Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.00753 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910192880320512 |
|---|---|
| author | Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Guo, Jizhou Chen, Yankai Miao, Chunyu Nguyen, Hoang Zhou, Yue Zhang, Weizhi Fang, Liancheng Zhang, Hanrong Wang, Fangxin Zhang, Pengfei Wang, Huacan He, Langzhou Li, Yangning Li, Dongyuan Jiang, Renhe Liu, Xue Yu, Philip S. |
| author_facet | Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Guo, Jizhou Chen, Yankai Miao, Chunyu Nguyen, Hoang Zhou, Yue Zhang, Weizhi Fang, Liancheng Zhang, Hanrong Wang, Fangxin Zhang, Pengfei Wang, Huacan He, Langzhou Li, Yangning Li, Dongyuan Jiang, Renhe Liu, Xue Yu, Philip S. |
| contents | 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 survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment and profiling, human feedback, interaction types, orchestration, and communication, explores emerging applications, and discusses unique challenges and opportunities arising from human-AI collaboration. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-Human-Agent-Collaboration-Interaction-Systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Guo, Jizhou Chen, Yankai Miao, Chunyu Nguyen, Hoang Zhou, Yue Zhang, Weizhi Fang, Liancheng Zhang, Hanrong Wang, Fangxin Zhang, Pengfei Wang, Huacan He, Langzhou Li, Yangning Li, Dongyuan Jiang, Renhe Liu, Xue Yu, Philip S. Computation and Language Machine Learning 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 survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment and profiling, human feedback, interaction types, orchestration, and communication, explores emerging applications, and discusses unique challenges and opportunities arising from human-AI collaboration. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-Human-Agent-Collaboration-Interaction-Systems. |
| title | LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.00753 |