arXiv:2604.08501v2 Announce Type: replace-cross Abstract: Scientific papers make claims about prior work backed by citations. Verifying those citations at scale (that each cited paper exists, says what the citation claims, and is itself reliable) is structurally beyond what human review can deliver: a typical paper has dozens of citations, and a careful reviewer reads at most a handful end-to-end. AI-assisted writing makes this gap even more urgent: LLMs hallucinate references and may fill in plausible details from titles or abstracts of papers they never read, worse for the smaller local-weig
The proliferation of AI-assisted writing, particularly LLMs hallucinating references, is making the verification of scientific claims more urgent and exposing critical flaws in current review processes.
This publication highlights an inevitable challenge to the integrity of scientific discourse posed by generative AI, necessitating new infrastructure to maintain trust and accuracy in research.
The burden of verifying scientific citations will shift from unscalable human review processes to AI-assisted infrastructure designed to counteract the inaccuracies introduced by other AI tools.
- · AI-powered verification tools
- · Academic integrity institutions
- · Researchers using verification tools
- · Traditional peer review processes
- · Scientific publications with weak vetting
- · LLMs without robust citation controls
Increased pressure on academic publishers and institutions to adopt advanced verification systems for submitted research.
Development of new industry standards and best practices for AI-assisted scientific writing and citation validation.
A potential bifurcation in scientific trust, where research verified by robust AI systems is given higher credibility.
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Read at arXiv cs.CL