# Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence arXiv: https://arxiv.org/abs/2307.02757 ยท HTML: https://mlsanity.com/paper/2307.02757/ ## Key facts - Cited by 26 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 11 of its 14 references that are in the map. - First posted on arXiv 6 Jul 2023. ## 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, 11 of 11) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023) - [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023) - [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022) - [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://mlsanity.com/paper/2305.10435/) (2023) ## Built on by (most cited first, 26 of 26) - [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023) - [A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application](https://mlsanity.com/paper/2412.17481/) (2024) - [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024) - [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024) - [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024) - [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023) - [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024) - [NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services](https://mlsanity.com/paper/2307.06148/) (2023) - [Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models](https://mlsanity.com/paper/2407.12532/) (2024) - [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024) - [Large Language Models Playing Mixed Strategy Nash Equilibrium Games](https://mlsanity.com/paper/2406.10574/) (2024) - [SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation](https://mlsanity.com/paper/2405.16072/) (2024) - [A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks](https://mlsanity.com/paper/2510.19973/) (2025) - [NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models](https://mlsanity.com/paper/2412.10107/) (2024) - [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025) - [Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management](https://mlsanity.com/paper/2510.03310/) (2025) - [Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations](https://mlsanity.com/paper/2510.13982/) (2025) - [AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks](https://mlsanity.com/paper/2511.17506/) (2025) - [Transformers for Green Semantic Communication: Less Energy, More Semantics](https://mlsanity.com/paper/2310.07592/) (2023) - [Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence](https://mlsanity.com/paper/2401.02668/) (2024) - [MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication](https://mlsanity.com/paper/2406.16962/) (2024) - [Large Language Models for Knowledge-Free Network Management: Feasibility Study and Opportunities](https://mlsanity.com/paper/2410.17259/) (2024) - [Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds](https://mlsanity.com/paper/2506.09335/) (2025) - [Decision-Theoretic Safety Assessment of Persona-Driven Multi-Agent Systems in O-RAN](https://mlsanity.com/paper/2604.09682/) (2026) - [Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management](https://mlsanity.com/paper/2605.03215/) (2026) - [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026) ## Abstract (arXiv) The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireless generations, harnessing the power of collective intelligence and paving the way for self-governed networks where intelligent decision-making happens right at the edge. This article puts the stepping-stone for incorporating multi-agent generative artificial intelligence (AI) in wireless networks, and sets the scene for realizing on-device LLMs, where multi-agent LLMs are collaboratively planning and solving tasks to achieve a number of network goals. We further investigate the profound limitations of cloud-based LLMs, and explore multi-agent LLMs from a game theoretic perspective, where agents collaboratively solve tasks in competitive environments. Moreover, we establish the underpinnings for the architecture design of wireless multi-agent generative AI systems at the network level and the agent level, and we identify the wireless technologies that are envisioned to play a key role in enabling on-device LLM. To demonstrate the promising potentials of wireless multi-agent generative AI networks, we highlight the benefits that can be achieved when implementing wireless generative agents in intent-based networking, and we provide a case study to showcase how on-device LLMs can contribute to solving network intents in a collaborative fashion. We finally shed lights on potential challenges and sketch a research roadmap towards realizing the vision of wireless collective intelligence.