Towards Nexus-Score: Metadata Gaps Limit Scholarly AI Attribution

Abstract:Artificial intelligence systems increasingly mediate how science is found and credited. We asked whether missing metadata prevents AI systems from crediting work. As a boundary test, an AI system citing without access to task-relevant paper lists often produced out-of-list identifiers, some fabricated. We then tested the mechanism in real scholarly infrastructure by using OpenAlex records to hide or restore author, institution, funder, reference, and text-access links while holding works and tasks fixed. Restoring the relevant link made the corresponding attribution possible; restoring the wrong kind did not, with 0 correct answers across 469 completed mismatched tests. Thus, in these tasks, one metadata facet did not substitute for another. Missing links led to invented answers, refusals, or tool-budget exhaustion, and web search did not recover hidden author links. In sum, AI systems credited work only when record connections were visible or recoverable. This motivates Nexus-Score, a record-level check for metadata gaps, to guide repair and help prepare the scholarly record for AI-mediated use.

Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2607.22684 [cs.DL]

(or arXiv:2607.22684v1 [cs.DL] for this version)

https://doi.org/10.48550/arXiv.2607.22684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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