# LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach

arXiv: https://arxiv.org/abs/2510.10895 · HTML: https://mlsanity.com/paper/2510.10895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 55 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 28 of 28)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [Hashing for Similarity Search: A Survey](https://mlsanity.com/paper/1408.2927/) (2014)
- [On the Characterization of Local Nash Equilibria in Continuous Games](https://mlsanity.com/paper/1411.2168/) (2014)
- [Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms](https://mlsanity.com/paper/2109.12286/) (2021)
- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches](https://mlsanity.com/paper/2310.09506/) (2023)
- [Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling](https://mlsanity.com/paper/2206.03844/) (2022)
- [Learning Random Access Schemes for Massive Machine-Type Communication with MARL](https://mlsanity.com/paper/2302.07837/) (2023)
- [Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control](https://mlsanity.com/paper/2401.12624/) (2024)
- [CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents](https://mlsanity.com/paper/2503.17850/) (2025)
- [ML Framework for Wireless MAC Protocol Design](https://mlsanity.com/paper/2402.07208/) (2024)
- [LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence](https://mlsanity.com/paper/2503.08123/) (2025)
- [LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols](https://mlsanity.com/paper/2505.16821/) (2025)
- [Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2505.21518/) (2025)
- [Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game](https://mlsanity.com/paper/2506.18126/) (2025)
- [Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications](https://mlsanity.com/paper/2205.10750/) (2022)
- [Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11265/) (2024)
- [Optimizing Wireless Discontinuous Reception via MAC Signaling Learning](https://mlsanity.com/paper/2406.13834/) (2024)
- [Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning](https://mlsanity.com/paper/2408.05884/) (2024)
- [Learning in Conjectural Stackelberg Games](https://mlsanity.com/paper/2501.13686/) (2025)
- [ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols](https://mlsanity.com/paper/2506.07945/) (2025)
- [Resilient-native and Intelligent NextG Systems](https://mlsanity.com/paper/2506.12795/) (2025)
- [WiLLM: an Open Framework for LLM Services over Wireless Systems](https://mlsanity.com/paper/2506.19030/) (2025)

## Abstract (arXiv)

Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-specified network performance, they suffer from poor generalizability and resilience, demanding costly retraining to adapt to dynamic environments. To overcome this limitation, we introduce a game-theoretic LLM-empowered multi-agent DRL (MARL) framework, in which the uplink transmission between a base station and a varying number of user equipments is modeled as a dynamic multi-follower Stackelberg game (MFSG), capturing the network's natural hierarchical structure. Within this game, LLM-driven agents, coordinated through proximal policy optimization (PPO), synthesize adaptive, semantic MAC protocols in response to network dynamics. Protocol action grammar (PAG) is employed to ensure the reliability and efficiency of this process. Under this system, we further analyze the existence and convergence behavior in terms of a Stackelberg equilibrium by studying the learning dynamics of LLM-empowered unified policies in response to changing followers. Simulations corroborate that our framework achieves a 77.6% greater throughput and a 65.2% fairness improvement over conventional baselines. Besides, our framework generalizes excellently to a fluctuating number of users without requiring retraining or architectural changes.