Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes

arXiv:2604.26689v4 Announce Type: replace-cross Abstract: Compositional machine-learning (ML) systems assemble runtime behavior from libraries of independently re-trained capability modules. Replacing one module raises a regression-testing question that static dependence analysis cannot answer: which existing compositions stay valid, and at what test cost? We frame capability updates as regression test selection (RTS) and contribute four results. First, a paired cross-version swap protocol isolates the marginal effect of a single module update. Second, on two contact-rich manipulation tasks we

Source: arXiv cs.AI — read the full report at the original publisher.

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