AI for Service: Proactive Assistance with AI Glasses
2025/10/16 by Wen, Zichen, Wang, Yiyu, Liao, Chenfei +10 · 4 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2510.14359
Abstract
In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.
Citations
- Efficient Multi-modal Large Language Models via Progressive Consistency Distillation
- Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving
- Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
- AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence
- TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
- Shifting AI Efficiency From Model-Centric to Data-Centric Compression
- Qwen3 Technical Report
- StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
- Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications
- Kimi-VL Technical Report
- Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
- Qwen2.5-VL Technical Report
- Seamless Integration: The Evolution, Design, and Future Impact of Wearable Technology
- Mirai: A Wearable Proactive AI "Inner-Voice" for Contextual Nudging
- Kimi k1.5: Scaling Reinforcement Learning with LLMs
- Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction
- Agent Workflow Memory
- The Llama 3 Herd of Models
- VideoLLM-online: Online Video Large Language Model for Streaming Video
- A Survey on Self-Evolution of Large Language Models
- The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
- Qwen Technical Report
- Multimodal Foundation Models: From Specialists to General-Purpose Assistants
- GPT-4 Technical Report
- Zone-based Federated Learning for Mobile Sensing Data
- Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals
- A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
- How Far Are We From AGI: Are LLMs All We Need?
Cited by
Related