Networked AI services are increasingly delivered through edge infrastructures to support latency-sensitive applications. Edge scheduling is critical for deciding where and how AI services are executed under limited communication and computing resources. Existing frameworks usually assume that requirements are given as numerical constraints, such as latency bounds, energy budgets, or cost limits. In practice, users often express expectations through ambiguous natural language, creating a gap between user intent and resource constrained scheduling. To bridge this gap, we propose semantic-contract-guided edge potential scheduling (STEPS), a natural language driven scheduling framework for LLM assisted edge AI services. STEPS introduces semantic contracts as executable interfaces between user-side semantics and edge-side decision making. An LLM assisted semantic parser extracts service levels and confidence scores, which are converted into service preferences, fulfillment bounds, and semantic uncertainty. Based on these contracts, STEPS formulates edge scheduling as a contract-guided potential game that jointly determines execution-node selection, computing-resource provisioning, and bandwidth allocation. It also builds feedback signals from semantic request drift, fulfillment drift, fulfillment pressure, and admission pressure to adjust semantic admission, contract conservativeness, and edge coordination. We characterize the exact potential game structure, establish pure strategy Nash equilibrium existence, and prove convergence and stability properties. Experiments show that STEPS improves semantic contract fulfillment, reduces contract guided service loss, and maintains robust adaptation under ambiguous requests and non-stationary edge environments.

翻译：网络化AI服务日益通过边缘基础设施交付以支持延迟敏感型应用。边缘调度对于在有限通信与计算资源下决定AI服务的执行位置与方式至关重要。现有框架通常假设需求以数值约束形式给出，如延迟界限、能量预算或成本限制。实践中，用户常通过模糊的自然语言表达期望，在用户意图与资源约束调度之间形成鸿沟。为弥合这一鸿沟，我们提出语义合约引导的边缘势能调度（STEPS），一种面向LLM辅助边缘AI服务的自然语言驱动调度框架。STEPS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。LLM辅助的语义解析器提取服务等级与置信度分数，进而转化为服务偏好、履约界限与语义不确定性。基于这些合约，STEPS将边缘调度建模为合约引导的势能博弈，联合确定执行节点选择、计算资源配置与带宽分配。同时，它从语义请求漂移、履约漂移、履约压力与准入压力构建反馈信号，以调整语义准入、合约保守性与边缘协调。我们刻画了精确势能博弈结构，建立了纯策略纳什均衡存在性，并证明了收敛性与稳定性。实验表明，STEPS提升了语义合约履约率，降低了合约引导的服务损失，并在模糊请求与非平稳边缘环境下保持了鲁棒自适应能力。