GCAgent: Enhancing Group Chat Communication through Dialogue Agents System

• URL: http://arxiv.org/abs/2603.05240v1
• Date: Thu, 05 Mar 2026 14:55:57 GMT
• Title: GCAgent: Enhancing Group Chat Communication through Dialogue Agents System
• Authors: Zijie Meng, Zheyong Xie, Zheyu Ye, Chonggang Lu, Zuozhu Liu, Zihan Niu, Yao Hu, Shaosheng Cao,
• Abstract summary: GCAgent is a large language model (LLMs)-driven system for enhancing group chats communication with both entertainment- and utility-oriented dialogue agents.<n>The system comprises three tightly integrated modules: Agent Builder, Dialogue Manager, and Interface plugins.<n>In real-world deployments over 350 days, it increased message volume by 28.80%, significantly improving group activity and engagement.
• Score: 24.954331957757645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a key form in online social platforms, group chat is a popular space for interest exchange or problem-solving, but its effectiveness is often hindered by inactivity and management challenges. While recent large language models (LLMs) have powered impressive one-to-one conversational agents, their seamlessly integration into multi-participant conversations remains unexplored. To address this gap, we introduce GCAgent, an LLM-driven system for enhancing group chats communication with both entertainment- and utility-oriented dialogue agents. The system comprises three tightly integrated modules: Agent Builder, which customizes agents to align with users' interests; Dialogue Manager, which coordinates dialogue states and manage agent invocations; and Interface Plugins, which reduce interaction barriers by three distinct tools. Through extensive experiment, GCAgent achieved an average score of 4.68 across various criteria and was preferred in 51.04\% of cases compared to its base model. Additionally, in real-world deployments over 350 days, it increased message volume by 28.80\%, significantly improving group activity and engagement. Overall, this work presents a practical blueprint for extending LLM-based dialogue agent from one-party chats to multi-party group scenarios.

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