Citation networks are critical in modern science, and predicting which previous papers (candidates) will a new paper (query) cite is a critical problem. However, the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of LLMs with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges. First, in practice, a new paper may select its citations from gigantic existing papers, where the texts exceed the context length of LLMs. Second, logical relationships between papers are implicit, and directly prompting an LLM to predict citations may result in surface-level textual similarities rather than the deeper logical reasoning. In this paper, we introduce the novel concept of core citation, which identifies the critical references that go beyond superficial mentions. Thereby, we elevate the citation prediction task from a simple binary classification to distinguishing core citations from both superficial citations and non-citations. To address this, we propose $\textbf{HLM-Cite}$, a $\textbf{H}$ybrid $\textbf{L}$anguage $\textbf{M}$odel workflow for citation prediction, which combines embedding and generative LMs. We design a curriculum finetune procedure to adapt a pretrained text embedding model to coarsely retrieve high-likelihood core citations from vast candidates and then design an LLM agentic workflow to rank the retrieved papers through one-shot reasoning, revealing the implicit relationships among papers. With the pipeline, we can scale the candidate sets to 100K papers. We evaluate HLM-Cite across 19 scientific fields, demonstrating a 17.6% performance improvement comparing SOTA methods.
Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering
Multi-Persona Debate System for Automated Scientific Hypothesis Generation
Department of Materials Science & Engineering, Ajou University（材料科学与工程系，阿乔大学）；Department of Energy Systems Research, Ajou University（能源系统研究系，阿乔大学）；Department of Nuclear Science and Engineering, Massachusetts Institute of Technology（核科学与工程系，麻省理工学院）；Department of Materials Science and Engineering, Massachusetts Institute of Technology（材料科学与工程系，麻省理工学院）；Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology（材料科学与工程系，乌山国家科学与技术研究院）；Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology（人工智能研究生院，乌山国家科学与技术研究院）
LitCurate: A Configuration-Driven AI-Assisted Framework for Scientific Database Construction with an Application to Lower-Mantle Equation-of-State Data
Assessing the impact of Open Research Information Infrastructures using NLP driven full-text Scientometrics: A case study of the LXCat open-access platform
DA-IICT, Dhirubhai Ambani University（达吉巴伊·安巴尼大学）；Smart Energy Learning Center, DAU（达吉巴伊·安巴尼大学智能能源学习中心）
