@inproceedings{zou-etal-2026-llm,
    title = "{LLM}-Based Human-Agent Collaboration and Interaction Systems: A Survey",
    author = "Zou, Henry Peng  and
      Huang, Wei-Chieh  and
      Wu, Yaozu  and
      Guo, Jizhou  and
      Chen, Yankai  and
      Miao, Chunyu  and
      Nguyen, Hoang H  and
      Zhou, Yue  and
      Zhang, Weizhi  and
      Fang, Liancheng  and
      Zhang, Hanrong  and
      Wang, Fangxin  and
      Zhang, Pengfei  and
      He, Langzhou  and
      Li, Yangning  and
      Li, Dongyuan  and
      Jiang, Renhe  and
      Yu, Philip S.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1811/",
    pages = "36335--36364",
    ISBN = "979-8-89176-395-1",
    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 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."
}