@inproceedings{shen-etal-2026-acr,
    title = "{ACR}: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue",
    author = "Shen, Jiawei  and
      Zhu, Jia  and
      Guo, Hanghui  and
      Shi, Weijie  and
      Cui, Yue  and
      Niu, Qingyu  and
      Ma, Guoqing  and
      Liu, Jingjiang  and
      Liang, Yidan  and
      Wang, Yilin  and
      Di, Shimin  and
      Xu, Jiajie",
    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.155/",
    pages = "3149--3167",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have shown remarkable performance in multi-turn dialogue. However, in multi-turn dialogue, models still struggle to stay aligned with what has been established earlier, follow dependencies across many turns, and avoid drifting into incorrect facts as the interaction grows longer. Existing approaches primarily focus on extending the context window, introducing external memory, or applying context compression, yet these methods still face limitations such as contextual inertia and state drift. To address these challenges, we propose the Adaptive Context Refactoring (ACR) Framework, which dynamically monitors and reshapes the interaction history to mitigate contextual inertia and state drift actively. ACR is built on a library of context refactoring operators and a teacher-guided self-evolving training paradigm that learns when to intervene and how to refactor, thereby decoupling context management from the reasoning process. Extensive experiments on multi-turn dialogue demonstrate that our method significantly outperforms existing baselines while reducing token consumption. Our code is available at https://github.com/ClannadKno/multi-turn."
}