With AI-as-a-Service (AIaaS) now deployed across multiple providers and model tiers, selecting the appropriate model instance at run time is increasingly outside the end user's knowledge and operational control. Accordingly, the 6G service providers are envisioned to play a crucial role in exposing AIaaS in a setting where users submit only an intent while the network helps in the intent-to-model matching (resolution) and execution placement under policy, trust, and Quality of Service (QoS) constraints. The network role becomes to discover candidate execution endpoints and selects a suitable model/anchor under policy and QoS constraints in a process referred here to as AI-paging (by analogy to cellular call paging). In the proposed architecture, AI-paging is a control-plane transaction that resolves an intent into an AI service identity (AISI), a scoped session token (AIST), and an expiring admission lease (COMMIT) that authorizes user-plane steering to a selected AI execution anchor (AEXF) under a QoS binding. AI-Paging enforces two invariants: (i) lease-gated steering (without COMMIT, no steering state is installed) and (ii) make-before-break anchoring to support continuity and reliability of AIaaS services under dynamic network conditions. We prototype AI-Paging using existing control- and user-plane mechanisms (service-based control, QoS flows, and policy-based steering) with no new packet headers, ensuring compatibility with existing 3GPP-based exposure and management architectures, and evaluate transaction latency, relocation interruption, enforcement correctness under lease expiry, and audit-evidence overhead under mobility and failures.

翻译：随着AI即服务（AIaaS）现已部署于多个提供商和模型层级，在运行时选择合适的模型实例日益超出终端用户的知识范围和操作控制能力。因此，预计6G服务提供商将在以下场景中发挥关键作用：用户仅提交意图，而网络在策略、信任和服务质量（QoS）约束下协助完成意图到模型的匹配（解析）及执行放置。网络的角色演变为发现候选执行端点，并在策略和QoS约束下选择合适模型/锚点，这一过程在此类比蜂窝呼叫寻呼，称为AI寻呼（AI-paging）。在所提出的架构中，AI寻呼是一种控制平面事务，它将意图解析为AI服务标识（AISI）、范围化会话令牌（AIST）以及具有时效性的准入租约（COMMIT），该租约授权在QoS绑定下将用户平面流量引导至选定的AI执行锚点（AEXF）。AI寻呼强制执行两个不变性：（i）租约门控的流量引导（若无COMMIT，则不安装任何引导状态）；（ii）先建后断的锚定机制，以支持动态网络条件下AIaaS服务的连续性和可靠性。我们利用现有控制平面和用户平面机制（基于服务的控制、QoS流和基于策略的流量引导）对AI寻呼进行原型实现，无需新增数据包头，确保与现有基于3GPP的暴露和管理架构兼容，并评估了事务延迟、重定位中断、租约到期下的执行正确性，以及移动性和故障下的审计证据开销。