The official repository of our paper "SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World". We will release code and data upon paper notification.
Find SmartAgent in Awesome-LLM-Reasoning 
We formulate COUT to achieve embodied personalized agent training in terms of three stages of thought. In Thought #1, according to a user's instruction, an agent performs GUI actions to search for an item pool. In Thought #2 with seeing the pool, the agent reasons underlying requirements behind the original instruction, as implied by the previous actions. In Thought #3, based on the underlying thought, the agent recommends items within the pool to complete the user's instruction.
The first environment supports full-stage embodied personalized evaluation.
The capabilities of SmartAgent from basic embodied operations to personalized reasoning.
Two-stage training paradigm of SmartAgent.
Please consider citing our paper and staring this repo if you find SmartAgent helpful in your work, thanks!
@article{zhang2024smartagent,
title={SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World},
author={Zhang, Jiaqi and Gao, Chen and Zhang, Liyuan and Li, Yong and Yin, Hongzhi},
journal={arXiv preprint arXiv:2412.07472},
year={2024}
}