Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications
- URL: http://arxiv.org/abs/2511.02979v1
- Date: Tue, 04 Nov 2025 20:37:13 GMT
- Title: Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications
- Authors: Esther Sun, Zichu Wu,
- Abstract summary: This paper proposes a Four-Quadrant Technical taxonomy for AI companion applications.<n>The framework is structured along two critical axes: Virtual vs. Embodied and Emotional Companionship vs. Functional Augmentation.
- Score: 1.9749995192137824
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The design and application of LLM-based personas in AI companionship is a rapidly expanding but fragmented field, spanning from virtual emotional compan- ions 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 applica- tions 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.
Related papers
- Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
arXiv Detail & Related papers (2026-01-13T23:36:30Z) - How LLMs are Shaping the Future of Virtual Reality [2.4150871564195007]
The integration of Large Language Models (LLMs) into Virtual Reality (VR) games marks a paradigm shift in the design of immersive, adaptive, and intelligent digital experiences.<n>This paper examines how these models are transforming narrative generation, non-player character (NPC) interactions, accessibility, personalization, and game mastering.
arXiv Detail & Related papers (2025-08-01T16:08:05Z) - Whom to Respond To? A Transformer-Based Model for Multi-Party Social Robot Interaction [4.276453870301421]
We propose a Transformer-based multi-task learning framework to improve the decision-making process of social robots.<n>We construct a novel multi-party HRI dataset that captures real-world complexities, such as gaze misalignment.<n>Our findings contribute to the development of socially intelligent social robots capable of engaging in natural and context-aware multi-party interactions.
arXiv Detail & Related papers (2025-07-15T03:42:14Z) - Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
arXiv Detail & Related papers (2025-05-13T17:59:20Z) - Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [116.8199519880327]
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI)<n>In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
arXiv Detail & Related papers (2024-07-09T14:14:47Z) - On the Emergence of Symmetrical Reality [51.21203247240322]
We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.
We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
arXiv Detail & Related papers (2024-01-26T16:09:39Z) - Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and
Opportunities [68.03971716740823]
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users.
This survey focuses on the representation and intelligence for the four fundamental system components in ubiquitous Metaverse.
arXiv Detail & Related papers (2023-07-13T11:14:46Z) - Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse.
We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse.
Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
arXiv Detail & Related papers (2022-02-15T03:34:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.