Abstract
Longevity clinics need more than predictions: they need to translate a person's measurements into reviewable biological states, trace the evidence, and decide what to reassess. We introduce the Virtual Longevity Clinic (VLC), a clinic-oriented AI-agent research prototype that makes this interpretive lens explicit as a versioned, auditable State Map—the state-representation layer of a biomedical world model. An observation mapper records how measurements enter the map, while the knowledge base that supplies evidence and the map's inference boundaries remain distinct layers alongside it. The architecture is designed to connect map-grounded findings to person-level option review and planned follow-up. In a targeted survey of research systems and public clinic-product materials, VLC is the first research prototype we identified in person-level longevity medicine to make the State Map explicit, versioned, and auditable and connect it to option review and planned follow-up. We evaluated one aging-hallmark module workflow in 11 prepared cases (10 de-identified public profiles and one synthetic). Across five reruns per case (55 total), the top-ranked module remained unchanged. In a separate option-list replay, 29/32 suggestions were traceable to supplied options. Single-model blinded judging did not favor deliberation over a no-peeking control (14/33 versus 17/33; two ties); deliberation used 2.2 times as many tokens. A five-role round averaged 8,916 tokens and 137 seconds; its token-equivalent list-price estimate was approximately RMB 0.01 under stated assumptions. These results demonstrate repeatable software behavior and partial option traceability—not clinical benefit, safety, or calibrated confidence. Map selection and cross-Map comparisons, clinician review, and real follow-up remain untested. A paired fixed-input State Map v1/v2 replay is the next direct test before prospective clinician-reviewed studies.