In the rapidly advancing research fields such as AI, managing and staying abreast of the latest scientific literature has become a significant challenge for researchers. Although previous efforts have leveraged AI to assist with literature searches, paper recommendations, and question-answering, a comprehensive support system that addresses the holistic needs of researchers has been lacking. This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. This system stands out by offering a unified platform that supports researchers through various stages of their literature review process, facilitated by a conversational interface that prioritizes user interaction and personalization. Our evaluation demonstrates SurveyAgent’s effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.

If you find this work useful to your research, please kindly cite our paper:

@article{wang2024surveyagent,
  title={SurveyAgent: A Conversational System for Personalized and Efficient Research Survey},
  author={Wang, Xintao and Chen, Jiangjie and Li, Nianqi and Chen, Lida and Yuan, Xinfeng and Shi, Wei and Ge, Xuyang and Xu, Rui and Xiao, Yanghua},
  journal={arXiv preprint arXiv:2404.06364},
  year={2024}
}