arXiv：cs.LG· Alexandra Gonz\'alez, Quim Motger, Xavier Franch, Silverio Mart\'inez-Fern\'andez·· 4 小时前AI 评分25
如何用系统综述方法检索 AI 注册表中的 ML 资产：一个框架
A Framework for the Systematic Review of ML Assets in AI Registries
AI 导读
该论文提出一套将系统综述方法迁移到 AI 注册表的框架，把预训练模型、数据集、benchmark 等 ML 资产视为一等分析单元，覆盖规划、执行与记录三个阶段。框架整合了注册表感知的检索策略、跨注册表 schema 对齐和依赖驱动的资产探索，使 ML 资产选择从非正式的经验判断变为可追溯、可复现的证据过程。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09551 [cs.LG] |
| (or arXiv:2610.09551v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09551 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alexandra González [view email]
[v1]
Wed, 7 Oct 2026 06:50:15 UTC (514 KB)
来源：arXiv：cs.LG · arxiv.org