@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
%A Zou, Henry Peng
%A Huang, Wei-Chieh
%A Wu, Yaozu
%A Guo, Jizhou
%A Chen, Yankai
%A Miao, Chunyu
%A Nguyen, Hoang H.
%A Zhou, Yue
%A Zhang, Weizhi
%A Fang, Liancheng
%A Zhang, Hanrong
%A Wang, Fangxin
%A Zhang, Pengfei
%A He, Langzhou
%A Li, Yangning
%A Li, Dongyuan
%A Jiang, Renhe
%A Yu, Philip S.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zou-etal-2026-llm
%X 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.
%U https://aclanthology.org/2026.findings-acl.1811/
%P 36335-36364
Markdown (Informal)
[LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://aclanthology.org/2026.findings-acl.1811/) (Zou et al., Findings 2026)
ACL
- Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, and Philip S. Yu. 2026. LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36335–36364, San Diego, California, United States. Association for Computational Linguistics.