**Intent → NL Mapping Conceptual Proposal** presents a *purely theoretical* framework for mapping human Intent (WHAT/WHY) into a stabilized semantic layer called **Normalized Language (NL)**. The proposal introduces **IntentOS**, a conceptual input-side system that structures and normalizes Intent before it is provided to a language model. The work explicitly avoids any algorithmic or implementable mechanisms and does **not** modify, influence, or bypass internal LLM inference pathways. Instead, it focuses on reducing ambiguity inherent in natural language—polysemy, implicit assumptions, contextual drift—and proposes NL as a low-context semantic space for representing Intent with greater stability and reproducibility. Key ideas include: * Separation of Intent (WHAT/WHY) from HOW-generation processes * Use of NL as a normalized semantic coordinate space, independent of any natural-language form * Conceptual decomposition of Intent into structural semantic units * Removal of pre-inference processes such as implicit completion or intent guessing in natural-language inputs * Safety-oriented design: IntentOS functions only as an external input-structuring layer, not as an LLM modification This paper is positioned strictly as a **research-oriented conceptual model**, highlighting possibilities for future semantic stabilization frameworks while intentionally avoiding system-level implementation or automation details.