Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequences

Abstract:Scientific workflows often require choosing among known relations before a deterministic calculation can proceed. Whether observations share a culture, treatment or reference standard can change the scientific meaning of the resulting count or comparison. We evaluate Jev as a semantic decision component using a harness that follows its documented guidance and assigns arithmetic to code. The study compares twelve model configurations on twenty source-grounded Choices across ten scientific cases, each repeated five times. We measure semantic selections, downstream outputs and final claim labels separately. Jev matched five other configurations at complete semantic correctness and achieved the lowest observed median latency among successful responses. Across three comparison models, seven wrong selections on one culture-history question changed downstream counts while preserving the correct final label. These results identify a useful role for Jev in prepared scientific decision tasks and show why evaluating that role requires checking the relations and quantities that a workflow will reuse.

11 pages, 1 figure, 5 tables. Includes references and appendices

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.24965 [cs.CL]

(or arXiv:2609.24965v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.24965 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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