BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature

• URL: http://arxiv.org/abs/2601.16993v1
• Date: Mon, 12 Jan 2026 16:30:45 GMT
• Title: BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature
• Authors: Peiran Li, Fangzhou Lin, Shuo Xing, Xiang Zheng, Xi Hong, Jiashuo Sun, Zhengzhong Tu, Chaoqun Ni,
• Abstract summary: BibAgent is a scalable, end-to-end agentic framework for automated citation verification.<n>It integrates retrieval, reasoning, and adaptive evidence aggregation, applying strategies for accessible and paywalled sources.<n>Our results demonstrate that BibAgent outperforms state-of-the-art Large Language Model (LLM) baselines in citation verification accuracy and interpretability.
• Score: 21.872874595027824
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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