Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.

翻译：先前的研究已证明AI代理作为伴侣的潜力，能够为人类提供持续的情感支持。本文强调个人AI伴侣中自主适应的必要性，这是一个尚未充分探索但前景广阔的方向。这种适应性至关重要，因为它能促进与用户更个性化的互动，并使代理能够根据用户不断变化的需求而演进。然而，赋予代理自主适应能力面临独特挑战，包括确定满足用户期望的最佳适应方案，以及确保适应过程中的平稳过渡。为解决这些问题，我们设计了一个分层框架AutoPal，该框架能够基于用户交互对代理角色进行可控且真实的调整。我们构建了一个角色匹配数据集，以促进最优角色适应策略的学习。大量实验证明了AutoPal的有效性，并凸显了自主适应能力在AI伴侣中的重要性。