SurveyAgent: A Conversational System for Personalized and Efficient
  Research Survey
Xintao Wang Jiangjie Chen Nianqi Li Lida Chen Xinfeng Yuan Wei Shi Xuyang Ge Rui Xu Yanghua Xiao
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.