The 17% Gap: Quantifying Epistemic Decay in AI-Assisted Survey Papers

Background: The adoption of Large Language Models (LLMs) in scientific writing promises efficiency but risks introducing informational entropy. While “hallucinated papers” are a known artifact, the systematic degradation of valid citation chains remains unquantified. Methodology: We conducted a forensic audit of 50 recent survey papers in Artificial Intelligence (N = 5,514 citations) published between September 2024 and January 2026. We utilized a hybrid verification pipeline combining DOI resolution, Crossref metadata analysis, Semantic Scholar queries, and fuzzy text matching to distinguish between formatting errors (“Sloppiness”) and verifiable non-existence (“Phantoms”). Results: We detect a persistent 17.0% Phantom Rate—citations that cannot be resolved to any digital object despite aggressive forensic recovery. Diagnostic categorization reveals three distinct failure modes: pure hallucinations (5.1%), hallucinated identifiers with valid titles (16.4%), and parsing-induced matching failures (78.5%). Longitudinal analysis reveals a flat trend (+0.07 pp/month), suggesting that high-entropy citation practices have stabilized as an endemic feature of the field. Conclusion: The scientific citation graph in AI survey literature exhibits “link rot” at scale. This suggests a mechanism where AI tools act as “lazy research assistants,” retrieving correct titles but hallucinating metadata, thereby severing the digital chain of custody required for reproducible science.