The Citation Integrity Engine.
"Algorithmic eradication of citation cartels, hallucinated references, and recursive dependency loops via topological data analysis."
I. The Crisis of Epistemic Noise
The reliability of the scientific citation graph has been critically compromised by three primary vectors: LLM-hallucinated references, organized citation cartels designed to inflate h-indices, and circular dependency loops where hypotheses are validated purely by internal reference chains. The Citation Integrity Engine (CIE) was engineered to surgically detect and flag these anomalies prior to peer review.
II. Topological Data Analysis (TDA)
Upon manuscript ingestion, all references are extracted and projected into a multi-dimensional directed graph. The CIE applies Topological Data Analysis to measure the "Betti numbers" of the citation network.
Unusually dense, isolated clusters of self-citation or tightly knit cross-citations between a static group of authors are immediately flagged. If the clustering coefficient exceeds normal distributions for the specific sub-field by a factor of 3σ, the manuscript is halted, and the handling Associate Editor is notified of potential cartel activity.
III. Synthetic Hallucination Detection
With the advent of generative AI drafting, authors frequently submit manuscripts containing entirely fabricated citations—DOIs that do not exist, or real DOIs attached to fabricated paper titles.
The CIE performs a real-time cryptographic ping against global DOI registries (Crossref, DataCite) as well as our internal ARK directory. If a reference cannot be deterministically resolved to a verifiable archival node, the submission is automatically rejected during the Neural Pre-Screening phase.