Multidisciplinary Professional Identity in AI Retrieval: A Bounded Review of Relevant Prior Art

I began this review with a question about how AI retrieval systems represent people whose professional activities span several fields. A person may hold different roles at the same time, change affiliations, or publish under different name variants. These circumstances create challenges for identifying the correct person and establishing which professional information applies at a particular time. I examined existing research to identify methods relevant to these problems. With AI assistance, I assembled 45 bibliographic records, provisionally grouped into 44 study clusters. Nine records received checks against specific passages in the original publications. The remaining 36 did not receive equivalent passage-level verification. One journal article and its corresponding preprint were identified as two records of the same study. The reviewed literature describes methods for identity resolution, author disambiguation, temporal data representation, provenance tracking, and evidence attribution. These methods address different technical problems. Their combined application to multidisciplinary professional identities has not been established by this review. The research question is whether linking person identities, professional roles, time periods, and supporting sources would improve the accuracy of information retrieved about multidisciplinary careers. This remains a hypothesis. I have not developed a MIRF system, conducted an experiment, or measured its accuracy. The literature search was selective and non-exhaustive. AI assistance was used for screening and synthesis, and no independent human reviewer assessed the findings. The review documents selected prior art, its evidentiary limitations, and questions for future investigation. It does not establish a new algorithm, scientific originality, or a validated method.