GCAgent: Enhancing Group Chat Communication through Dialogue Agents System
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915837541089280 |
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| author | Meng, Zijie Xie, Zheyong Ye, Zheyu Lu, Chonggang Liu, Zuozhu Niu, Zihan Hu, Yao Cao, Shaosheng |
| author_facet | Meng, Zijie Xie, Zheyong Ye, Zheyu Lu, Chonggang Liu, Zuozhu Niu, Zihan Hu, Yao Cao, Shaosheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05240 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GCAgent: Enhancing Group Chat Communication through Dialogue Agents System Meng, Zijie Xie, Zheyong Ye, Zheyu Lu, Chonggang Liu, Zuozhu Niu, Zihan Hu, Yao Cao, Shaosheng Artificial Intelligence 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. |
| title | GCAgent: Enhancing Group Chat Communication through Dialogue Agents System |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.05240 |