ML research is not serious research, NeurIPS board statement suggests (statmodeling.stat.columbia.edu)

The NeurIPS board recently addressed concerns over the integrity of research papers, specifically those containing "hallucinated references" generated by large language models (LLMs). An independent analysis identified that at least 53 papers accepted for NeurIPS 2025 included citations that were fabricated or significantly distorted, raising questions about the implications of such errors for the scientific rigor of AI research. The board highlighted ongoing deliberations about how to handle these issues, considering whether to reject affected papers, allow for corrections, or implement stricter submission policies that reject any paper with hallucinated references. This issue is crucial for the AI/ML community as it underscores the broader challenges posed by relying on LLMs in academic writing. The board's stance suggests a willingness to adapt review processes, but also raises concerns about potential complacency regarding the accuracy of references and citations. The distinction between minor citation errors and significant hallucinations questions the integrity of the research itself, with implications for authors' accountability in ensuring the validity of their claims. Ultimately, the NeurIPS board's handling of this matter will influence the standards and practices within AI research, shaping future interactions with generative models.