With the rapid expansion of the Internet of Things (IoT) and heterogeneous wireless networks, the Age of Information (AoI) has emerged as a critical metric for evaluating the performance of real-time and personalized systems. While AoI-based random access is essential for next-generation applications such as the low-altitude economy and indoor service robots, existing strategies, ranging from rule-based protocols to learning-based methods, face critical challenges, including idealized model assumptions, slow convergence, and poor generalization. In this article, we propose Reflex-Core, a novel Large Language Model (LLM) agent-based framework for AoI-driven random access in heterogeneous networks. By devising an "Observe-Reflect-Decide-Execute" closed-loop mechanism, this framework integrates Supervised Fine-Tuning (SFT) and Proximal Policy Optimization (PPO) to enable optimal, autonomous access control. Based on the Reflex-Core framework, we develop a Reflexive Multiple Access (RMA) protocol and a priority-based RMA variant for intelligent access control under different heterogeneous network settings. Experimental results demonstrate that in the investigated scenarios, the RMA protocol achieves up to a 14.9% reduction in average AoI compared with existing baselines, while the priority-based version improves the convergence rate by approximately 20%.

翻译：随着物联网（IoT）和异构无线网络的快速扩张，信息年龄（AoI）已成为评估实时与个性化系统性能的关键指标。尽管基于AoI的随机接入对于低空经济、室内服务机器人等下一代应用至关重要，但现有策略——从基于规则的协议到基于学习的方法——均面临关键挑战，包括理想化的模型假设、收敛速度缓慢以及泛化能力不足。本文提出Reflex-Core，一种基于大型语言模型（LLM）智能体的新型异构网络AoI驱动随机接入框架。该框架通过设计“观察-反思-决策-执行”闭环机制，结合监督微调（SFT）与近端策略优化（PPO），实现最优的自主接入控制。基于Reflex-Core框架，我们开发了反射式多址接入（RMA）协议及其基于优先级的变体，以适应不同异构网络场景下的智能接入控制需求。实验结果表明，在所研究的场景中，RMA协议相较于现有基线方法可实现平均AoI最高降低14.9%，而基于优先级的版本将收敛速度提升了约20%。