The Case of the Mysterious Citations
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Feb 2026.
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Citing papers per year: 2026: 5.
5 papers.
- 2026DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytellingcited by 7
- 2026HALLMARK: How to Diagnose LLM Citation Verifiers and When to Deploy Themcited by 2
- 2026HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientistscited by 1
- 2026sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writingcited by 0
- 2026Transect: Retaining Observability for Long-Horizon LLM Agent Evaluationscited by 0
- 2026Transect: Retaining Observability for Long-Horizon LLM Agent Evaluations6 Oct 2026
- 2026HALLMARK: How to Diagnose LLM Citation Verifiers and When to Deploy Them20 Jul 2026
- 2026HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists29 Apr 2026
- 2026sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing9 Apr 2026
- 2026DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling7 Apr 2026