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.

Hierarchical text classification (HTC) is a significant but challenging task in natural language processing (NLP) due to its complex taxonomic label hierarchy. Recently, there have been a number of approaches that applied prompt learning to HTC problems, demonstrating impressive efficacy. The majority of prompt-based studies emphasize global hierarchical features by employing graph networks to represent the hierarchical structure as a whole, with limited research on maintaining path consistency within the internal hierarchy of the structure. In this paper, we formulate prompt-based HTC as a named entity recognition (NER) task and introduce conditional random fields (CRF) and Global Pointer to establish hierarchical dependencies. Specifically, we approach single- and multi-path HTC as flat and nested entity recognition tasks and model them using span- and token-based methods. By narrowing the gap between HTC and NER, we maintain the consistency of internal paths within the hierarchical structure through a simple and effective way. Extensive experiments on three public datasets show that our method achieves state-of-the-art (SoTA) performance.