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

• URL: http://arxiv.org/abs/2505.00753v4
• Date: Thu, 26 Jun 2025 12:53:30 GMT
• Title: LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
• Authors: Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Yankai Chen, Chunyu Miao, Hoang Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Xue Liu, Philip S. Yu,
• Abstract summary: Large language models (LLMs) have sparked growing interest in building fully autonomous agents.<n>LLM-HAS incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability and safety.<n>This paper provides the first comprehensive and structured survey of LLM-HAS.
• Score: 34.275920463375684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 & 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.

Related papers

• LLM-Powered AI Agent Systems and Their Applications in Industry [3.103098467546532]
  Large Language Models (LLMs) have reshaped agent systems.<n>Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction.
  arXiv  Detail & Related papers  (2025-05-22T01:52:15Z)
• Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models [75.4890331763196]
  Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems.<n>LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations.
  arXiv  Detail & Related papers  (2025-03-20T22:37:15Z)
• Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems [11.522282769053817]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making.<n>Researchers have begun incorporating LLMs into multi-agent systems to tackle tasks beyond the scope of single-agent setups.<n>This survey serves as a catalyst for further innovation, fostering more robust, scalable, and intelligent multi-agent systems.
  arXiv  Detail & Related papers  (2025-02-20T07:18:34Z)
• Large Language Models for Multi-Robot Systems: A Survey [9.31855372655603]
  Multi-Robot Systems (MRS) pose unique challenges, including coordination, scalability, and real-world adaptability.<n>This survey provides the first comprehensive exploration of Large Language Models (LLMs) integration into MRS.<n>We highlight key applications in diverse domains, such as household robotics, construction, formation control, target tracking, and robot games.
  arXiv  Detail & Related papers  (2025-02-06T06:52:14Z)
• Position: Towards a Responsible LLM-empowered Multi-Agent Systems [22.905804138387854]
  The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation.<n>These advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound, threatening system stability.<n>To address these risks, a human-centered design approach with active dynamic moderation is essential.
  arXiv  Detail & Related papers  (2025-02-03T16:04:30Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Challenges in Human-Agent Communication [55.53932430345333]
  We identify and analyze twelve key communication challenges that these systems pose.<n>These include challenges in conveying information from the agent to the user, challenges in enabling the user to convey information to the agent, and overarching challenges that need to be considered across all human-agent communication.<n>Our findings serve as an urgent call for new design patterns, principles, and guidelines to support transparency and control in these systems.
  arXiv  Detail & Related papers  (2024-11-28T01:21:26Z)
• Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends [64.57762280003618]
  It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks.<n>This paper investigates scenarios involving the autonomous collaboration of future LM agents.
  arXiv  Detail & Related papers  (2024-09-22T14:09:49Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science [65.77763092833348]
  Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that demand careful consideration for safety. This paper conducts a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.