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GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

Fasheng Miao, Feng Zhang, Fubin Wu, Haozhe Lu, Jiaji Liu, Jialu Li, Luo Jin, Lu Sun, Rui Luo, Xiang Li, Xinran Liu, Xinyi Wang, Yingxian Li, Yuqi Qiu, Yuxin Hu, Zhengze Zhang, Zuyao Xu

Large language models fabricate citations at rates from 14 to 95 percent, and the fraction of papers containing such errors rose 81 percent in 2025.

arxiv:2602.06718 v2 · 2026-02-06 · cs.CR · cs.AI

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Claims

C1strongest claim

ghost citations represent a systemic threat to academic integrity, with all 13 tested LLMs hallucinating citations at rates from 14.23% to 94.93%, 1.07% of 56,381 papers containing invalid citations, and an 80.9% increase in 2025.

C2weakest assumption

The GhostCite framework accurately identifies invalid citations without substantial false positives or negatives, and the sampled AI/ML and Security venues from 2020-2025 represent broader trends in citation validity.

C3one line summary

LLMs hallucinate citations at rates from 14.23% to 94.93%, with 1.07% of papers containing invalid citations and an 80.9% increase in 2025.

References

52 extracted · 52 resolved · 3 Pith anchors

[1] URL: https://www.consul tmu.co.uk/ghost-references-cause-many-gen ai-errors/
[2] URL:https://openrouter.ai
[3] Scrape.do: Which API Is Better? URL: https://scrape.do/compare/scrapingdog-vs-s crapedo/
[4] URL: https://fo rums.zotero.org/discussion/75155/ghost-cit ations 2018
[5] arxiv e-print archive: Computer science subject classes. https://arxiv.org/archive/cs , 2025. Accessed: 2025-09-24 2025

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First computed 2026-05-17T23:39:04.459737Z
Builder pith-number-builder-2026-05-17-v1
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f94baa90e35299e6b5d481b9ecc5323571db0c31de08395faeb49b937850c437

Aliases

arxiv: 2602.06718 · arxiv_version: 2602.06718v2 · doi: 10.48550/arxiv.2602.06718 · pith_short_12: 7FF2VEHDKKM6 · pith_short_16: 7FF2VEHDKKM6NNOU · pith_short_8: 7FF2VEHD
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7FF2VEHDKKM6NNOUQG46ZRJSGV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f94baa90e35299e6b5d481b9ecc5323571db0c31de08395faeb49b937850c437
Canonical record JSON
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