Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequences

2026-09-21

AI Summary

研究使用Jev作为语义决策组件，通过比较不同配置下的科学案例，解决了科学工作流中选择合适关系的问题，以确保计算的准确性和效率。

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.

Problem

• Scientific Workflows
• Semantic Choices
• Consequences
• Jev

Innovation

• Jev
• Semantic Decision
• Scientific Workflow
• Latency
• Correctness