Edge user/service scheduling has become a cornerstone of distributed AI systems, determining where and how AI services are executed under limited communication and computing resources. Existing edge scheduling frameworks usually assume that service requirements are given as numerical constraints, such as latency bounds or energy budgets. In practice, users often express service expectations through ambiguous and context-dependent natural language, creating a gap between user intent and scheduling decisions. To bridge this semantic-to-optimization gap, we propose semantic contract-guided edge potential scheduling (STEPS), a natural language-driven scheduling framework that introduces semantic contracts as executable interfaces between user-side semantics and edge-side decision making. In STEPS, a large language model (LLM)-assisted parser interprets natural language requests and extracts semantic service requirements with confidence scores, which are converted into service requirements and semantic uncertainty. Based on this information, STEPS formulates edge scheduling as a contract-guided potential game that jointly determines execution-node selection, computing-resource provisioning, and bandwidth allocation. STEPS further uses feedback signals to support adaptive scheduling under evolving service and network conditions. We characterize the exact potential game structure, establish the existence of a pure-strategy Nash equilibrium, and prove convergence and stability properties of the scheduling and adaptation processes. Extensive experiments show that STEPS improves semantic contract fulfillment, reduces contract-guided service loss, and maintains robust adaptation under ambiguous natural language requests in non-stationary networked AI environments.

翻译：边缘用户/服务调度已成为分布式AI系统的核心基石，决定了在有限通信和计算资源下AI服务的执行位置与方式。现有边缘调度框架通常假设服务需求以数值约束形式给出（如延迟界限或能耗预算）。在实践中，用户常通过模糊且上下文依赖的自然语言表达服务期望，由此形成用户意图与调度决策之间的鸿沟。为弥合这一语义到优化的差距，我们提出语义合约引导的边缘势能调度（STEPS），这是一种自然语言驱动的调度框架，通过引入语义合约作为用户端语义与边缘端决策之间的可执行接口。在STEPS中，一个基于大语言模型（LLM）的解析器解释自然语言请求并提取带置信度评分的语义服务需求，将其转化为服务需求与语义不确定性。基于此信息，STEPS将边缘调度建模为合约引导的势能博弈模型，联合决策执行节点选择、计算资源分配和带宽分配。STEPS进一步利用反馈信号支持在服务与网络条件动态变化下的自适应调度。我们刻画了精确势能博弈结构，证明了纯策略纳什均衡的存在性，并论证了调度与自适应过程的收敛性和稳定性。大量实验表明，STEPS能够提升语义合约的履约率，降低合约引导的服务损失，并在非平稳网络化AI环境中保持对模糊自然语言请求的鲁棒自适应能力。