Intelligence Brief · Vaccine Science · Independent Evaluation
Same input. ASI vs frontier-AI defaults. Ranked #1 on R&D-risk reduction.
A senior vaccine scientist’s antibody-dependent-enhancement question on a live-attenuated dengue vaccine was answered by ASI’s proprietary methodology and by frontier-AI defaults on the same scientific input. The resulting deliverables were independently evaluated. The verdict was unanimous.
A senior vaccine scientist posed three queries about antibody-dependent enhancement (ADE) safety of a live-attenuated dengue vaccine, accompanied by a six-paper reference corpus.
The same input was given to ASI’s proprietary methodology and to frontier-AI defaults. The resulting deliverables were independently evaluated.
Top-rank unanimous on R&D-risk reduction.
Independent evaluation converged on ASI’s deliverable as #1. Verbatim from one of the evaluator analyses:
“ASI reduces R&D risk most for any operator who would make a deployment, regulatory, or monitoring decision based on these outputs. ASI is the only output that formally decomposes the question into a resolved safety layer and an empirically active coverage layer requiring 12–24 months of post-licensure surveillance.”
Structural attributes the evaluators surfaced.
The most consequential single difference, per the evaluators: ASI was the only output to decompose the operator’s question into a resolved safety layer and an empirically active coverage layer that named the post-licensure surveillance timeline. The other outputs treated the question as a literature inquiry. ASI treated it as a deployment decision under uncertainty.
ASI’s deliverable additionally surfaced a structural conflict-of-interest disclosure (the engagement principal was lead author of one supporting corpus paper) and named the children 2–11yr age-group scope as a high-severity uncertainty — neither addressed in the frontier-AI defaults.
Same scientific input. Independent evaluation. ASI’s deliverable ranked #1.
If you are evaluating AI for scientific discovery, the question is not which model. The question is which method. The evaluation is rerunnable on any engagement where the operator has informed consent.
If you’re making a deployment, regulatory, or monitoring decision and want this kind of audit on your scientific input, the evaluation is rerunnable.
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