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    <abstract>Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-linear flow of human conversation. The prevalent approach of treating dialogue history as a flat, linear sequence is misaligned with the intrinsically hierarchical and branching structure of natural discourse, leading to inefficient context utilization and a loss of coherence during extended interactions involving topic shifts or instruction refinements. To address this limitation, we introduce Context-Agent, a novel framework that models multi-turn dialogue history as a dynamic tree structure. This approach mirrors the inherent non-linearity of conversation, enabling the model to maintain and navigate multiple dialogue branches corresponding to different topics. Furthermore, to facilitate robust evaluation, we introduce the Non-linear Task Multi-turn Dialogue (NTM) benchmark, specifically designed to assess model performance in long-horizon, non-linear scenarios. Our experiments demonstrate that Context-Agent enhances task completion rates and improves token efficiency across various LLMs, underscoring the value of structured context management for complex, dynamic dialogues. The dataset and code is available at GitHub.</abstract>
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%0 Conference Proceedings
%T Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue
%A Hu, Junan
%A Guo, Shudan
%A Liu, Wenqi
%A Yin, Jianhua
%A Wei, Yinwei
%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 hu-etal-2026-context
%X Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-linear flow of human conversation. The prevalent approach of treating dialogue history as a flat, linear sequence is misaligned with the intrinsically hierarchical and branching structure of natural discourse, leading to inefficient context utilization and a loss of coherence during extended interactions involving topic shifts or instruction refinements. To address this limitation, we introduce Context-Agent, a novel framework that models multi-turn dialogue history as a dynamic tree structure. This approach mirrors the inherent non-linearity of conversation, enabling the model to maintain and navigate multiple dialogue branches corresponding to different topics. Furthermore, to facilitate robust evaluation, we introduce the Non-linear Task Multi-turn Dialogue (NTM) benchmark, specifically designed to assess model performance in long-horizon, non-linear scenarios. Our experiments demonstrate that Context-Agent enhances task completion rates and improves token efficiency across various LLMs, underscoring the value of structured context management for complex, dynamic dialogues. The dataset and code is available at GitHub.
%U https://aclanthology.org/2026.findings-acl.1472/
%P 29449-29462

Markdown (Informal)

[Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue](https://aclanthology.org/2026.findings-acl.1472/) (Hu et al., Findings 2026)

• Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue (Hu et al., Findings 2026)

ACL

• Junan Hu, Shudan Guo, Wenqi Liu, Jianhua Yin, and Yinwei Wei. 2026. Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29449–29462, San Diego, California, United States. Association for Computational Linguistics.