@inproceedings{cheng-etal-2026-mem2evolve,
title = "{M}em$^2${E}volve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation",
author = "Cheng, Zihao and
Liu, Zeming and
Shan, Yingyu and
Wang, Xinyi and
Zhu, Xiangrong and
Ma, Yunpu and
Wang, Hongru and
Guo, Yuhang and
Lin, Wei and
Wang, Yunhong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.952/",
pages = "20784--20831",
ISBN = "979-8-89176-390-6",
abstract = "While large language model{--}powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution. To address this limitation, we introduce a novel paradigm of co-evolutionary Capability Expansion and Experience Distillation. Guided by this paradigm, we propose the **Mem$^{\textbf{2}}$Evolve**, which integrates two core components: **Experience Memory** and **Asset Memory**. Specifically, Mem$^{2}$Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent{'}s capability space while simultaneously acquiring new experience to achieve co-evolution. Extensive experiments across 6 task categories and 8 benchmarks demonstrate that Mem$^{2}$Evolve achieves improvement of 18.53{\%} over standard LLMs, 11.80{\%} over agents evolving solely through experience, and 6.46{\%} over those evolving solely through asset creation, establishing it as a substantially more effective and stable self-evolving agent framework."
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<abstract>While large language model–powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution. To address this limitation, we introduce a novel paradigm of co-evolutionary Capability Expansion and Experience Distillation. Guided by this paradigm, we propose the **Mem²Evolve**, which integrates two core components: **Experience Memory** and **Asset Memory**. Specifically, Mem²Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent’s capability space while simultaneously acquiring new experience to achieve co-evolution. Extensive experiments across 6 task categories and 8 benchmarks demonstrate that Mem²Evolve achieves improvement of 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solely through asset creation, establishing it as a substantially more effective and stable self-evolving agent framework.</abstract>
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%0 Conference Proceedings
%T Mem²Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation
%A Cheng, Zihao
%A Liu, Zeming
%A Shan, Yingyu
%A Wang, Xinyi
%A Zhu, Xiangrong
%A Ma, Yunpu
%A Wang, Hongru
%A Guo, Yuhang
%A Lin, Wei
%A Wang, Yunhong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cheng-etal-2026-mem2evolve
%X While large language model–powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution. To address this limitation, we introduce a novel paradigm of co-evolutionary Capability Expansion and Experience Distillation. Guided by this paradigm, we propose the **Mem²Evolve**, which integrates two core components: **Experience Memory** and **Asset Memory**. Specifically, Mem²Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent’s capability space while simultaneously acquiring new experience to achieve co-evolution. Extensive experiments across 6 task categories and 8 benchmarks demonstrate that Mem²Evolve achieves improvement of 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solely through asset creation, establishing it as a substantially more effective and stable self-evolving agent framework.
%U https://aclanthology.org/2026.acl-long.952/
%P 20784-20831
Markdown (Informal)
[Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation](https://aclanthology.org/2026.acl-long.952/) (Cheng et al., ACL 2026)
ACL
- Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, and Yunhong Wang. 2026. Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20784–20831, San Diego, California, United States. Association for Computational Linguistics.