对LLM人设设计的系统化：面向AI陪伴应用的四象限技术分类
Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications
AI总结：
本文提出四象限技术分类系统，系统化LLM人设设计，涵盖虚拟与具身、情感与功能增强，分析不同应用场景的技术挑战与核心问题。
摘要：
LLM基于的人设在AI陪伴中的设计和应用是一个迅速扩展但碎片化的领域，从虚拟情感伴侣和游戏NPC到具身的功能机器人。这种在目标、模态和技术栈上的多样性产生了对统一框架的迫切需求。为解决这一差距，本文通过提出面向AI陪伴应用的四象限技术分类系统来系统化该领域。该框架沿两个关键轴线构建：虚拟与具身，以及情感陪伴与功能增强。第一象限（虚拟陪伴）探索虚拟偶像、浪漫伴侣和故事角色，引入一个四层技术框架来分析其在维持长期情感一致性方面的挑战。第二象限（功能性虚拟助手）分析工作、游戏和心理健康中的AI应用，强调从“感受”到“思考和行动”的转变，并指出关键技术如企业RAG和设备端推理。第三和第四象限（具身智能）从虚拟转向现实世界，分析家庭机器人和垂直领域助手，揭示符号 grounding、数据隐私和伦理责任等核心挑战。该分类系统不仅为研究人员和开发者提供了导航复杂人设设计空间的系统地图，也为政策制定者识别和解决不同应用场景中的独特风险提供了基础。
英文摘要：
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 systematizes the field by proposing a Four-Quadrant Technical Taxonomy for AI companion applications. The framework is structured along two critical axes: Virtual vs. Embodied and Emotional Companionship vs. Functional Augmentation. 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. 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. 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 design space but also a basis for policymakers to identify and address the unique risks inherent in different application scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
