Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue

2026-04-07

AI Summary

This work addresses the challenge of maintaining coherence in large language models during nonlinear, hierarchical, and multi-branch human conversations, where inefficient context utilization often leads to degraded performance. To this end, the authors propose Context-Agent, a dynamic discourse tree framework that models multi-turn dialogues as an expandable tree structure, aligning with the inherent nonlinearity of natural conversation and enabling effective context maintenance and navigation across topical branches. Additionally, they introduce NTM, the first benchmark specifically designed for evaluating nonlinear long-range dialogue systems, thereby moving beyond conventional linear modeling paradigms. Experimental results demonstrate that the proposed approach significantly improves task completion rates and token efficiency across multiple large language models, substantiating the effectiveness of structured context management in complex, dynamic conversational settings.

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.

Problem

• non-linear dialogue
• discourse structure
• context management
• dialogue coherence
• large language models

Innovation

• dynamic discourse tree
• non-linear dialogue
• context management
• multi-turn dialogue
• structured conversation modeling