@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://preview.aclanthology.org/ingest-acl/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."
}