Span-level Detection of AI-generated Scientific Text via Contrastive Learning and Structural Calibration

• URL: http://arxiv.org/abs/2510.00890v1
• Date: Wed, 01 Oct 2025 13:35:14 GMT
• Title: Span-level Detection of AI-generated Scientific Text via Contrastive Learning and Structural Calibration
• Authors: Zhen Yin, Shenghua Wang,
• Abstract summary: Sci-SpanDet is a structure-aware framework for detecting AI-generated scholarly texts.<n>It combines section-conditioned stylistic modeling with multi-level contrastive learning to capture human nuanced-AI differences.<n>It achieves state-of-the-art performance, with F1(AI) of 80.17, AUROC of 92.63, and Span-F1 of 74.36.
• Score: 2.105564340986074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of large language models (LLMs) in scientific writing raises serious concerns regarding authorship integrity and the reliability of scholarly publications. Existing detection approaches mainly rely on document-level classification or surface-level statistical cues; however, they neglect fine-grained span localization, exhibit weak calibration, and often fail to generalize across disciplines and generators. To address these limitations, we present Sci-SpanDet, a structure-aware framework for detecting AI-generated scholarly texts. The proposed method combines section-conditioned stylistic modeling with multi-level contrastive learning to capture nuanced human-AI differences while mitigating topic dependence, thereby enhancing cross-domain robustness. In addition, it integrates BIO-CRF sequence labeling with pointer-based boundary decoding and confidence calibration to enable precise span-level detection and reliable probability estimates. Extensive experiments on a newly constructed cross-disciplinary dataset of 100,000 annotated samples generated by multiple LLM families (GPT, Qwen, DeepSeek, LLaMA) demonstrate that Sci-SpanDet achieves state-of-the-art performance, with F1(AI) of 80.17, AUROC of 92.63, and Span-F1 of 74.36. Furthermore, it shows strong resilience under adversarial rewriting and maintains balanced accuracy across IMRaD sections and diverse disciplines, substantially surpassing existing baselines. To ensure reproducibility and to foster further research on AI-generated text detection in scholarly documents, the curated dataset and source code will be publicly released upon publication.

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