GPTZero Finds AI-Hallucinated Citations in NeurIPS 2025 Papers
In a comprehensive sweep of more than 4,000 papers accepted and presented at NeurIPS 2025, the Canadian startup GPTZero uncovered hundreds of instances of AI-generated or incorrect citations slipping past traditional review filters. The finding adds to a growing concern about citation integrity in fast-moving AI research and highlights the need for stronger verification processes as conferences scale up their output.
What GPTZero Analyzed and How
GPTZero’s team analyzed a broad slice of NeurIPS 2025 proceedings, focusing on references, in-text citations, and bibliographic entries. The goal was to identify “AI-hallucinated” citations—statements that reference papers, datasets, or methods that either do not exist, are misattributed, or mischaracterized by automated or semi-automated generation tools. The company emphasizes that their work is exploratory and intended to spur discussion about improving accuracy in scholarly writing as AI-assisted authoring becomes more prevalent.
Scope and Methods
The project examined thousands of citations across multiple tracks within NeurIPS 2025, looking for irregularities such as non-existent papers, duplicated references with inconsistent metadata, and clashes between claimed claims and actual sources. While the researchers acknowledge the complexity of AI-driven literature reviews and the potential for false positives, they report a nontrivial rate of suspect citations that warrant closer human verification.
Why This Matters for Researchers
Accurate citations are foundational to academic credibility. When AI tools contribute to drafting, summarizing, or suggesting references, the risk of introducing hallucinated or incorrect citations increases. The NeurIPS 2025 findings suggest several implications for researchers, reviewers, and conference organizers:
- Researchers should double-check citations produced or suggested by AI tools, especially for highly cited or niche sources.
- Reviewers may need to allocate additional time or employ citation-verification tools during the evaluation process.
- Conferences and journals could implement automated cross-checking against bibliographic databases to flag potential discrepancies before publication.
Implications for the Peer-Review Process
As AI-assisted writing becomes more common, traditional peer review must evolve. The NeurIPS 2025 findings point to several potential improvements in the workflow:
– Standardized citation verification steps integrated into the manuscript submission system.
– Transparent reporting of AI-assisted editing or drafting in the manuscript methods or acknowledgments sections.
– Collaboration between publishers and bibliographic databases to build real-time checks for emerging AI-generated or misattributed references.
Practical Steps for Authors
Authors who use AI tools should adopt best practices to minimize hallucinated citations and protect the integrity of their work:
- Always verify every cited work directly in the source database or the original publication.
- Mention AI-assisted writing or drafting only if it contributed to the manuscript, with appropriate disclosures.
- Maintain meticulous notes of sources consulted during research to support traceability.
- Utilize reference-management software that can perform consistency checks and update metadata accurately.
What’s Next for NeurIPS and AI Research
The findings from GPTZero serve as a call to action rather than a verdict on NeurIPS 2025 as a whole. They underscore the importance of maintaining rigorous research practices amid rapid AI development. Organizers may explore integrating automated citation verification tools, while authors are encouraged to adopt robust governance around AI-assisted writing. If researchers, reviewers, and conference committees collaborate on enhanced verification, the credibility of AI research can keep pace with its innovation.
Conclusion
GPTZero’s analysis of NeurIPS 2025 papers reveals a non-trivial presence of AI-hallucinated citations, prompting a broader conversation about research integrity in an era of AI-assisted scholarship. By embracing stricter verification, transparent AI-use disclosures, and stronger bibliographic controls, the AI research community can strengthen trust in published work while continuing to push the boundaries of innovation.
