Intention-Aware Semantic Agent Communications for AI Glasses

TL;DR

This paper introduces an intention-aware semantic communication framework for AI glasses that reduces bandwidth usage by transmitting only task-relevant semantic content, maintaining performance under resource constraints.

Contribution

It proposes a novel architecture where AI glasses adaptively transmit semantic information based on inferred user intentions, improving efficiency and robustness.

Findings

Achieves over 50% bandwidth reduction depending on the task.

Maintains task performance despite significant bandwidth savings.

Semantic transmission degrades gracefully under low SNR conditions.

Abstract

Smart glasses are emerging as a promising interface between humans and artificial intelligence (AI) agents, enabling first-person perception, contextual awareness, and real-time assistance. However, continuous offloading of visual data from wearable devices to cloud-based vision-language models (VLMs) is fundamentally constrained by limited wireless bandwidth and energy resources. This paper proposes an intention-aware semantic agent communication framework for AI glasses, where data transmission is guided by user intention rather than raw pixel fidelity. In the proposed architecture, AI glasses act as an edge semantic agent while a server-side VLM executes high-level cognition and reasoning. The user intention can be inferred by the server-side VLM through the current transmitted content and the historical prompts. Driven by specific user intentions, the glasses adaptively preserve textual content, document layout, or object semantics before transmission. We evaluate three representative scenarios with different lightweight preprocessing tools on the AI glasses. Simulation results demonstrate that intention-aware preprocessing significantly achieves more than 50% bandwidth reduction depending on the current task while maintaining task performance. Moreover, semantic transmission exhibits graceful degradation under low SNRs. The findings demonstrate that aligning communication resources with user intention is essential for robust and efficient wearable AI agent systems.