Conflict Detection in AI-RAN: Efficient Interaction Learning and Autonomous Graph Reconstruction

2026-01-19

AI Summary

This work addresses the performance degradation and service disruptions in AI-native mobile networks caused by conflicting objectives among multiple agents. To this end, we propose the first conflict detection framework tailored for AI-RAN, which employs a two-tower encoder to learn agent interactions directly from RAN data. A data-driven sparsification mechanism is introduced to automatically reconstruct conflict graphs without requiring manual thresholds or hyperparameter tuning. By eliminating reliance on complex graph neural networks and handcrafted rules, our approach significantly reduces computational overhead while enhancing the accuracy and adaptability of graph construction. This provides an efficient and robust foundation for cooperative intelligence in 6G AI-native networks.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 based on 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 a 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.

Problem

• Conflict Detection
• AI-native RAN
• 6G
• Multi-agent Conflicts
• Conflict Graph

Innovation

• AI-native RAN
• conflict detection
• two-tower encoder
• autonomous graph reconstruction
• sparsity-based learning