发表机构
Sharif University of Technology（谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究System One模型在无线决策中的应用，通过Jev实现，在保持决策质量的同时大幅降低响应时延，揭示质量与时延的权衡，并定位为时延敏感无线控制的决策接口。
AI 中文摘要
许多无线控制任务需要在严格的时延和可靠性约束下，从有限可行集中重复选择单个动作。尽管大型语言模型（LLMs）最近已成为通用决策引擎，但其自回归生成机制与这类有界控制问题并不自然契合。本文研究System One模型，该模型直接学习在明确定义的决策空间上的概率分布，作为无线决策的一种轻量级替代方案。我们形式化了其决策结构和学习目标，确定了其在物理层控制、无线资源管理、移动性、网络切片和网络运营中的适用性，并通过代表性无线案例研究评估了其实际行为。使用Jev作为System One的实现，我们将其决策质量和客户端观察到的时延与生成式LLMs及传统基线进行了基准比较。在接收天线选择中，与所评估的LLMs相比，Jev将中位响应时延降低了高达8.5倍，尽管这一增益以决策质量相对于更强的任务特定替代方案的损失为代价。更值得注意的是，在意图条件下的RAN切片中，Jev在实现与所评估LLMs相当的效用的同时，提供了超过3.5倍的中位响应时延降低。来自边缘服务编排的补充证据进一步表明，更快的决策并不一定转化为更低的端到端服务时延。这些结果揭示了质量/时延之间的基本权衡，并将System One模型定位为数值优化的替代品，而非替代品，而是时延敏感、有界和意图驱动的无线控制的一种有前景的决策接口。
英文摘要
Many wireless control tasks require repeated selection of a single action from a finite feasible set under stringent latency and reliability constraints. While large language models (LLMs) have recently emerged as general-purpose decision engines, their autoregressive generation mechanism is not naturally aligned with such bounded control problems. This paper investigates System-One models, which directly learn probability distributions over explicitly defined decision spaces, as a lightweight alternative for wireless decision-making. We formalize their decision structure and learning objective, identify their applicability across physical-layer control, radio resource management, mobility, network slicing, and network operations, and evaluate their practical behavior through representative wireless case studies. Using Jev as a System-One implementation, we benchmark decision quality and client-observed latency against generative LLMs and conventional baselines. In receive-antenna selection, Jev delivers up to an 8.5x reduction in median response latency relative to the evaluated LLMs, although this gain comes with a loss in decision quality compared with stronger task-specific alternatives. More notably, in intent-conditioned RAN slicing, Jev achieves utility comparable to the evaluated LLMs while providing more than a 3.5x reduction in median response latency. Complementary evidence from edge-service orchestration further shows that faster decisions do not necessarily translate into lower end-to-end service latency. These results expose a fundamental quality/latency tradeoff and position System-One models not as replacements for numerical optimization, but as a promising decision interface for latency-sensitive, bounded, and intent-driven wireless control.
