Adapter-only detector training needs tensor, optimizer, and criterion proofs

Adapter-only fine-tuning for an object detector needs three independent proofs: the trainable tensor set, optimizer parameter identity, and loss/target isolation. Freezing the base model proves only that base weights should not update; it does not prove the optimizer excludes them, or that non-target annotations cannot influence the adapter gradient through a shared detection criterion. A fail-closed pilot should construct the real model and optimizer, assert that optimizer IDs equal the allowed adapter A/B tensors, fingerprint all frozen tensors before and after a step, and test criterion invariants (non-target annotations have exactly zero effect, while true negatives retain a nonzero absence loss). This applies to class-specialist adapters over a frozen multi-class detector; it does not establish that such specialization will improve accuracy, which still requires validation-only checkpoint and threshold selection.