通过代理强化学习在NetGPT中实现通信激励的协作推理
Communications-Incentivized Collaborative Reasoning in NetGPT through Agentic Reinforcement Learning
AI总结：
本文提出NetGPT框架，通过代理强化学习实现AI原生xG网络的协作推理与自适应优化。
摘要：
下一代（xG）无线网络的发展标志着从以连接为中心的架构向集成数据、计算和通信的AI原生设计的范式转变。然而，现有通信系统中的AI部署仍然大多孤立，提供孤立的优化，缺乏内在适应性、动态任务委托和多代理协作。在本工作中，我们提出了一种统一的代理NetGPT框架，用于AI原生的xG网络，其中NetGPT核心可以自主推理或通过代理通信将子任务委托给领域专用的代理。该框架建立了清晰的模块化责任和互操作的工作流，使网络能够实现可扩展的分布式智能。为了支持持续优化协作推理策略，该框架进一步通过部分可观测条件下的代理强化学习和随机外部状态进行增强。训练流程结合了针对外部代理不确定性的掩码损失、熵引导的探索和多目标奖励，共同捕捉任务质量、协调效率和资源约束。通过这一过程，NetGPT学会何时以及如何协作，有效平衡内部推理与代理调用。总体而言，本工作为能够自主感知、推理和行动的自进化、AI原生的xG网络提供了基础架构和训练方法，适用于复杂通信环境。
英文摘要：
The evolution of next-Generation (xG) wireless networks marks a paradigm shift from connectivity-centric architectures to Artificial Intelligence (AI)-native designs that tightly integrate data, computing, and communication. Yet existing AI deployments in communication systems remain largely siloed, offering isolated optimizations without intrinsic adaptability, dynamic task delegation, or multi-agent collaboration. In this work, we propose a unified agentic NetGPT framework for AI-native xG networks, wherein a NetGPT core can either perform autonomous reasoning or delegate sub-tasks to domain-specialized agents via agentic communication. The framework establishes clear modular responsibilities and interoperable workflows, enabling scalable, distributed intelligence across the network. To support continual refinement of collaborative reasoning strategies, the framework is further enhanced through Agentic reinforcement learning under partially observable conditions and stochastic external states. The training pipeline incorporates masked loss against external agent uncertainty, entropy-guided exploration, and multi-objective rewards that jointly capture task quality, coordination efficiency, and resource constraints. Through this process, NetGPT learns when and how to collaborate, effectively balancing internal reasoning with agent invocation. Overall, this work provides a foundational architecture and training methodology for self-evolving, AI-native xG networks capable of autonomous sensing, reasoning, and action in complex communication environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
