Knowledge distillation, model extraction, and behavior transfer have become central concerns in frontier AI. The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it. This paper presents a public, trade-secret-safe theoretical framework for reducing that asymmetry at the architectural level. The core claim is that distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time. To formalize this idea, the paper introduces a constraint-coupled reasoning framework with four elements: bounded transition burden, path-load accumulation, dynamically evolving feasible regions, and a capability-stability coupling condition. The paper is intentionally public-safe: it omits proprietary implementation details, training recipes, thresholds, hidden-state instrumentation, deployment procedures, and confidential system design choices. The contribution is therefore theoretical rather than operational. It offers a falsifiable architectural thesis, a clear threat model, and a set of experimentally testable hypotheses for future work on distillation resistance, alignment, and model governance.

翻译：知识蒸馏、模型提取与行为迁移已成为前沿人工智能领域的核心关注点。其主要风险并非简单的复制行为，而是存在这样一种可能性：有价值的能力可以比最初伴随它的治理结构更廉价地被转移。本文提出了一个公开且不涉及商业机密的抗不对称性理论框架，该框架在架构层面运作。核心论断是：当高级能力与随时间塑造状态转移的内部稳定性约束耦合时，蒸馏作为捷径的价值将降低。为形式化这一思想，本文引入了包含四个要素的约束耦合推理框架：有界转移负担、路径负荷累积、动态演化的可行域，以及能力-稳定性耦合条件。本文刻意保持公共安全性：它省略了专有实现细节、训练方案、阈值、隐藏状态监测手段、部署流程以及机密的系统设计选择。因此，其贡献是理论性的而非操作性的。它为未来关于抗蒸馏、对齐及模型治理的研究工作，提供了一个可证伪的架构性论点、一个清晰威胁模型以及一组可实验检验的假设。