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.

翻译：在人工智能等快速发展的研究领域，如何管理和追踪最新科学文献已成为研究者面临的重要挑战。尽管已有研究利用人工智能辅助文献检索、论文推荐及问答功能，但始终缺乏能够全面满足研究者需求的综合性支持系统。本文提出SurveyAgent——一种创新的对话系统，旨在为研究者提供个性化且高效的科研调研辅助。该系统整合三大核心模块：用于整理论文的知识管理模块、用于发现相关文献的推荐模块，以及用于深度内容交互的问答模块。其独特优势在于通过统一平台支持研究者完成文献综述各阶段工作，并借助优先考虑用户交互与个性化需求的对话界面实现这一目标。评估结果表明，SurveyAgent能有效简化科研流程，充分展现其促进研究者与科学文献互动方式的革新能力。