@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue
%A Shen, Jiawei
%A Zhu, Jia
%A Guo, Hanghui
%A Shi, Weijie
%A Cui, Yue
%A Niu, Qingyu
%A Ma, Guoqing
%A Liu, Jingjiang
%A Liang, Yidan
%A Wang, Yilin
%A Di, Shimin
%A Xu, Jiajie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shen-etal-2026-acr
%X 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.
%U https://aclanthology.org/2026.findings-acl.155/
%P 3149-3167
Markdown (Informal)
[ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue](https://aclanthology.org/2026.findings-acl.155/) (Shen et al., Findings 2026)
ACL
- Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, and Jiajie Xu. 2026. ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3149–3167, San Diego, California, United States. Association for Computational Linguistics.