Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance

Guanyu Lin ,

Tao Feng ,

Pengrui Han ,

Ge Liu ,

Jiaxuan You

LLM MQIA NLP RAG PEDS Agent Autonomous Workflows

随着科学研究的增多，研究人员面临着浩瀚文献的导航和阅读的艰巨任务。现有的解决方案，如文档问答，未能有效地提供个性化和最新的信息。我们提出了Paper Copilot，这是一个自我进化、高效的LLM系统，旨在基于思维检索、用户配置文件和高性能优化来协助研究人员。具体而言，Paper Copilot可以提供个性化的研究服务，维护实时更新的数据库。量化评估表明，Paper Copilot在高效部署后可以节省69.92\%的时间。本文详细介绍了Paper Copilot的设计和实现，突出了它对个性化学术支持的贡献以及简化研究过程的潜力。

As scientific research proliferates, researchers face the daunting task of navigating and reading vast amounts of literature. Existing solutions, such as document QA, fail to provide personalized and up-to-date information efficiently. We present Paper Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Paper Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Paper Copilot saves 69.92\% of time after efficient deployment. This paper details the design and implementation of Paper Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process.