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

Paper · 2026 · Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu et al.

Summary

Fully autonomous LLM agents remain hard to trust in practice. They hallucinate, especially across chained actions. They also falter on long, expertise-heavy tasks and raise safety, bias and accountability concerns in areas such as finance or healthcare. This survey argues for keeping people in the loop and gives what the authors call the first structured overview of LLM-based human-agent systems (LLM-HAS). In these systems, humans supply missing information, critique or correct outputs, or take control when stakes are high.

The authors build a taxonomy with five dimensions: environment and profiling, human feedback, interaction type, orchestration, and communication. They use it to classify existing work. They also compare three ways of building such systems: prompting, supervised fine-tuning and reinforcement learning. Finally, they collect benchmarks across domains and discuss open problems, including variability in human behaviour, agent-centric designs, weak evaluation practices and safety gaps.

The result is a shared vocabulary and reference map for designing and assessing collaborative agents. It is backed by a maintained GitHub list of papers and resources.

Key points

  • Human feedback is grouped along three axes:
    • type: evaluative, corrective, guidance or implicit;
    • granularity: holistic or segment-level;
    • timing: initial, during or post task.
    • Examples cited include Uni-RLHF, PRELUDE, Self-Refine, VeriPlan and AgentA/B.
  • Interaction types cover collaboration (delegation, supervision, coordination, cooperation), competition and coopetition. Orchestration is split into one-by-one vs. simultaneous execution and synchronous vs. asynchronous timing.
  • Communication is characterized by its mode, which can be observation, conversation or a shared message pool, and by its organization, which may be hierarchical, centralized or decentralized. Users are profiled as "lazy" or "informative."
  • Implementation trade-offs differ by approach:
    • SFT methods (PRELUDE, XtraGPT, CollabLLM) yield steadier behaviour at a higher data cost.
    • RL methods (UserRL, MUA-RL, SWEET-RL, ReHAC) handle long horizons but struggle with reward design.
    • Hybrid pipelines combining these approaches are increasingly common.
  • Benchmarks span embodied AI, coding, web navigation, healthcare, retail, travel and finance. Examples include MINT, WebLINX, ColBench, ConvCodeWorld, τ-Bench, τ2-Bench, UserBench and InterruptBench. The repository is Awesome-Human-Agent-Collaboration-Interaction-Systems.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations🎓McGill UniversityMontreal · Canada87 works · 11,027 citations🎓The University of TokyoTokyo · Japan90 works · 9,156 citations🎓University of Illinois ChicagoChicago · United States61 works · 20,107 citations🎓University of California, IrvineIrvine · United States37 works · 36,319 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates124 works · 5,676 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1811

@inproceedings{zou2026llm,
  title = {{LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey}},
  author = {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},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1811},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1811}
}

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