The rapid proliferation of machine learning model reuse has transformed the AI ecosystem into a highly interconnected supply chain. Traditional compliance tools and static reports struggle to navigate these massive, multi-hop dependency networks. To address this, we present AI Supply Chain Galaxy (AISCG), an interactive 3D visual analytics system for model provenance and compliance auditing. AISCG maps models into a 3D spatial layout, integrating explicit structural dependencies with a rule-based compliance engine. It supports multi-scale exploration, from global community detection to localized, path-aware lineage tracing. We demonstrate its efficacy through an ecosystem-scale empirical analysis of 908,449 models from Hugging Face. Our findings reveal a concerning landscape: 55.46% of models exhibit compliance risks or metadata conflicts/omissions. We also identified distinct risk patterns, including a 56.67% license omission rate in adapter derivations and an 8.05% "license drift" rate in fine-tuning. Through a case study on the complex Llama model family, we show how AISCG empowers analysts to intuitively trace inherited restrictive terms and identify root causes across deep topological networks, significantly reducing the cognitive load of compliance auditing.

翻译：机器学习模型复用技术的急剧扩展，已将AI生态系统转变为高度互联的供应链体系。传统合规工具与静态报告在处理这些大规模、多跳依赖网络时举步维艰。为此，我们提出AI供应链星系（AISCG）——一种面向模型溯源与合规审计的交互式3D可视化分析系统。AISCG将模型映射至三维空间布局，融合显式结构依赖关系与基于规则的合规引擎。该系统支持多尺度探索，涵盖全局社区检测至局部路径感知的谱系追踪。通过对Hugging Face平台908,449个模型进行生态级实证分析，我们揭示了令人担忧的现状：55.46%的模型存在合规风险或元数据冲突/缺失。同时识别出典型风险模式，包括适配器衍生模型中56.67%的许可证遗漏率，以及微调过程中8.05%的"许可证漂移"率。通过针对复杂Llama模型家族的案例研究，我们展示了AISCG如何帮助分析人员直观追溯继承性限制条款，并在深层拓扑网络中定位根本原因，从而显著降低合规审计的认知负荷。