Artificial Intelligence (AI)-native mobile networks represent a fundamental step toward 6G, where learning, inference, and decision making are embedded into the Radio Access Network (RAN) itself. In such networks, multiple AI agents optimize the network to achieve distinct and often competing objectives. As such, conflicts become inevitable and have the potential to degrade performance, cause instability, and disrupt service. Current approaches for conflict detection rely on conflict graphs created from relationships between AI agents, parameters, and Key Performance Indicators (KPIs). Existing works often rely on complex and computationally expensive Graph Neural Networks (GNNs) and depend on manually chosen thresholds to create conflict graphs. In this work, we present the first systematic framework for conflict detection in AI-native mobile networks, propose an efficient two-tower encoder architecture for learning interactions based on data from the RAN, and introduce a data-driven sparsity-based mechanism for autonomously reconstructing conflict graphs without manual fine-tuning.

翻译：人工智能原生移动网络是迈向6G的基础性步骤，其将学习、推理和决策嵌入无线接入网络本身。在此类网络中，多个AI代理为达成不同且常具竞争性的目标而优化网络。因此，冲突不可避免，并可能降低性能、引发不稳定及中断服务。现有冲突检测方法依赖于基于AI代理、参数与关键性能指标间关系构建的冲突图。当前研究通常采用复杂且计算成本高昂的图神经网络，并依赖人工设定阈值来构建冲突图。本研究首次提出AI原生移动网络中冲突检测的系统性框架，设计了一种基于RAN数据的高效双塔编码器架构以学习交互关系，并引入数据驱动的稀疏性机制实现无需人工调参的自主冲突图重构。