用3D可视化追踪AI模型依赖链，自动发现版权合规风险
AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
- 将模型依赖关系映射为3D空间，结合规则引擎实现动态合规检测
- 分析90万+模型发现55.46%存在版权风险，适配器遗漏许可证率达56.67%
- 支持从全局社区到局部路径的多尺度探索，降低审计认知负担
机器学习模型的快速复用使AI生态演变为高度互联的供应链。传统合规工具与静态报告难以应对大规模、多跳依赖网络。为此，我们提出AI Supply Chain Galaxy（AISCG），一个交互式3D可视化分析系统，用于模型来源追溯与合规审计。AISCG将模型布局于3D空间，融合显式结构依赖与基于规则的合规引擎，支持多尺度探索——从全局社区发现到局部路径感知的溯源。通过对Hugging Face上908,449个模型的生态系统级实证分析，我们发现55.46%的模型存在合规风险或元数据冲突/缺失。识别出显著风险模式：适配器衍生中56.67%遗漏许可证，微调中8.05%出现“许可证漂移”。通过对复杂Llama模型族的案例研究，展示AISCG如何帮助分析师直观追踪继承的限制条款，定位深层拓扑网络中的根因，显著降低合规审计的认知负荷。
原文摘要 · Abstract (English)
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.
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