# Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm arXiv: https://arxiv.org/abs/2512.04405 ยท HTML: https://mlsanity.com/paper/2512.04405/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. - Builds on 32 of its 61 references that are in the map. - First posted on arXiv 4 Dec 2025. ## Placed in - LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/) ## Builds on (most cited first, 30 of 32) - [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024) - [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023) - [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023) - [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024) - [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2505.01074/) (2025) - [ORANSight-2.0: Foundational LLMs for O-RAN](https://mlsanity.com/paper/2503.05200/) (2025) - [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023) - [Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing](https://mlsanity.com/paper/2401.01531/) (2024) - [Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks](https://mlsanity.com/paper/2205.05036/) (2022) - [Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework](https://mlsanity.com/paper/2310.17471/) (2023) - [LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs](https://mlsanity.com/paper/2412.19811/) (2024) - [S-RAN: Semantic-Aware Radio Access Networks](https://mlsanity.com/paper/2407.11161/) (2024) - [LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN](https://mlsanity.com/paper/2504.18062/) (2025) - [Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation](https://mlsanity.com/paper/2312.05746/) (2023) - [Semantic Communication Enhanced by Knowledge Graph Representation Learning](https://mlsanity.com/paper/2407.19338/) (2024) - [Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04672/) (2024) - [A Survey of Secure Semantic Communications](https://mlsanity.com/paper/2501.00842/) (2025) - [O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks](https://mlsanity.com/paper/2503.03523/) (2025) - [Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN](https://mlsanity.com/paper/2503.08353/) (2025) - [AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities](https://mlsanity.com/paper/2211.03979/) (2022) - [Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach](https://mlsanity.com/paper/2412.14119/) (2024) - [Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN](https://mlsanity.com/paper/2507.21696/) (2025) - [Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency](https://mlsanity.com/paper/2408.03806/) (2024) - [Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective](https://mlsanity.com/paper/2408.04188/) (2024) - [REAL: Reinforcement Learning-Enabled xApps for Experimental Closed-Loop Optimization in O-RAN with OSC RIC and srsRAN](https://mlsanity.com/paper/2502.00715/) (2025) - [Learning Wireless Data Knowledge Graph for Green Intelligent Communications: Methodology and Experiments](https://mlsanity.com/paper/2404.10365/) (2024) - [Semantic Vehicle-to-Everything (V2X) Communications Towards 6G](https://mlsanity.com/paper/2407.17186/) (2024) - [Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach](https://mlsanity.com/paper/2409.02728/) (2024) - [Consistent and Repeatable Testing of O-RAN Distributed Unit (O-DU) across Continents](https://mlsanity.com/paper/2410.04416/) (2024) - [Robust Deep Joint Source Channel Coding for Task-Oriented Semantic Communications](https://mlsanity.com/paper/2503.12907/) (2025) ## Built on by (most cited first, 3 of 3) - [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026) - [Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences](https://mlsanity.com/paper/2605.02584/) (2026) - [LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization](https://mlsanity.com/paper/2607.16066/) (2026) ## Abstract (arXiv) The evolution toward sixth-generation wireless systems positions intelligence as a native network capability, fundamentally transforming the design of radio access networks (RANs). Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles. SemCom departs from bit-level fidelity and instead emphasizes task-oriented meaning exchange, enabling compact SC and introducing new performance measures such as semantic fidelity and task success rate. Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration, increasingly supported by foundation models and knowledge graphs. In this work, we first introduce the conceptual foundations of SemCom and agentic networking, and discuss why existing AI-driven O-RAN solutions remain largely bit-centric and task-siloed. We then present a unified taxonomy that organizes recent research along three axes: i) semantic abstraction level (symbol/feature/intent/knowledge), ii) agent autonomy and coordination granularity (single-, multi-, and hierarchical-agent), and iii) RAN control placement across PHY/MAC, near-real-time RIC, and non-real-time RIC. Based on this taxonomy, we systematically introduce enabling technologies including task-oriented semantic encoders/decoders, multi-agent reinforcement learning, foundation-model-assisted RAN agents, and knowledge-graph-based reasoning for cross-layer awareness. Representative 6G use cases, such as immersive XR, vehicular V2X, and industrial digital twins, are analyzed to illustrate the semantic-agentic convergence in practice. Finally, we identify open challenges in semantic representation standardization, scalable trustworthy agent coordination, O-RAN interoperability, and energy-efficient AI deployment, and outline research directions toward operational semantic-agentic AI-RAN.