@inproceedings{yang-etal-2026-towards-self,
    title = "Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement",
    author = "Yang, Cheng  and
      Yang, Xuemeng  and
      Wen, Licheng  and
      Fu, Daocheng  and
      Mei, Jianbiao  and
      Wu, Rong  and
      Cai, Pinlong  and
      Shen, Yufan  and
      Deng, Nianchen  and
      Xu, Jia  and
      Shi, Botian  and
      Qiao, Yu  and
      Li, Haifeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1522/",
    pages = "30424--30451",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly outperforming previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE{'}s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials."
}