@inproceedings{wang-etal-2026-memgovern,
title = "{M}em{G}overn: Enhancing Code Agents through Learning from Governed Human Experiences",
author = "Wang, Qihao and
Cheng, Ziming and
Zhang, Shuo and
Liu, Fan and
Xu, Rui and
Lian, Heng and
Wang, Kunyi and
Yu, Xiaomin and
Yin, Jianghao and
Hu, Sen and
Hu, Yue and
Zhang, Shaolei and
Liu, Yanbing and
Chen, Ronghao and
Wang, Huacan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.898/",
pages = "19012--19033",
ISBN = "979-8-89176-462-0",
abstract = "While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a ``closed-world'' limitation: they attempt to fix bugs from scratch or solely using information from the current repository, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench benchmark by 4.65{\%}. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure."
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<abstract>While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a “closed-world” limitation: they attempt to fix bugs from scratch or solely using information from the current repository, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench benchmark by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.</abstract>
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%0 Conference Proceedings
%T MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
%A Wang, Qihao
%A Cheng, Ziming
%A Zhang, Shuo
%A Liu, Fan
%A Xu, Rui
%A Lian, Heng
%A Wang, Kunyi
%A Yu, Xiaomin
%A Yin, Jianghao
%A Hu, Sen
%A Hu, Yue
%A Zhang, Shaolei
%A Liu, Yanbing
%A Chen, Ronghao
%A Wang, Huacan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-memgovern
%X While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a “closed-world” limitation: they attempt to fix bugs from scratch or solely using information from the current repository, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench benchmark by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.
%U https://aclanthology.org/2026.findings-emnlp.898/
%P 19012-19033
Markdown (Informal)
[MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences](https://aclanthology.org/2026.findings-emnlp.898/) (Wang et al., Findings 2026)
ACL
- Qihao Wang, Ziming Cheng, Shuo Zhang, Fan Liu, Rui Xu, Heng Lian, Kunyi Wang, Xiaomin Yu, Jianghao Yin, Sen Hu, Yue Hu, Shaolei Zhang, Yanbing Liu, Ronghao Chen, and Huacan Wang. 2026. MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19012–19033, Budapest, Hungary. Association for Computational Linguistics.