Machine translation post-editing (MTPE) is central to evaluating and ensuring translation quality, particularly for low-resource languages (LRLs), where systems are more error-prone than for high-resource languages. Traditional token-based models segment text according to statistical patterns of their (primarily high-resource) training data, which can distort meaning, fragment words in morphologically rich languages, and complicate MTPE and evaluation. Current evaluation metrics also tend to emphasize surface-level similarity to reference texts, overlooking how humans actually approach translation tasks and creating issues when references are unavailable or a more abstract interpretation is needed. In this position paper, we argue that emerging architectures (Large Concept Models [LCMs] and Byte Latent Transformers [BLTs]) and insights from cognitive science open new possibilities for MTPE frameworks. LCMs represent meaning at the conceptual level, enabling evaluation of different translation approaches and the robustness of such models in MT. At the same time, BLTs operate below the token level, potentially easing post-editing across diverse language scripts. Drawing on cognitive theories of bilingualism and meaning representation, we outline hypotheses and research methods for evaluating post-editing data, translation quality, and interface design toward more robust, human-centered MT evaluation.