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

TL;DR

Paper Copilot is a self-evolving, efficient large language model system that provides personalized, real-time academic assistance, significantly reducing researchers' time spent on literature navigation and reading.

Contribution

It introduces a novel self-evolving LLM system that combines thought-retrieval, user profiling, and optimization for personalized, up-to-date academic support.

Findings

Saves 69.92% of research time after deployment.

Provides personalized research services with real-time database updates.

Demonstrates effective design and implementation for academic assistance.

Abstract

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.