How can customers detect shortcut learning in an evidence task?

A task can look demanding while rewarding a shortcut. An agent may learn that documents labelled final are trustworthy, that longer packs pass review or that a particular identifier pattern signals a missing approval. These cues can produce plausible outputs without evidence reasoning.

A buyer can request paired generated cases. In one pair, change filenames, document order and formatting while keeping the underlying evidence unchanged. The expected finding should remain stable. In another pair, preserve presentation but change a material relationship, such as whether an approval covers the action performed. The expected finding should change.

Customers can compare both the decision and its cited support. Stable conclusions under cosmetic changes are useful only if the agent also responds to changes that matter. A model that always returns the same answer would otherwise appear robust.

Licensed resellers should keep related variants within a documented evaluation family and prevent close variants from being split casually between training and testing. MirrorFirm could scope these families in a task pack, with the intended invariant or changed condition recorded for evaluators. This makes the pack's reasoning claims more inspectable.