SEMTRA: Global Semantic Transition and Rough-Set Rules for Auditable Post-Hoc Explainability

Deep learning architectures generate highly effective but difficult-to-audit latent representations, creating a practical gap between predictive performance and verifiable explanations. Existing post hoc techniques often produce fragmented local attributions rather than dataset-level rulebooks. In this work, we propose Global SEMantic TRAnsition (SEMTRA), a post hoc framework that maps frozen representation features into semantic attributes, discretizes those attributes, and induces rough-set production rules with explicit coverage, conflict, fidelity, and abstention reporting. Evaluated on the Animals with Attributes 2 (AwA2) Protocol A, the semantic transition achieved a Mean Absolute Error (MAE) of 0.1029±0.0005. The extracted rulebook covered 84.80% of test instances, yielding a covered accuracy of 39.73% and a covered fidelity to the base predictor of 40.48%. Under the Protocol B split, continuous semantic-prototype transfer reached an unseen-object accuracy of 44.02%±1.22% as a semantic-transfer validation. Cross-domain validations using SUN and Derm7pt demonstrated that the audit protocol is portable yet strongly dataset-dependent. In the controlled synthetic benchmark, SEMTRA achieved a macro-F1 score of 0.879 at zero semantic noise and degraded to 0.838 at the highest evaluated noise level. Ultimately, SEMTRA serves as a transparent audit layer to expose the verifiable logical subset of a model, rather than replacing the underlying predictor.