BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation

GPTZero found 53 NeurIPS 2025 accepted papers with hallucinated citations, and venues now screen submissions with automated reference checkers. Rule- and Large Language Model (LLM)-based verifiers are emerging, but many are released without a shared evaluation and each flags a different kind of error, so it is unclear which to trust or when to deploy them. We address this with HALLMARK (Hallucination benchmark): 2,526 BibTeX entries spanning 14 hallucination types, with per-type and diagnostic sub-test labels and further temporal, cross-domain, and real-world evaluation splits. We evaluate zero-shot and tool-augmented LLMs, a DOI-lookup baseline, and our own co-designed rule-based verifier, bibtex-updater. Verifiers can miss hallucinations and wrongly flag correct papers, so every flag still needs human review. The false positive rate (FPR) determines the review effort and thus limits deployment. The tested models exhibit an order-of-magnitude spread in FPR; agentic lookups buy recall but inflate FPR, and neither smaller models nor prompt optimization reach the frontier's operating point. A two-stage cascade with bibtex-updater avoids that inflation, providing an upper bound, given the co-design. These rankings, however, depend on what counts as a hallucination: re-scoring the types that describe a real work cited inaccurately changes which verifier ranks first, and venues have not agreed on a definition. We call for a community definition and release per-type labels and a re-scoring protocol so that any agreed definition can be scored against every verifier.

Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibliographic verification. Existing detectors often reduce verification to binary found/not-found decisions and rely on brittle parsing or incomplete retrieval, offering little field-level signal to auditors. We reframe citation hallucination detection as taxonomy-aligned field-level adjudication and introduce a 12-code taxonomy spanning Real, Potential, and Hallucinated citations. Based on this taxonomy, we build CiteTracer, a cascading multi-agent detector that extracts structured citations from PDF and BibTeX, retrieves evidence through cache lookup, URL fetch, scholar connectors, and web search, applies deterministic field matching, and routes ambiguous cases to class-specialist judgers. We release a benchmark of 2,450 synthetic citations built from real seeds with controlled LLM mutations, paired with 957 real-world fabricated citations drawn from desk-rejected submissions at ICLR 2026 and an anonymous conference. CiteTracer reaches 97.1% accuracy on the synthetic benchmark, with class-level F1 scores of 97.0, 95.8, and 98.5 for Real, Potential, and Hallucinated, respectively, and detects 97.1% of fabrications on the real-world set without abstaining. Code: https://github.com/aaFrostnova/CiteTracer.

Large language models can generate polished scientific text that includes unsupported claims, allowing hallucinations to enter the archival record. Assessing this risk via technical statements is difficult and often requires expert judgment, but citations provide a more auditable surface: a reference either resolves to a real scholarly work with compatible authorship, or it does not. We measure citation hallucination in peer-reviewed proceedings using a conservative definition limited to identity-level failures: non-existent works and substantial author-list mismatches. We explicitly exclude ordinary bibliographic drift (e.g., venue/year differences, publication-status updates, minor name variants). To audit citations at scale, we build RefChecker, a verification pipeline that resolves bibliography entries against multiple bibliographic sources and escalates unresolved cases to web-search re-verification. We apply RefChecker to accepted camera-ready papers from ICLR, ICML, NeurIPS, and USENIX Security. Hallucinated citations have entered the archival record. While reference-level rates are usually below 1%, proceedings are large enough that paper-level failures are visible: in 2025, roughly one in twenty NeurIPS and USENIX Security papers contains at least two likely hallucinated academic-paper-like references under our strict definition. We also observe post-ChatGPT increases in several venues, including a tail of papers with 5+ failures in a single bibliography, and likely hallucinated citations even among award-winning papers. These results suggest peer review alone does not reliably enforce citation integrity, yet auditing is tractable (about 0.04$ per paper in one venue-scale scan). We open-source RefChecker for routine, reproducible citation verification before publication (https://github.com/markrussinovich/refchecker).