ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems
This addresses the issue of high physical and cognitive workload for users by enabling proactive assistance, representing a novel paradigm shift rather than an incremental improvement.
The paper tackles the problem of reactive LLM agents by proposing ProAgent, an end-to-end proactive system that uses sensory contexts and LLM reasoning to provide assistance, achieving up to 33.4% higher proactive prediction accuracy and 16.8% higher tool-calling F1 score over baselines.
Large Language Model (LLM) agents are emerging to transform daily life. However, existing LLM agents primarily follow a reactive paradigm, relying on explicit user instructions to initiate services, which increases both physical and cognitive workload. In this paper, we propose ProAgent, the first end-to-end proactive agent system that harnesses massive sensory contexts and LLM reasoning to deliver proactive assistance. ProAgent first employs a proactive-oriented context extraction approach with on-demand tiered perception to continuously sense the environment and derive hierarchical contexts that incorporate both sensory and persona cues. ProAgent then adopts a context-aware proactive reasoner to map these contexts to user needs and tool calls, providing proactive assistance. We implement ProAgent on Augmented Reality (AR) glasses with an edge server and extensively evaluate it on a real-world testbed, a public dataset, and through a user study. Results show that ProAgent achieves up to 33.4% higher proactive prediction accuracy, 16.8% higher tool-calling F1 score, and notable improvements in user satisfaction over state-of-the-art baselines, marking a significant step toward proactive assistants. A video demonstration of ProAgent is available at https://youtu.be/pRXZuzvrcVs.