Intent → NL Mapping Conceptual Proposal

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.