LLM Agent for Hyper-Parameter Optimization
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters optimization approaches for Warm-Start Particles Swarm Optimization with Crossover and Mutation (WS-PSO-CM) algorithm, designed for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication, are primarily heuristic-based, exhibiting low levels of automation and improvable performance. In this paper, we design an Large Language Model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and Model Context Protocol (MCP) are applied. In particular, the LLM agent is first set up via a profile, which specifies the boundary of hyper-parameters, task objective, terminal condition, conservative or aggressive strategy of optimizing hyper-parameters, and LLM configurations. Then, the LLM agent iteratively invokes WS-PSO-CM algorithm for exploration. Finally, the LLM agent exits the loop based on the terminal condition and returns an optimized set of hyperparameters. Our experiment results show that the minimal sum-rate achieved by hyper-parameters generated via our LLM agent is significantly higher than those by both human heuristics and random generation methods. This indicates that an LLM agent with PSO and WS-PSO-CM algorithm knowledge is useful in seeking high-performance hyper-parameters.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 23 references that are in the map. First posted on arXiv 18 Jun 2025.
Builds on
5 of 23 references.
References · 5
- 2019Radio Map Based 3D Path Planning for Cellular-Connected UAVcited by 24
- 2024Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settingscited by 6
- 2023Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstaclescited by 5
- 2025Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edgecited by 4
- 2023Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communicationscited by 1
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- 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial
- 2025HybridRAG-based LLM Agents for Low-Carbon Optimization in Low-Altitude Economy Networks
- 2025Skypilot: Fine-Tuning LLM with Physical Grounding for AAV Coverage Search ↗
- 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach
- 2026LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging ↗
- 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
- 2026Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
- 2025RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms