Towards Nexus-Score: Metadata Gaps Limit Scholarly AI Attribution
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 tas...
| Authors | Aadi Narayana Varma Dantuluri, Sushrut Thorat, Paras Chopra |
|---|---|
| Primary category | cs.DL |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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