2025/12/12 by Yan, Zhengxu, Li, Han, Feng, Yuming
Computer Science · Decision Sciences · Medicine · #Artificial Intelligence in Healthcare and Education #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management
paper · doi:10.48550/arxiv.2512.14738
openalex publication_date 2025/12/12 · openalex created_date 2025/12/19 · openalex updated_date 2026/07/28
The accelerating pace of scientific publication makes it difficult to identify truly original research among incremental work. We propose a framework for estimating the conceptual novelty of research papers by combining semantic representation learning with retrieval-based comparison against prior literature. We model novelty as both a binary classification task (novel vs. non-novel) and a pairwise ranking task (comparative novelty), enabling absolute and relative assessments. Experiments benchmark three model scales, ranging from compact domain-specific encoders to a zero-shot frontier model. Results show that fine-tuned lightweight models outperform larger zero-shot models despite their smaller parameter count, indicating that task-specific supervision matters more than scale for conceptual novelty estimation. We further deploy the best-performing model as an online system for public interaction and real-time novelty scoring.