, failure-prevention Skills improve reliability, and ERS coordinates both effectively. The materials will be released at https://github.com/FrontisAI/OpenRSI."
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<abstract>Open machine learning engineering tasks require agents to interpret data, build models, debug code, and produce valid competition submissions. Although current coding agents can generate runnable programs, they often rediscover solutions across tasks and repeat engineering mistakes across feedback rounds. We propose EEMA, an experience-evolving meta-agent framework that organizes accumulated experience by purpose and routes it during multi-round solving. Its static component distills public competition material and historical failure traces into compact Skills: task-level modeling priors for improving solution quality and failure-prevention contracts for maintaining execution validity. Its dynamic component, Experience Routed Search (ERS), operates above a lower-level coding agent. At each round, ERS selects a draft, debug, or improve action, routes the corresponding Skill, and updates task memory, allowing cross-task experience to guide code generation while task-level feedback shapes later decisions. On MLE-bench Lite (22 tasks), EEMA achieves a 77.3% medal rate, 20.1% average leaderboard Grade, and 96.0% execution success, outperforming all baselines including GPT-5.5-xhigh. Ablations show that task Skills improve solution quality, failure-prevention Skills improve reliability, and ERS coordinates both effectively. The materials will be released at https://github.com/FrontisAI/OpenRSI.</abstract>
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%0 Conference Proceedings
%T EEMA: Experience-Evolving Meta Agents for AutoML
%A Tian, Kai
%A Wang, Weizhi
%A Zhao, Kaikai
%A Jiang, Che
%A Yang, Junlin
%A Yuan, Zhenzhao
%A Zhong, Jincheng
%A Jia, Guoli
%A Wang, Yuru
%A Long, Xinwei
%A Ding, Ning
%A Zhou, Bowen
%A Zhang, Kaiyan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tian-etal-2026-eema
%X Open machine learning engineering tasks require agents to interpret data, build models, debug code, and produce valid competition submissions. Although current coding agents can generate runnable programs, they often rediscover solutions across tasks and repeat engineering mistakes across feedback rounds. We propose EEMA, an experience-evolving meta-agent framework that organizes accumulated experience by purpose and routes it during multi-round solving. Its static component distills public competition material and historical failure traces into compact Skills: task-level modeling priors for improving solution quality and failure-prevention contracts for maintaining execution validity. Its dynamic component, Experience Routed Search (ERS), operates above a lower-level coding agent. At each round, ERS selects a draft, debug, or improve action, routes the corresponding Skill, and updates task memory, allowing cross-task experience to guide code generation while task-level feedback shapes later decisions. On MLE-bench Lite (22 tasks), EEMA achieves a 77.3% medal rate, 20.1% average leaderboard Grade, and 96.0% execution success, outperforming all baselines including GPT-5.5-xhigh. Ablations show that task Skills improve solution quality, failure-prevention Skills improve reliability, and ERS coordinates both effectively. The materials will be released at https://github.com/FrontisAI/OpenRSI.
%U https://aclanthology.org/2026.emnlp-main.873/
%P 20188-20212
Markdown (Informal)
[EEMA: Experience-Evolving Meta Agents for AutoML](https://aclanthology.org/2026.emnlp-main.873/) (Tian et al., EMNLP 2026)
ACL
- Kai Tian, Weizhi Wang, Kaikai Zhao, Che Jiang, Junlin Yang, Zhenzhao Yuan, Jincheng Zhong, Guoli Jia, Yuru Wang, Xinwei Long, Ning Ding, Bowen Zhou, and Kaiyan Zhang. 2026. EEMA: Experience-Evolving Meta Agents for AutoML. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20188–20212, Budapest, Hungary. Association for Computational Linguistics.