AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance

2026-06-15

AI Summary

This work addresses the challenge of tracking multi-hop dependencies and license risks in complex AI model supply chains, where traditional compliance tools fall short. The authors propose the first interactive visual analytics system that integrates 3D spatial layout with path-aware provenance tracing. By leveraging graph layout algorithms, a rule-driven compliance engine, and a multi-scale exploration mechanism, the system enables fine-grained auditing—from global community detection to local path tracing. Empirical evaluation on 908,449 Hugging Face models reveals that 55.46% exhibit compliance risks, including a 56.67% license omission rate in adapter-derived models and an 8.05% license drift rate in fine-tuned models. These findings demonstrate the system’s effectiveness in identifying and attributing license conflicts, omissions, and drifts at scale.

Abstract

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.

Problem

• AI supply chain
• license compliance
• model provenance
• dependency networks
• compliance auditing

Innovation

• 3D visual analytics
• model provenance
• license compliance
• dependency network
• interactive visualization