AI供应链星系：用于许可证合规的3D可视化分析
AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
AI总结：
提出AI供应链星系（AISCG），一种交互式3D可视化分析系统，通过空间布局和规则引擎对Hugging Face上908,449个模型进行实证分析，发现55.46%的模型存在合规风险，并识别出适配器派生中56.67%的许可证遗漏和微调中8.05%的许可证漂移等风险模式。
摘要：
机器学习模型复用的快速普及已将AI生态系统转变为一个高度互联的供应链。传统的合规工具和静态报告难以应对这些庞大且多跳的依赖网络。为此，我们提出了AI供应链星系（AISCG），一个用于模型溯源和合规审计的交互式3D可视化分析系统。AISCG将模型映射到3D空间布局中，将显式结构依赖与基于规则的合规引擎相结合。它支持多尺度探索，从全局社区检测到局部、路径感知的谱系追踪。我们通过对Hugging Face上908,449个模型的生态系统规模实证分析展示了其有效性。我们的发现揭示了一个令人担忧的现状：55.46%的模型存在合规风险或元数据冲突/缺失。我们还识别出不同的风险模式，包括适配器派生中56.67%的许可证遗漏率和微调中8.05%的“许可证漂移”率。通过对复杂的Llama模型家族进行案例研究，我们展示了AISCG如何帮助分析人员直观地追溯继承的受限条款，并在深层拓扑网络中识别根本原因，从而显著降低合规审计的认知负荷。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
