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