BibAgent自动检测学术文献中的引用错误，提升科学可信度。
BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature
- 构建智能体框架，融合检索、推理与证据聚合，支持公开与付费文献的引用验证。
- 在6350个跨学科样本上测试，比现有大模型更准确且可解释。
- 提出五类引用错误分类体系，适合科研诚信审查与论文审核人员使用。
引用是科学权威的基础，但普遍存在误引现象，从细微扭曲到伪造参考文献不等。系统性验证目前不可行：人工审核无法应对现代出版量，现有自动化工具受限于仅分析摘要或小规模、特定领域的数据集，部分原因在于全文访问的“付费墙”障碍。我们提出BibAgent，一个可扩展的端到端智能体框架，用于自动化引用验证。BibAgent整合检索、推理与自适应证据聚合，对可获取和受版权保护的来源采用不同策略。对于受版权保护的引用，引入新型证据委员会机制，通过下游引用共识推断引用有效性。为支持系统评估，我们构建了五类误引分类体系，并发布MisciteBench，一个涵盖254个领域、包含6350个误引样本的大规模跨学科基准。结果表明，BibAgent在引用验证准确率与可解释性方面均优于当前最先进的大语言模型基线，实现对科学文献中引用不一致的可扩展、透明检测。
原文摘要 · Abstract (English)
Citations are the bedrock of scientific authority, yet their integrity is compromised by widespread miscitations: ranging from nuanced distortions to fabricated references. Systematic citation verification is currently unfeasible; manual review cannot scale to modern publishing volumes, while existing automated tools are restricted by abstract-only analysis or small-scale, domain-specific datasets in part due to the "paywall barrier" of full-text access. We introduce BibAgent, a scalable, end-to-end agentic framework for automated citation verification. BibAgent integrates retrieval, reasoning, and adaptive evidence aggregation, applying distinct strategies for accessible and paywalled sources. For paywalled references, it leverages a novel Evidence Committee mechanism that infers citation validity via downstream citation consensus. To support systematic evaluation, we contribute a 5-category Miscitation Taxonomy and MisciteBench, a massive cross-disciplinary benchmark comprising 6,350 miscitation samples spanning 254 fields. Our results demonstrate that BibAgent outperforms state-of-the-art Large Language Model (LLM) baselines in citation verification accuracy and interpretability, providing scalable, transparent detection of citation misalignments across the scientific literature.
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