Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance.

翻译：增强现实辅助系统在支持用户完成装配、烹饪等任务方面日益普及。然而，现有系统通常仅能根据用户请求提供被动响应，缺乏对丰富情境信息与用户个性化特征的考量。为突破这一局限，本文提出一种新型增强现实辅助系统Satori，该系统通过对用户状态与环境上下文进行联合建模来实现主动式任务引导。我们的系统将信念-欲望-意图模型与先进的多模态大语言模型相结合，以推断符合情境的指导策略。该设计基于两项涉及十二位专家的形成性研究。一项包含十六名被试的组内研究表明，Satori在无需人工配置或启发式规则的情况下，其性能可与设计者构建的Wizard-of-Oz系统相媲美，从而显著提升了系统的泛化能力与可复用性，为增强现实辅助技术开辟了新的可能性。