arXiv:2602.06718v2 Announce Type: replace-cross
Abstract: Citations provide the basis for trusting scientific claims; when they are invalid or fabricated, this trust collapses. With the advent of Large Language Models (LLMs), this risk has intensified: LLMs are increasingly used for academic writing, but their tendency to fabricate citations (“ghost citations”) poses a systemic threat to citation validity. To quantify this threat, we develop citeb, an open-source framework for large-scale citation verification, and conduct a comprehensive study of citation validity in the LLM era through three complementary experiments. First, we benchmark 13 LLMs on citation generation task in various research domains, finding that all models hallucinate citations at rate from 14.23% to 94.93%. Second, we analyze 2.2 million citations from 56,381 papers at AI/ML and Security venues (2020–2025), finding that 1.07% of papers contain invalid citations, with an 80.9% increase in 2025. Third, we survey 97 researchers, finding that 87.2% use AI-powered tools in their workflows, 76.7% of reviewers do not thoroughly check references, and 74.5% view peer review as ineffective at catching citation errors. Based on these findings, we argue that ghost citations represent a systemic threat to academic integrity, and call for coordinated efforts from community to address this challenge.
Crisis support teams’ technological openness and learning attitudes toward the AI based virtual patient system crisis support VR
BackgroundAgainst the backdrop of escalating global humanitarian crises, innovative didactic simulations are becoming increasingly important. A promising alternative to traditional classroom-based didactics for learning psychological