这一节就是可直接放入论文正文的 Problem Statement（问题定义）小节。

它的目标只有一个：把“协议层缺失”从一种直觉判断，形式化为 ITU 可以讨论、可以立项的“标准级问题”。

我同样给你 中英文对照，语气严格、边界清晰。

Problem Statement｜问题定义

中文版（正式正文可用）

随着人工智能系统在通信网络、云计算平台及关键基础设施中的广泛部署，当前技术体系正逐步暴露出一类并非源于模型性能本身的结构性问题。这些问题集中体现在系统之间的互操作性不足、行为结果难以审计以及跨组织治理能力有限等方面。

现有 AI 系统的设计重心，主要位于模型结构、训练方法与参数规模等实现层面。不同组织和平台在这些层面上形成了高度异构的技术路径，而系统之间的交互，往往依赖隐式语义假设或私有接口完成。这种架构在单一系统内部可以维持运作，但在跨平台、跨组织乃至跨司法辖区的部署场景中，逐渐显现出不可扩展性与不可验证性。

具体而言，当前体系面临以下结构性缺口：

1. 缺乏最小一致的语义状态定义 不同 AI 系统对输入、输出与中间状态的语义解释缺乏统一的最小公共集合，导致系统间行为难以对齐。
2. 缺乏可审计的交互边界 系统输出通常无法明确映射到可验证的协议状态，使得跨系统责任归属、行为回溯与合规审计难以实现。
3. 缺乏跨实现的治理锚点 现有治理机制多依附于具体模型或平台，难以在多实现并存的环境中形成稳定、可继承的治理结构。

上述问题并非通过改进单一模型或算法即可解决，而是反映出在模型实现之上、应用逻辑之下，尚缺乏一个可被标准化的协议层，用以明确系统交互的最小语义状态、边界条件与治理约束。

因此，本文所提出的问题可表述为：在多种 AI 实现路径长期并存的前提下，是否有必要引入一个协议级框架，以支持人工智能系统在全球范围内实现可互操作、可审计、可治理的规模化部署？

这一问题具有明显的跨组织与跨域属性，构成了一个尚未被现有标准体系系统性覆盖的技术与治理空白。

English Version (Problem Statement)

As artificial intelligence systems are increasingly deployed across communication networks, cloud platforms, and critical infrastructure, a class of structural challenges is emerging that cannot be attributed solely to model performance. These challenges primarily manifest as limitations in interoperability, auditability of system behavior, and cross-organizational governance.

Current AI system design largely focuses on implementation-level concerns, including model architectures, training methodologies, and parameter scaling. As a result, heterogeneous technical approaches have emerged across organizations and platforms. Interactions between systems are often mediated through implicit semantic assumptions or proprietary interfaces. While such arrangements may function within isolated environments, they reveal significant limitations when applied to cross-platform, cross-organizational, or cross-jurisdictional deployments.

Specifically, the existing landscape exhibits three interrelated gaps:

1. Absence of minimal shared semantic state definitions There is no commonly agreed minimal semantic representation for inputs, outputs, or intermediate states across AI systems, making behavioral alignment difficult.
2. Lack of auditable interaction boundaries System outputs cannot be consistently mapped to verifiable protocol states, limiting traceability, accountability, and compliance auditing across systems.
3. Lack of governance anchors across implementations Governance mechanisms are typically bound to specific models or platforms, preventing stable and transferable governance structures in heterogeneous environments.

These challenges cannot be resolved by improving individual models or algorithms. Rather, they indicate the absence of a standardizable protocol layer situated above model implementations and below application-specific logic—one that defines minimal shared semantic states, interaction boundaries, and governance constraints.

Accordingly, the problem addressed in this paper can be stated as follows: Given the long-term coexistence of diverse AI implementation paths, is a protocol-level framework required to enable interoperable, auditable, and governable deployment of AI systems at global scale?

This question spans organizational, technical, and jurisdictional boundaries, and represents a gap not yet systematically addressed by existing standards frameworks.

这一小节在 ITU 评审眼中的意义（内部判断）

• 它把问题锁定在“协议层”，而非模型或应用
• 它给出了 可拆分、可研究、可推进的子问题结构
• 它明确暗示： “这是一个需要标准组织介入的问题，而不是技术竞赛。”