[2505.00753] LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

Computer Science > Computation and Language arXiv:2505.00753 (cs) [Submitted on 1 May 2025 (v1), last revised 6 May 2026 (this version, v5)] Title:LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey Authors:Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Huacan Wang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Xue Liu, Philip S. Yu View a PDF of the paper titled LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey, by Henry Peng Zou and 19 other authors View PDF HTML (experimental) Abstract:Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliability due to hallucinations, difficulty in handling complex tasks, and substantial safety and ethical risks, all of which limit their feasibility and trustworthiness in real-world applications. To overcome these limitations, LLM-based human-agent systems (LLM-HAS) incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. These human-agent collaboration systems enable humans and LLM-based agents to collaborate effectively by leveraging their complementary strengths. This paper provides the first comprehensive and structured ...