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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

TL;DR

SurveyAgent delivers a holistic, conversational assistant for literature reviews by integrating Knowledge Management, Recommendation, and Query Answering within a ReAct-based framework. It combines keyword-based search (via arXiv Sanity) with semantic re-ranking from LLMs and a chunk-based long-context QA approach to handle large academic texts. The system includes a structured paper corpus, a defined paper schema, and a HuggingChat-based UI, enabling organized collections, personalized recommendations, and targeted Q&A. Empirical results demonstrate high action-planning accuracy and superior recommendation performance compared to baselines, highlighting the approach's potential to significantly enhance research efficiency and navigation of the growing scientific corpus.

Abstract

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.

SurveyAgent: A Conversational System for Personalized and Efficient Research Survey

TL;DR

SurveyAgent delivers a holistic, conversational assistant for literature reviews by integrating Knowledge Management, Recommendation, and Query Answering within a ReAct-based framework. It combines keyword-based search (via arXiv Sanity) with semantic re-ranking from LLMs and a chunk-based long-context QA approach to handle large academic texts. The system includes a structured paper corpus, a defined paper schema, and a HuggingChat-based UI, enabling organized collections, personalized recommendations, and targeted Q&A. Empirical results demonstrate high action-planning accuracy and superior recommendation performance compared to baselines, highlighting the approach's potential to significantly enhance research efficiency and navigation of the growing scientific corpus.

Abstract

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.
Paper Structure (27 sections, 2 figures, 5 tables)

This paper contains 27 sections, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Language Agents
  4. Paper Querying
  5. Paper Search and Recommendation
  6. Methodology
  7. Agent Framework
  8. Modules and Actions
  9. Knowledge Management Module
  10. Recommendation Module
  11. Query Answering Module
  12. Paper Processing
  13. Paper Sources
  14. Paper Schema
  15. Paper Parsing
  16. ...and 12 more sections

Figures (2)

  • Figure 1: Typical use cases of SurveyAgent in scientific research scenarios, where users interact with the agent through conversations.
  • Figure 2: An overview of SurveyAgent. It adopts the ReAct framework, with key modules and actions displayed on the left. We demonstrate the autonomous workflow of SurveyAgent in assisting researchers with paper recommendation (Case 1) and literature summarization (Case 2).