分析68万次提交，揭示开源模型演化的规律与模式。
How do Machine Learning Models Change?
- 通过挖掘代码仓库，分类超68万次提交，构建演化模型。
- 发现发布行为集中于模型输出、共享与文档更新，而非细粒度修改。
- 热门项目起点更成熟，协作密集项目有独特文档和技术演化路径。
机器学习模型及其开源实现的普及推动了人工智能的研究与应用发展。以Hugging Face为代表的平台构建了动态演化的生态，但缺乏对模型长期变化的大规模研究。本研究基于对10万个模型和2251个版本在Hugging Face上的超过68万次提交进行挖掘，采用扩展的机器学习变更分类体系，并利用贝叶斯网络建模提交与发布活动的时间模式。结果表明，提交活动符合数据科学标准流程（如CRISP-DM），强调迭代优化；而发布则集中于模型输出、共享与文档等重大更新。此外，高人气项目通常具备更成熟的初始状态，基础性提交较少；而协作频繁的项目则展现出独特的文档与技术演化特征。这些发现深化了对社区平台上模型演化机制的理解，为模型维护实践提供了参考。
原文摘要 · Abstract (English)
The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable this evolving ecosystem, yet a large-scale longitudinal study of how these models change is lacking. This study addresses this gap by analyzing over 680,000 commits from 100,000 models and 2,251 releases from 202 of these models on HF using repository mining and longitudinal methods. We apply an extended ML change taxonomy to classify commits and use Bayesian networks to model temporal patterns in commit and release activities. Our findings show that commit activities align with established data science methodologies, such as the Cross-Industry Standard Process for Data Mining (CRISP-DM), emphasizing iterative refinement. Release patterns tend to consolidate significant updates, particularly in model outputs, sharing, and documentation, distinguishing them from granular commits. Furthermore, projects with higher popularity exhibit distinct evolutionary paths, often starting from a more mature baseline with fewer foundational commits in their public history. In contrast, those with intensive collaboration show unique documentation and technical evolution patterns. These insights enhance the understanding of model changes on community platforms and provide valuable guidance for best practices in model maintenance.
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