Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications

The design and application of LLM-based personas in AI companionship is a rapidly expanding but fragmented field, spanning from virtual emotional companions and game NPCs to embodied functional robots. This diversity in objectives, modality, and technical stacks creates an urgent need for a unified framework. To address this gap, this paper \textbf{systematizes} the field by proposing a \textbf{Four-Quadrant Technical Taxonomy} for AI companion applications. The framework is structured along two critical axes: \textbf{Virtual vs. Embodied} and \textbf{Emotional Companionship vs. Functional Augmentation}. \textbf{Quadrant I (Virtual Companionship)} explores virtual idols, romantic companions, and story characters, introducing a four-layer technical framework to analyze their challenges in maintaining long-term emotional consistency. \textbf{Quadrant II (Functional Virtual Assistants)} analyzes AI applications in work, gaming, and mental health, highlighting the shift from "feeling" to "thinking and acting" and pinpointing key technologies like enterprise RAG and on-device inference. \textbf{Quadrants III \& IV (Embodied Intelligence)} shift from the virtual to the physical world, analyzing home robots and vertical-domain assistants, revealing core challenges in symbol grounding, data privacy, and ethical liability. This taxonomy provides not only a systematic map for researchers and developers to navigate the complex persona \textbf{design space} but also a basis for policymakers to identify and address the unique risks inherent in different application scenarios.