CORE:Toward Ubiquitous 6G Intelligence Through Collaborative Orchestration of Large Language Model Agents Over Hierarchical Edge

• URL: http://arxiv.org/abs/2601.21822v1
• Date: Thu, 29 Jan 2026 15:08:19 GMT
• Title: CORE:Toward Ubiquitous 6G Intelligence Through Collaborative Orchestration of Large Language Model Agents Over Hierarchical Edge
• Authors: Zitong Yu, Boquan Sun, Yang Li, Zheyan Qu, Xing Zhang,
• Abstract summary: Collaborative Orchestration Role at Edge (CORE) is a collaborative learning system in which multiple large language models (LLMs) are distributed across mobile devices and tiered edge servers.<n>The system integrates real-time perception,dynamic role orchestration, and pipeline-parallel execution, to facilitate efficient and rapid collaboration among distributed agents.
• Score: 28.17507879390089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid advancements in sixth-generation (6G) networks and large language models (LLMs) have paved the way for ubiquitous intelligence, wherein seamless connectivity and distributed artificial intelligence (AI) have revolutionized various aspects of our lives.However, realizing this vision faces significant challenges owing to the fragmented and heterogeneous computing resources across hierarchical networks, which are insufficient for individual LLM agents to perform complex reasoning tasks.To address this issue, we propose Collaborative Orchestration Role at Edge (CORE), an innovative framework that employs a collaborative learning system in which multiple LLMs, each assigned a distinct functional role, are distributed across mobile devices and tiered edge servers. The system integrates three optimization modules, encompassing real-time perception,dynamic role orchestration, and pipeline-parallel execution, to facilitate efficient and rapid collaboration among distributed agents. Furthermore, we introduce a novel role affinity scheduling algorithm for dynamically orchestrating LLM role assignments across the hierarchical edge infrastructure, intelligently matching computational demands with available dispersed resources.Finally, comprehensive case studies and performance evaluations across various 6G application scenarios demonstrated the efficacy of CORE, revealing significant enhancements in the system efficiency and task completion rates. Building on these promising outcomes, we further validated the practical applicability of CORE by deploying it on a real-world edge-computing platform,that exhibits robust performance in operational environments.

Related papers

• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
  Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
  arXiv  Detail & Related papers  (2025-09-27T10:53:48Z)
• LLM Enabled Multi-Agent System for 6G Networks: Framework and Method of Dual-Loop Edge-Terminal Collaboration [30.5296965737426]
  The framework and method of the LLM-enabled multi-agent system with dual-loop terminal-edge collaborations are proposed in 6G networks.<n>The improved task planning capability and task execution efficiency are validated through the conducted case study in 6G-supported urban safety governance.
  arXiv  Detail & Related papers  (2025-09-05T10:40:31Z)
• Application of LLM Guided Reinforcement Learning in Formation Control with Collision Avoidance [1.1718316049475228]
  Multi-Agent Systems (MAS) excel at accomplishing complex objectives through the collaborative efforts of individual agents.<n>In this paper, we introduce a novel framework that aims to overcome the challenge of designing an effective reward function.<n>By giving large language models (LLMs) on the prioritization of tasks, our framework generates reward functions that can be dynamically adjusted online.
  arXiv  Detail & Related papers  (2025-07-22T09:26:00Z)
• Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
  We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
  arXiv  Detail & Related papers  (2025-05-29T07:24:37Z)
• Multi-Agent Collaboration via Evolving Orchestration [55.574417128944226]
  Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
  arXiv  Detail & Related papers  (2025-05-26T07:02:17Z)
• An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning [13.3347292702828]
  This paper proposes a framework called Autonomous Reinforcement Coordination (ARC) for a SemCom-enabled SAGIN.<n>ARC decomposes orchestration into two tiers, utilizing LLMs for high-level planning and RL agents for low-level decision-making.
  arXiv  Detail & Related papers  (2025-02-22T11:53:34Z)
• C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation [13.120930059424975]
  C-3PO is a proxy-centric framework that facilitates communication between retrievers and large language models.<n>Our framework implements three specialized agents that collaboratively optimize the entire RAG pipeline.
  arXiv  Detail & Related papers  (2025-02-10T07:04:32Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration [44.40722828581203]
  We propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. We first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel L'evy flight search.
  arXiv  Detail & Related papers  (2020-05-21T20:04:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.