A Configuration-First Framework for Reproducible, Low-Code Localization

Excerpt

arXiv:2510.25692v4 Announce Type: replace-cross Abstract: As machine learning (ML) increasingly underpins critical applications, credible, comparable, and repeatable experimental results become more important. Everyday workflows should make rigorous experiment specification and controlled execution the default while allowing advanced experimentation when required. In practice, researchers still have to combine tools for configuration, execution, versioning, and evaluation, and repeat common impl