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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.14738 |
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| _version_ | 1866915706393591808 |
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| author | Yan, Zhengxu Li, Han Feng, Yuming |
| author_facet | Yan, Zhengxu Li, Han Feng, Yuming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NoveltyRank: A Retrieval-Augmented Framework for Conceptual Novelty Estimation in AI Research Yan, Zhengxu Li, Han Feng, Yuming Machine Learning Computation and Language 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. |
| title | NoveltyRank: A Retrieval-Augmented Framework for Conceptual Novelty Estimation in AI Research |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2512.14738 |