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AI for Service: Proactive Assistance with AI Glasses

Zichen Wen, Yiyu Wang, Chenfei Liao, Boxue Yang, Junxian Li, Weifeng Liu, Haocong He, Bolong Feng, Xuyang Liu, Yuanhuiyi Lyu, Xu Zheng, Xuming Hu, Linfeng Zhang

arXiv:2510.14359v1cs.AIcs.CLcs.CV

TL;DR

Existing AI services largely wait for explicit user commands, while AI4Service aims to anticipate needs and intervene proactively. Alpha-Service is a five-component framework implemented as an AI-glasses multi-agent system; case studies demonstrate proactive service, with computational and energy demands remaining a challenge.

  • Problem

    Most existing AI service models require explicit user commands, limiting the proposed shift toward seamless assistance in daily life.

    Source: §1 · p. 2

  • Method

    Alpha-Service is a von Neumann-inspired, five-component framework, implemented as an AI-glasses multi-agent system for proactive assistance.

    Source: §1 · pp. 2–3

  • Results

    The AI-glasses system proactively identifies service opportunities and offers solutions without human intervention, with case studies spanning gaming, museum guidance, and shopping support.

    Source: §1 · p. 3, §7 · p. 20

  • Takeaways & Limitations

    The case studies illustrate Alpha-Service’s ability to perceive user intent and anticipate needs across gaming, museum guidance, and shopping support.

    Source: §7 · p. 20

    Real-time multimodal inference and continuous video analysis impose high computing and energy demands on resource-constrained AI glasses.

    Source: §6 · p. 20

Abstract

from arXiv · show

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.

1 Introduction

The paper frames AI4Service as a move beyond reactive, command-driven services toward assistance that anticipates needs. It proposes Alpha-Service as a five-unit framework and introduces an AI-glasses implementation that proactively offers Blackjack advice.

1 Introduction

  • The paper situates AI4Service amid advances in models and wearable hardware that make real-time human-AI interaction feasible.Examples of hardware include AI speakers, headphones, and glasses.Source: §1 · p. 2
  • Existing AI services largely wait for explicit commands, while AI4Service aims to serve users proactively across daily life.The proposal emphasizes generalization, proactivity, and customization.Source: §1 · p. 2
  • Alpha-Service is a von Neumann-inspired framework with five components for perception, scheduling, tool use, memory, and user-facing output.Its units include Input, CPU, ALU, Memory, and Output.Source: §1 · p. 2
  • The AI-glasses system proactively identifies service opportunities and offers Blackjack advice without requiring human intervention.The example advice is whether a player should request another card.Source: §1 · p. 3

2 Concept of AI for Service

AI4Service combines timely service triggering with generalized and personalized assistance. Its two layers address when to intervene and how to tailor the service to the event and user.

2.1 Definition and Key Layers

  • AI4Service aims to respond proactively, timely, and personally by anticipating needs from users’ context, behavior, and long-term preferences.Its two layers are “Know When” and “Know How.”Source: §2.1 · p. 3

2.2 Know When: Event Prediction and Timing

  • “Know When” requires continuous analysis of real-time streams to identify meaningful state changes and classify the new event quickly.Different event types, such as taking a call or stepping away, call for different service responses; timing should avoid both delay and unnecessary interruptions.Source: §2.2 · p. 3

2.3 Know How: Generalized and Personalized Services

  • Generalized services use the event type and short-term context to offer standardized options without considering personal history.For example, a traveler arriving at an unfamiliar location might receive general information about it.Source: §2.3 · p. 3
  • Personalized services use long-term context, repeated behavior, preferences, and habits to provide customized assistance that anticipates individual needs.At Cinque Terre, an example draws on travel and food interests to suggest nearby restaurants and wine routes.Source: §2.3 · pp. 3–4
  • The paper presents combining “Know When” with a progression from generalized to personalized service as the basis for a seamless, user-centric service ecosystem.Source: §2.3 · p. 4

3 Architecture

Alpha-Service organizes perception, dispatch, computation, memory, and delivery in a von Neumann-inspired modular system. Its architecture combines continuous visual monitoring, task orchestration, optional web search, long-term context retrieval, and spoken feedback.

3.1 Overview: Von Neumann-Inspired Design

  • Alpha-Service uses five modular units—Input, Central Processing, Arithmetic Logic, Memory, and Output—with the CPU coordinating data and control.The design supports both reactive and proactive assistance.Source: §3.1 · p. 4

3.2 Input Unit: Trigger and Streaming MLLMs

  • A lightweight Qwen2.5-VL-3B trigger model continuously monitors first-person video for assistance cues, activating a Qwen2.5-VL-7B model for deeper scene analysis.This hierarchical setup is intended to sustain efficient perception while providing detailed analysis on demand.Source: §3.2 · pp. 4–5

3.3 Central Processing Unit: Task Orchestration and Synthesis

  • The Qwen3-8B-based CPU decomposes requests into executable subtasks, routes them to suitable units, and integrates their outputs into a coherent response.It can combine visual descriptions, search results, and retrieved history.Source: §3.3 · p. 5

3.4 Arithmetic Logic Unit: Tools Integration

  • The ALU uses web search when internal knowledge is insufficient, returning concise, real-time information for scenarios such as museum visits, machinery support, and staff training.The system parses top-ranked links, summaries, and snippets before delivering results.Source: §3.4 · pp. 5–6

3.5 Memory Unit: Long-Term Context Storage

  • The Memory Unit stores interaction history and context so the agent can retrieve relevant past queries, actions, and preferences across tasks and sessions.Records are written asynchronously and relevant entries are injected into the prompt for a new task.Source: §3.5 · p. 6

3.6 Output Unit: Human-Friendly Synthesis

  • The Output Unit uses synthesized speech to deliver real-time responses in hands-free settings where visual suggestions may be missed.Before text-to-speech, the agent condenses reasoning into concise, actionable instructions.Source: §3.6 · p. 6

4 Case Study

This section presents case studies of Alpha-Service across Blackjack, museum-tour, and clothing-shopping scenarios. The examples show it detecting service opportunities or game-state changes, analyzing context, and delivering timely advice, explanations, and recommendations.

4.1 Case i : Blackjack playing guide

  • The scene shows face-up 4, 2, and 6 of spades totaling 12; the system recommends hitting, citing a controlled 31% bust chance with face cards/10s and high improvement probability with cards 2–9.Source: §4.1 · p. 8, { Arithmetic Logic Unit – Tools… · p. 13
  • The Trigger Model identified four game-state transitions at 8s, 13s, 21s, and 27s, prompting detailed scene analysis that supported timely assistance.The Streaming Model described the cards and hand values at trigger points, while the CPU adapted its service from hand-improvement advice to post-game learning recommendations.Source: §4.1 · pp. 8, 11

{ Arithmetic Logic Unit – Tools Integration

  • The Output Unit transformed complex analytical outputs into clear, structured Blackjack recommendations, delivering the advice in visually accessible and auditorily engaging formats while maintaining technical precision for effective decision support.At 6 points, the optimal move was HIT; at 15 points, the decision was conditional: hit against dealer 2–6 and stand against 7–Ace. The game ended in a bust at 25 points.Source: { Arithmetic Logic Unit – Tools… · pp. 13–14

4.2 Case ii: Guided tour explanation in museum

  • The museum-tour figure caption describes Alpha-Service proactively searching the image of an exhibition artifact online and then explaining the Four-Ram Square Zun.The caption identifies it as a bronze ritual wine vessel from the Shang Dynasty and gives 15s as the example timing.Source: Figure 4 · p. 14
  • At 15s, the trigger model identified four museum-assistance moments and routed them for deeper analysis, while the streaming model described artifacts and visitor context.The moments included pausing at an exhibit, focusing the camera on details, or approaching a label; the CPU’s task-distribution component coordinated both prompts.Source: §4.2 · pp. 14–15
  • The system proposed Guided Tour Explanation to deepen visitors’ understanding, then generated a structured account of the Four Rams Square Zun’s history, culture, and technology.Its account identified a Shang-dynasty bronze ritual vessel and described its form, craftsmanship, use, and symbolism.Source: §4.2 · pp. 15–16

{ Arithmetic Logic Unit – Tools Integration

  • The ALU sought external knowledge and synthesized it with the visual context and proposed service into a recommendation describing the artifact’s ritual use, symbolism, and craftsmanship.The recommendation identified it as the Four-Ram Square Zun, a Shang-dynasty bronze vessel used in sacrificial ceremonies.Source: { Arithmetic Logic Unit – Tools… · p. 16

4.3 Case iii: Fit advisor in market

  • At 18s, the trigger model detected close-up handling of a dark long-sleeved garment, and the streaming model described its ribbed details and soft fabric.The case reports no logged false positives or misses.Source: §4.3 · p. 17

{ Arithmetic Logic Unit – Tools Integration

  • The fit advice covered garment quality, sizing checks, styling, and purchase considerations, while the final recommendation advised confirming measurements and care details before buying.Suggestions included pairing the garment with jeans and a casual suit jacket and considering it for autumn and winter if the size and quality were suitable.Source: §4.3 · pp. 17–18, { Arithmetic Logic Unit – Tools… · p. 18

5 Related Works

Related work spans proactive streaming-video interaction, coordinated agents and tool calling, and human-centered wearable AI. Alpha-Service is presented as bringing these strands together in AI glasses.

5.1 Proactive Interaction in Streaming Video

  • EgoLife (Yang et al., 2025b) provides an egocentric-video evaluation framework, while VideoLLM-Online introduced streaming EOS prediction; current approaches mainly describe moments rather than recommend proactive services.Dispider (Qian et al., 2025) responds to adjacent-frame changes, and StreamBridge (Wang et al., 2025) uses trigger models to time responses.Source: §5.1 · p. 19

5.2 Multi-Agent Systems and MCP-Based Tool Calling

  • Multi-agent systems coordinate specialized agents, and MCP-based tool calling structures how agents access external tools; Alpha-Service assigns tool orchestration to its CPU and tool use to its ALU.The related-work discussion frames combining these approaches as a basis for more robust and versatile service systems.Source: §5.2 · p. 19

5.3 Human-Centric AI in Wearables

  • Wearable AI research is shifting toward adaptive, personalized systems that account for privacy, autonomy, and cognitive load; this work instantiates proactive, context-aware assistance with AI glasses.The discussion also identifies on-device federated learning, context-aware inference, and human-in-the-loop feedback as relevant approaches.Source: §5.3 · p. 19

6 Challenges

Alpha-Service’s move from concept to real-world use faces constraints in edge-device efficiency, generalization and personalization, deployment robustness, privacy, and user trust.

Computational and Energy Constraints

  • Real-time multimodal inference and continuous video analysis on resource-constrained AI glasses make low-latency, energy-efficient service a commercial implementation bottleneck.The framework must reconcile latency and energy demands on edge devices.Source: §6 · p. 20

Generalization vs. Personalization Trade-off

  • General strategies may be insufficiently targeted, while excessive personalization risks information cocoons or overfitting; cold-start personalization with little user data remains a challenge.The design must balance generalized and personalized services.Source: §6 · p. 20

Scalability and Robustness in Real-World Settings

  • Although case studies perform well, robustness and scalability remain challenged in settings with extreme lighting, noise, or multiple users.Stable module collaboration, error recovery, and cross-scenario service migration also remain open issues.Source: §6 · p. 20

Privacy and Data Security

  • Continuous egocentric-video perception collects and processes substantial personal data, and localized storage does not eliminate privacy concerns from long-term behavior recording and preference learning.The paper identifies localization and anonymization alongside personalization as important to user trust.Source: §6 · p. 20

User Adaptation and Trust Building

  • Some users may be uncomfortable with proactive intervention or develop dependency, so future systems should support explainable decisions, user feedback, corrections, and service disabling.These interaction controls are proposed as ways to build trust and promote human-AI collaboration.Source: §6 · p. 20

7 Conclusion

The paper introduces AI4Service and Alpha-Service as a framework for proactive assistance, then demonstrates the concept through AI-glasses case studies. It positions the work as an initial step and identifies personalization, tool expansion, and large-scale user studies as future work.

Contribution

  • Alpha-Service organizes five components inspired by von Neumann architecture to address when to act and how to assist effectively.The conclusion describes the framework as a systematic foundation for proactive assistants.Source: §7 · p. 20

Case Studies

  • A multi-agent implementation on AI glasses demonstrates assistance across gaming, museum guidance, and shopping case studies without explicit commands.The cases illustrate perception of user intent, anticipation of needs, and timely assistance.Source: §7 · p. 20

Future Work

  • Future work will strengthen Memory Unit personalization, broaden ALU tools, and use large-scale studies to assess long-term effects in everyday contexts.The paper frames the work as a step toward more symbiotic human-AI interaction.Source: §7 · p. 20

A Prompts for deciding whether using a web search or not.

The supplied prompt fragments specify a decision-mechanism prompt and a tool-use instruction that includes a web-search response format.

Prompt

Tool-use prompt

B Prompts for action instruction generation.

The Output Unit prompt turns detailed reasoning into a concise action recommendation. It asks for one direct sentence.

B Prompts for action instruction generation.

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