(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Large-scale multilingual language models have enabled remarkable zero-shot cross-lingual transfer, yet parameter-efficient adaptation mechanisms such as prompt tuning remain constrained by their reliance on fixed prompt positions. The syntactic diversity, morphological richness, and divergent word order patterns across languages challenge the assumption that a single prompt placement, typically a prefix, can serve all target languages effectively. This paper presents a systems-oriented investigation of adaptive prompt position optimization for cross-lingual prompt transfer. We propose a conceptual architecture in which a lightweight position controller learns to determine the most favorable insertion point for soft prompt embeddings conditioned on language identity, typological features, and task semantics. The work examines structural trade-offs involved in designing the controller, the coordination between a shared multilingual backbone and dynamic prompt injection, and the implications for large-scale deployment. Beyond architectural considerations, we explore governance dimensions such as fairness across resource-disparate languages, robustness to syntactic perturbation, and the alignment with sustainable and equitable multilingual AI policies. By framing prompt positioning as a control problem within a socio-technical infrastructure, the paper provides a forward-looking perspective on how adaptive mechanisms can reconcile computational efficiency with linguistic inclusivity in multilingual natural language processing systems.