LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
Pith reviewed 2026-05-22 17:17 UTC · model grok-4.3
The pith
This survey organizes LLM-based human-agent systems around five core components to address autonomous agent limitations.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper claims that LLM-based human-agent systems incorporate human-provided information, feedback, or control into the agent system to enhance performance, reliability, and safety. It provides the first comprehensive survey that clarifies fundamental concepts, systematically presents core components 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.
What carries the argument
A five-component taxonomy of environment and profiling, human feedback, interaction types, orchestration, and communication that structures the analysis of how humans and LLM-based agents collaborate.
If this is right
- Humans and agents can collaborate more effectively by using the complementary strengths outlined in the components.
- System reliability increases when human feedback mechanisms are integrated to reduce hallucinations and handle complex tasks.
- Structured orchestration and communication enable safer deployment in real-world applications.
- Identified challenges in ethics and interaction design point to specific directions for future system improvements.
Where Pith is reading between the lines
- The taxonomy could serve as a checklist for developers building new human-agent systems to ensure coverage of key aspects.
- Future surveys might test the framework by attempting to fit post-2024 papers into the same five components.
- Links to broader human-computer interaction research could be explored by mapping existing HCI models onto these components.
- Empirical studies could measure whether systems that explicitly address all five components show measurable gains in user trust.
Load-bearing premise
The selected papers and the five-component taxonomy together give a complete and non-redundant picture of the field without major gaps or selection bias.
What would settle it
A review that identifies multiple major papers on LLM human-agent systems that cannot be placed into any of the five components or that requires an additional core component not listed in the survey.
read the original 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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript surveys LLM-based human-agent collaboration and interaction systems (LLM-HAS). It argues that fully autonomous LLM agents suffer from hallucinations, limited reliability on complex tasks, and safety risks, motivating the incorporation of human feedback and control. The central claim is that this is the first comprehensive structured survey of the area; it organizes the literature around a five-component taxonomy (environment and profiling, human feedback, interaction types, orchestration, and communication), reviews emerging applications, and discusses challenges and opportunities. A GitHub repository with paper lists and resources is provided.
Significance. If the taxonomy proves non-redundant and the literature coverage systematic, the survey could become a useful reference that consolidates an interdisciplinary area and guides future work on human-LLM collaboration. The open GitHub resource list is a concrete strength that aids reproducibility and community follow-up. The significance is reduced, however, by the absence of any documented selection protocol, which leaves the completeness claim open to doubt.
major comments (2)
- [Abstract / Introduction] Abstract and Introduction: the claim to provide the 'first comprehensive and structured survey' is load-bearing for the paper's contribution, yet no literature-search protocol, databases, search terms, date range, or inclusion/exclusion criteria are stated. Without this information the five-component taxonomy cannot be evaluated for gaps or selection bias.
- [Taxonomy sections] Taxonomy presentation (presumably §3–§7): the five components are presented as the core organizing structure, but the manuscript does not explain why these particular dimensions were selected or demonstrate that they are mutually exclusive and collectively exhaustive relative to the broader literature.
minor comments (2)
- The GitHub repository is a helpful resource; consider adding direct citation keys or DOIs in the text so readers can quickly locate the surveyed papers.
- Some figures or tables summarizing the distribution of papers across the five components would improve readability and allow readers to assess coverage at a glance.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed feedback on our survey of LLM-based human-agent collaboration systems. We address each major comment below and outline the revisions we will make to improve transparency and rigor.
read point-by-point responses
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Referee: [Abstract / Introduction] Abstract and Introduction: the claim to provide the 'first comprehensive and structured survey' is load-bearing for the paper's contribution, yet no literature-search protocol, databases, search terms, date range, or inclusion/exclusion criteria are stated. Without this information the five-component taxonomy cannot be evaluated for gaps or selection bias.
Authors: We agree that documenting the literature search protocol is necessary to support the claim of a comprehensive survey and to permit evaluation of the taxonomy. In the revised manuscript we will add a dedicated subsection (likely in the Introduction) that specifies the databases consulted (arXiv, Google Scholar, ACL Anthology, and selected conference proceedings), the search keywords and queries, the date range (primarily 2022–early 2025), and the inclusion/exclusion criteria used. We will also report the approximate number of papers initially retrieved and ultimately included. This addition will make the selection process transparent and allow readers to assess potential gaps or biases. revision: yes
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Referee: [Taxonomy sections] Taxonomy presentation (presumably §3–§7): the five components are presented as the core organizing structure, but the manuscript does not explain why these particular dimensions were selected or demonstrate that they are mutually exclusive and collectively exhaustive relative to the broader literature.
Authors: We acknowledge that the rationale for the five-component taxonomy should be stated explicitly. In the revision we will expand the opening of the taxonomy section to explain how the dimensions were derived from prior human-AI interaction frameworks and adapted to the distinctive characteristics of LLM agents. We will also add a brief analysis (or supplementary table) that maps representative papers from the surveyed literature onto the five components, thereby illustrating their mutual exclusivity and collective coverage. Any remaining gaps will be noted as directions for future work. revision: yes
Circularity Check
Survey paper with no derivations, predictions, or self-referential reductions.
full rationale
The paper is a literature synthesis that organizes LLM-HAS research around a five-component taxonomy (environment and profiling, human feedback, interaction types, orchestration, communication). No equations, fitted parameters, predictions, or derivations appear in the abstract or described structure. The claim of providing the 'first comprehensive and structured survey' rests on external literature consolidation rather than any internal reduction or self-citation chain that forces the result. The taxonomy is presented as an organizational framework for existing work, not derived from quantities defined inside the paper. This qualifies as self-contained against external benchmarks with no circularity.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Fully autonomous LLM-based agents face significant challenges including limited reliability due to hallucinations, difficulty handling complex tasks, and substantial safety and ethical risks.
Forward citations
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discussion (0)
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