pith:7FF2VEHD
GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models
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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\usepackage{pith}
\pithnumber{7FF2VEHDKKM6NNOUQG46ZRJSGV}
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Claims
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.
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.
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.
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| First computed | 2026-05-17T23:39:04.459737Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f94baa90e35299e6b5d481b9ecc5323571db0c31de08395faeb49b937850c437
Aliases
· · · · ·Agent API
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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