# Towards Nexus-Score: Metadata Gaps Limit Scholarly AI Attribution

arXiv: https://arxiv.org/abs/2607.22684 · HTML: https://mlsanity.com/paper/2607.22684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Reference Coverage Analysis of OpenAlex compared to Web of Science and Scopus](https://mlsanity.com/paper/2401.16359/) (2024)
- [Evaluating the Linguistic Coverage of OpenAlex: An Assessment of Metadata Accuracy and Completeness](https://mlsanity.com/paper/2409.10633/) (2024)

## Abstract (arXiv)

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.