# The Case of the Mysterious Citations

arXiv: https://arxiv.org/abs/2602.05867 · HTML: https://mlsanity.com/paper/2602.05867/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2026.

## Built on by (most cited first, 5 of 5)

- [DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling](https://mlsanity.com/paper/2604.06474/) (2026)
- [HALLMARK: How to Diagnose LLM Citation Verifiers and When to Deploy Them](https://mlsanity.com/paper/2607.18360/) (2026)
- [HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists](https://mlsanity.com/paper/2604.26835/) (2026)
- [sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing](https://mlsanity.com/paper/2604.08501/) (2026)
- [Transect: Retaining Observability for Long-Horizon LLM Agent Evaluations](https://mlsanity.com/paper/2610.08364/) (2026)

## Abstract (arXiv)

Mysterious citations are routinely appearing in peer-reviewed publications throughout the scientific community. In this paper, we developed an automated pipeline and examine the proceedings of four major high-performance computing conferences, comparing the accuracy of citations between the 2021 and 2025 proceedings. While none of the 2021 papers contained mysterious citations, every 2025 proceeding did, impacting 2-6% of published papers. In addition, we observe a sharp rise in paper title and authorship errors, motivating the need for stronger citation-verification practice. No author within our dataset acknowledged using AI to generate citations even though all four conference policies required it, indicating current policies are insufficient.