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<p>Assessing scientific novelty remains a challenging problem in research evaluation, particularly given the rapid growth of scientific publication output and the increasing automation of scientific writing. While large language models enable increasingly detailed semantic analysis of text, computational approaches to estimating novelty that do not depend on citation metadata, curated vocabularies, or other domain-specific resources remain under development. In this study, we investigate whether novelty-related signals can be identified directly from statistical patterns produced by transformer language models during text processing. Treating novelty as a property of the conceptual content expressed in scientific text, we estimate it through the analysis of article abstracts, which serve as compact representations of scientific contributions. To address this question, we construct a feature-based framework derived from token-level loss distributions and evaluate it across three STEM domains—engineering, neuroscience, and chemistry—using classifiers from different model families. The results indicate that the extracted&#xa0;distributional patterns remain stable across domains and robust under semantic reformulation of the input text. Overall, the findings suggest that transformer-derived statistical patterns provide a domain-agnostic basis for computational novelty estimation. The proposed framework remains applicable in relatively low-data settings and can be implemented using comparatively lightweight models, making it suitable for scalable novelty estimation in scientific evaluation workflows.</p>

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Domain agnostic features for robust novelty assessment of scientific publication

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Anna Kruzenshtern,
  • Viktor Dodonov,
  • Leonid Chechurin

摘要

Assessing scientific novelty remains a challenging problem in research evaluation, particularly given the rapid growth of scientific publication output and the increasing automation of scientific writing. While large language models enable increasingly detailed semantic analysis of text, computational approaches to estimating novelty that do not depend on citation metadata, curated vocabularies, or other domain-specific resources remain under development. In this study, we investigate whether novelty-related signals can be identified directly from statistical patterns produced by transformer language models during text processing. Treating novelty as a property of the conceptual content expressed in scientific text, we estimate it through the analysis of article abstracts, which serve as compact representations of scientific contributions. To address this question, we construct a feature-based framework derived from token-level loss distributions and evaluate it across three STEM domains—engineering, neuroscience, and chemistry—using classifiers from different model families. The results indicate that the extracted distributional patterns remain stable across domains and robust under semantic reformulation of the input text. Overall, the findings suggest that transformer-derived statistical patterns provide a domain-agnostic basis for computational novelty estimation. The proposed framework remains applicable in relatively low-data settings and can be implemented using comparatively lightweight models, making it suitable for scalable novelty estimation in scientific evaluation workflows.