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

TL;DR

<3-5 sentence high-level summary> Paper Copilot tackles the problem of researchers needing personalized, up-to-date access to a growing literature corpus. It combines thought-retrieval, real-time Arxiv updating, self-evolution, and high-performance optimizations (feature pre-computation, multi-threading, and caching) to deliver tailored academic assistance. The work demonstrates substantial efficiency gains (69.92% time savings) and positive user feedback (about 75% favor self-evolution), highlighting its potential to streamline literature review and idea generation. Future work aims to broaden sources beyond Arxiv to broaden coverage and usefulness.

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.

Figures (11)

• Figure 1: Comparison of (a) document Question Answering (QA) with our (b) Paper Copilot. Conventional document QA tends to help user understand the content of specific paper while our Paper Copilot can further act like a real research assistant who can provide personalized service based on user profile.
• Figure 2: Architecture of Paper Copilot from bottom-to-up perspective. (a) In personalized service, Paper Copilot provides interactive services including the generation of user research profile, analysis of research trends and ideas, and advisory chatting about research. (b) In large language model, user demand from interaction will be used for retrieving and collecting relevant context, and then LLM will generate answer and make response to user demand. (c) In efficient deployment, feature pre-computation, parallel computation and caching techniques are applied to speed up the retrieval process and guarantee the efficient response.
• Figure 3: Multi-thread engine keeps Paper Copilot service away from waiting for daily updating of papers and self-evolution of thoughts. The daily-update thread and self-evolution thread will achieve thought memory management and asynchronous I/O without disturbing the service thread.
• Figure 4: Flowchart for the interaction of user research profile in Paper Copilot. Users can input his/her name to generate the personalized profile based on historical publication. Besides, if users are unsatisfied with the generated profile or fail to get historical publication, they also can manually edit the profile.
• Figure 5: Diagram for the interaction of research trend and ideas in Paper Copilot. (a) Users can sign up with email to receive the weekly update. (b) Besides, users can also select the time range for getting the daily, weekly or all historical research trend.