The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution
Open accessAbstract
Task-oriented communications are an important element in future intelligent IoT systems. Existing IoT systems, however, are limited in their capacity to handle complex tasks, particularly in their interactions with humans to accomplish these tasks. In this article, we present LLMind, a large language model-based (LLM-based), task-oriented AI agent framework that enables effective collaboration among IoT devices, with humans communicating high-level verbal instructions, to perform complex tasks. Inspired by the functional specialization theory of the brain, our framework integrates an LLM with domain-specific AI modules, enhancing its capabilities. Complex tasks, which may involve collaborations of multiple domain-specific AI modules and IoT devices, are executed through a control script generated by the LLM using a Language-Code transformation approach, which first converts language descriptions to an intermediate finite-state machine (FSM) before final precise transformation to code. Furthermore, the framework incorporates a novel experience accumulation mechanism to enhance response speed and effectiveness, allowing the framework to evolve and become progressively sophisticated through continuing user and machine interactions.
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Cites
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
Song, C. H., Sadler, B. M., Wu, J., Chao, W.-L., Washington, C., & Su, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00280 · Cited by 435 · cited 2×
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Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J., & Lu, H. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01659-w · Cited by 404
Inner Monologue: Embodied Reasoning through Planning with Language Models
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2207.05608 · Cited by 287
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Cited by
Sorted by citations
When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383
“Yu et al. (2024) evaluates the performance of three LLMs, Gemini Pro, GPT-4, and GPT 3.5, on codes with identified vulnerabilities from real-world code reviews.”
InfluentialPlanGenLLMs: A Modern Survey of LLM Planning Capabilities
Wei, H., Zhang, Z., He, S., Xia, T., Pan, S., & Liu, F. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.958 · Cited by 36
Minimizing Response Latency in LLM-Based Agent Systems: A Comprehensive Survey
Park, G., Lee, S., & Park, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3664226 · Cited by 4
Metadata
- Authors
- Cui, H., Du, Y., Yang, Q., Shao, Y., & Liew, S. C.
- Affiliations
- Chinese University of Hong Kong (HK)
- University of Macau (MO)
- Venue
- IEEE Communications Magazine
- Published
- 2025-04
- Volume
- 63
- Issue
- 4
- Pages
- 214-220
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W4402916969
- Cited by
- 75
- References
- 4
- Citations by year
- 2026: 24 · 2025: 42
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- large language models, Internet of Things, task-oriented communication, AI agents, human-machine interaction, finite automata, experience accumulation
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