SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World
Jiaqi Zhang, Chen Gao, Liyuan Zhang, Yong Li, Hongzhi Yin

TL;DR
This paper introduces SmartAgent, a novel embodied reasoning framework that incorporates personalized user preferences through a chain-of-thought process, demonstrated on a new dataset for personalized agent tasks.
Contribution
It proposes the Chain-of-User-Thought paradigm and the SmartAgent framework, pioneering personalized embodied agent reasoning in cyber environments.
Findings
SmartAgent effectively interacts with GUI and accesses item pools.
SmartAgent generates explicit user requirements from previous actions.
SmartAgent recommends items aligning with implicit user preferences.
Abstract
Recent advances in embodied agents with multimodal perception and reasoning capabilities based on large vision-language models (LVLMs), excel in autonomously interacting either real or cyber worlds, helping people make intelligent decisions in complex environments. However, the current works are normally optimized by golden action trajectories or ideal task-oriented solutions toward a definitive goal. This paradigm considers limited user-oriented factors, which could be the reason for their performance reduction in a wide range of personal assistant applications. To address this, we propose Chain-of-User-Thought (COUT), a novel embodied reasoning paradigm that takes a chain of thought from basic action thinking to explicit and implicit personalized preference thought to incorporate personalized factors into autonomous agent learning. To target COUT, we introduce SmartAgent, an agent…
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Taxonomy
TopicsData Visualization and Analytics
