  With the increasing usage of machine-learning in high-energy physics
analyses, the publication of the trained models in a reusable form has become a
crucial question for analysis preservation and reuse. The complexity of these
models creates practical issues for both reporting them accurately and for
ensuring the stability of their behaviours in different environments and over
extended timescales. In this note we discuss the current state of affairs,
highlighting specific practical issues and focusing on the most promising
technical and strategic approaches to ensure trustworthy analysis-preservation.
This material originated from discussions in the LHC Reinterpretation Forum and
the 2023 PhysTeV workshop at Les Houches.
