# Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything

arXiv: https://arxiv.org/abs/2607.26602 · HTML: https://mlsanity.com/paper/2607.26602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [Large Language Models for Networking: Workflow, Advances and Challenges](https://mlsanity.com/paper/2404.12901/) (2024)
- [Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge](https://mlsanity.com/paper/1910.05054/) (2019)
- [Resource Allocation Driven by Large Models in Future Semantic-Aware Networks](https://mlsanity.com/paper/2501.14832/) (2025)
- [Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning](https://mlsanity.com/paper/2505.11570/) (2025)

## Abstract (arXiv)

The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications. However, the massive number of connected devices generates diverse and heterogeneous tasks, which pose increasing challenges for dynamic resource scheduling in IoE environments. Using their superior semantic understanding and reasoning capabilities, Large Artificial Intelligence Models (LAIMs) demonstrate significant potential to handle complex scheduling scenarios and improve resource utilization efficiency. This paper investigates a task-oriented LAIM-driven resource scheduling mechanism, which constructs a multidimensional scheduling decision model by integrating task semantics, network states, and constraint conditions. Furthermore, a task-oriented prompt generation method is designed to establish a deep association between task requirements and network state. In the proposed resource allocation scheme, an external evaluation and feedback module is incorporated to conduct real-time feasibility verification and performance evaluation of scheduling strategies, thus enhancing the robustness and adaptability of scheduling. Simulation results demonstrate that the proposed Large Language Model (LLM)-driven network architecture and resource allocation scheme achieve significant improvements in convergence speed, processing latency, and energy consumption, effectively enhancing IoE task responsiveness and resource utilization.