AI注册表中机器学习资产系统综述的框架

中文摘要 AI 辅助

背景：现代软件系统越来越依赖机器学习（ML）资产（即预训练模型、数据集、基准）来构建、评估和集成基于ML的系统。然而，当前对ML资产的探索、选择和重用实践并未得到与传统证据综合方法相当的系统化检索方法论的支持。因此，在实践中，ML资产的选择往往被呈现为一个既定的设计决策，其依据是非正式的论证，而非可追踪、基于证据且可更新的选择过程。目的：本文探讨系统综述方法如何支持ML资产检索。通过这样做，我们旨在使ML资产的选择透明且可复现，基于明确的证据，并最终更适用于其预期用途。方法：我们分析了科学文献中既定的系统综述实践，并将其阶段（即规划、执行和记录）适配到人工智能（AI）注册表中，将ML资产视为一等分析单元。所提出的框架整合了注册表感知的搜索策略、跨注册表模式对齐以及依赖驱动的ML资产探索。结果：我们将ML资产检索概念化为一个系统且可复现的过程，而非临时活动，并提出了一个用于结构化ML资产发现的框架。结论：这项工作展示了系统综述原则如何扩展到科学文献之外，以支持对不断演进的AI注册表进行证据综合。

英文摘要

Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems. However, current exploration, selection and reuse practices of ML assets are not supported by systematic retrieval methodologies comparable to those used in traditional evidence synthesis. Consequently, in practice, ML asset selection is often presented as a settled design decision, supported by informal justification rather than a traceable, evidence-based, and updatable selection process. Aims: This paper explores how systematic review methods can support ML asset retrieval. In doing so, we aim to make their selection transparent and reproducible, grounded in explicit evidence, and ultimately better suited to its intended use. Method: We analyze established systematic review practices from scientific literature and adapt their phases (i.e., planning, conducting, and documenting) to Artificial Intelligence (AI) registries, treating ML assets as first-class units of analysis. The resulting framework integrates registry-aware search strategies, cross-registry schema alignment, and dependency-driven ML asset exploration. Results: We conceptualize ML asset retrieval as a systematic and reproducible process rather than an ad hoc activity, and propose a framework for structured ML asset discovery. \textbf{Conclusions:} This work illustrates how systematic review principles can be extended beyond scientific literature to support evidence synthesis over evolving AI registries.

发表机构

• Universitat Politècnica de Catalunya - BarcelonaTech (UPC)（加泰罗尼亚理工大学（UPC））

机构由 AI 辅助整理，请以论文原文为准。