@inproceedings{gao-etal-2026-swe,
title = "{SWE}-{MEM}: Learning Adaptive Memory Management for Long-Horizon Coding Agents",
author = "Gao, Shuzheng and
Zeng, Wenhao and
Yu, Zhaojian and
Wangni, Jianqiao and
Wang, Chaozheng and
Cai, Kai and
He, Shilin and
Lyu, Michael R.",
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.1312/",
pages = "29855--29881",
ISBN = "979-8-89176-459-0",
abstract = "Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods rely on static compression workflows or rigid compression schedules, and often decouple memory decisions from task-solving performance. We present \textsc{SWE-MeM}, a training framework for proactive and on-demand memory management in software engineering agents. \textsc{SWE-MeM} provides a flexible memory tool that lets agents decide when, what, and how to compress based on trajectory state, task progress, and remaining context budget. We train agents with synthesized proactive memory-management trajectories and Memory-aware GRPO, which jointly optimizes memory management and issue resolution through memory-aware trajectory splitting and step-level credit assignment. On SWE-Bench Verified, \textsc{SWE-MeM} achieves 43.4{\%} and 60.2{\%} resolve rate with 4B and 30B models, respectively, outperforming existing memory management baselines in both performance and efficiency."
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<abstract>Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods rely on static compression workflows or rigid compression schedules, and often decouple memory decisions from task-solving performance. We present SWE-MeM, a training framework for proactive and on-demand memory management in software engineering agents. SWE-MeM provides a flexible memory tool that lets agents decide when, what, and how to compress based on trajectory state, task progress, and remaining context budget. We train agents with synthesized proactive memory-management trajectories and Memory-aware GRPO, which jointly optimizes memory management and issue resolution through memory-aware trajectory splitting and step-level credit assignment. On SWE-Bench Verified, SWE-MeM achieves 43.4% and 60.2% resolve rate with 4B and 30B models, respectively, outperforming existing memory management baselines in both performance and efficiency.</abstract>
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%0 Conference Proceedings
%T SWE-MEM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
%A Gao, Shuzheng
%A Zeng, Wenhao
%A Yu, Zhaojian
%A Wangni, Jianqiao
%A Wang, Chaozheng
%A Cai, Kai
%A He, Shilin
%A Lyu, Michael R.
%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 gao-etal-2026-swe
%X Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods rely on static compression workflows or rigid compression schedules, and often decouple memory decisions from task-solving performance. We present SWE-MeM, a training framework for proactive and on-demand memory management in software engineering agents. SWE-MeM provides a flexible memory tool that lets agents decide when, what, and how to compress based on trajectory state, task progress, and remaining context budget. We train agents with synthesized proactive memory-management trajectories and Memory-aware GRPO, which jointly optimizes memory management and issue resolution through memory-aware trajectory splitting and step-level credit assignment. On SWE-Bench Verified, SWE-MeM achieves 43.4% and 60.2% resolve rate with 4B and 30B models, respectively, outperforming existing memory management baselines in both performance and efficiency.
%U https://aclanthology.org/2026.emnlp-main.1312/
%P 29855-29881
Markdown (Informal)
[SWE-MEM: Learning Adaptive Memory Management for Long-Horizon Coding Agents](https://aclanthology.org/2026.emnlp-main.1312/) (Gao et al., EMNLP 2026)
ACL
- Shuzheng Gao, Wenhao Zeng, Zhaojian Yu, Jianqiao Wangni, Chaozheng Wang, Kai Cai, Shilin He, and Michael R. Lyu. 2026. SWE-MEM: Learning Adaptive Memory Management for Long-Horizon Coding Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29855–29881, Budapest, Hungary. Association for Computational Linguistics.