Ethical constraints on open-weight AI models are both a reflection of societal concerns and a foundation for AI governance policy. They are expected to propagate to downstream derivatives while implemented as voluntary metadata disclosures that must be restated at each generation of reuse. We audit 2,142,823 model repositories on Hugging Face Hub to test whether this disclosure-based governance infrastructure can sustain traceability across deep model lineages. Restriction evidence decays with a half-life of 1.31 derivation steps ($R^2$=0.98), and beyond seven downstream generations at least 80% of descendant models lack sufficient public evidence for a governance determination, a depth boundary we formalize as the governance horizon. Platform-level interventions to restore missing licence metadata reveal that policy design (not enforcement alone) is the binding factor: inheritance-only designs require near-complete enforcement to move the horizon, whereas a mandatory-declaration design that explicitly resolves orphan lineage components shifts the horizon already at moderate enforcement. The structural bottleneck is lineages with no inheritable upstream intent: such orphan components remain undecidable under any inheritance-only policy regardless of enforcement rate, and unresolved upstream nodes additionally create direct downstream undecidability bottlenecks that inheritance rules alone cannot recover. Comparison with PyPI, where governance signals are carried by explicit machine-readable declarations, corroborates that the collapse is topology-specific to open-weight derivation rather than inherent to open ecosystems. These results establish that disclosure-based governance has a shallow, structurally determined reach in open-weight AI, and that achieving deep supply-chain accountability requires provenance mechanisms propagating governance signals through derivation itself.

翻译：开放权重AI模型中的伦理约束既是社会关切的体现，也是AI治理政策的基础。这些约束需通过自愿性元数据披露实现，并在每次复用时重新声明，从而预期传播至下游衍生模型。我们对Hugging Face Hub上的2,142,823个模型仓库进行审计，以检验这种基于披露的治理基础设施能否在深层模型谱系中维持可追溯性。限制性证据以1.31阶推导半衰期衰减（$R^2$=0.98），超过七代下游衍生后，至少80%的后代模型缺乏足够的公开证据支持治理判定——我们将这一深度边界正式定义为“治理视界”。通过平台层面干预恢复缺失许可证元数据发现，政策设计（而非单纯执法）是关键制约因素：仅依赖继承的设计需近乎完全的执法力度才能移动视界，而明确解析孤立谱系组件的强制声明设计，即使在中等执法水平下也能推动视界迁移。结构性瓶颈在于无继承性上游意图的谱系：此类孤立组件在任何仅依赖继承的政策下均无法判定（无论执法力度如何），未解决的上游节点还会直接形成下游不可判定性瓶颈，单靠继承规则无法弥合。与PyPI的对比（其治理信号通过显式机器可读声明传递）印证了这种塌缩是开放权重推导特有的拓扑结构特性，而非开放生态系统的固有属性。这些结果表明，基于披露的治理在开放权重AI中具有浅层结构化触及范围，实现深度供应链问责需要能通过推导过程自身传播治理信号的溯源机制。