A rxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance (2024)

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 Arxiv Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Arxiv Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Arxiv Copilot saves 69.92% of time after efficient deployment. This paper details the design and implementation of Arxiv Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process. We have deployed Arxiv Copilot at: https://huggingface.co/spaces/ulab-ai/ArxivCopilot.

## introduction as scientific research has proliferated at an unprecedented rate, researchers are now supposed to navigate and interpret vast amounts of published and pre-print papers @xcite . indeed, researchers need to keep up with the latest trend. this involves continuously searching for relevant papers, quickly evaluating which papers for thorough reading, analyzing trending research topics, and reflecting potential ideas. therefore, they should dedicate significant time to following up the latest papers. however, the large volume of papers make it hard for them to locate the related information, resulting in the waste of time. fortunately, based on retrieval-augmented generation (rag) @xcite , llms @xcite can help to extract and summarize useful information from such external papers @xcite . thus, the above background leads us to a crucial question: how can we design a llm system that can assist researchers in obtaining the latest research information from massive papers? to provide intelligent assistance for researchers, existing works have targeted several tasks, such as skimming @xcite , searching @xcite , and reading @xcite . however, these approaches focus either on understanding the content of paper document (as shown in figure 1 (a) ) or improving the ranking of relevant papers. they fall short of acting like a real researcher who can get personalized and up-to-date information on demand. moreover, as researchers read more papers, they become increasingly experienced-a characteristic that current systems fail to replicate through self-evolution. finally, efficiency remains a critical challenge in retrieving and extracting useful information from the vast and continuously growing pool of papers. to address the above challenges, we develop arxiv copilot, a self-evolving and efficient llm system for personalized academic assistance. more specifically, arxiv copilot can provide personalized research service, self-evolve like a human researcher as shown in figure 1 (b) , and make prompt responses. the detailed characteristics of arxiv copilot are as below. more importantly, user comment feedback indicates that arxiv copilot can save researchers at least 20 minutes in obtaining the same amount of information. this demonstrates that arxiv copilot not only provides valuable academic assistance but also saves researchers' time. our evaluations, both quantitative and qualitative, further highlight its superiority in efficiency and user experience. specifically, we reduce 69.92% of time cost after efficient deployment. in summary, this work presents the following contributions: ## arxiv copilot as shown in figure 2 , our proposed arxiv copilot mainly consists of the following four key parts: for the detailed description of them, we will introduce in the subsequent section. ## conclusion and future work to address the challenges posed by the rapid growth of scientific research, we propose arxiv copilot with a personalized, self-evolving, and efficient llm system. it offers tailored research services, maintains a real-time updated database, and employs advanced optimization techniques to enhance performance. evaluations demonstrate its ability to significantly reduce the time researchers spend on literature review while improving accuracy and user experience. by setting a new standard for personalized academic support, arxiv copilot stands as a valuable tool for the scientific community, enhancing the research process. future work will focus on integrating additional sources beyond arxiv to provide a broader research perspective. after entering the question and arxiv copilot will give two answers. specifically, the first answer is with both thought and paper retrieval while the second answer is just with paper retrieval. here the second answer will have two feedback choices for users, one is 'like' and another is 'dislike'. if users click 'like', the first answer will be removed. otherwise the second answer will removed. besides, users can also clean the chat history by clicking "clear" and provide further feedback by clicking "comment".