Jev 在实际应用中的表现：关于 Jev 模型功能、应用及生态系统的数据驱动分析

中文摘要 AI 辅助

Jev 是一种快速、低成本的决策模型，能够以选择、二元判断和评分的形式回答自然语言问题。随着其公共生态系统迅速增长，Jev 在不同应用中的使用方式以及公众关注度与项目分布之间的关系仍不明确。为回答这些问题，我们对截至2026年9月22日从GitHub收集的2,170个公开可用的Jev项目进行了大规模数据驱动分析。我们发现Jev的公共生态系统早期增长迅速，既有新项目的创建，也有对现有仓库的集成。在不同领域中，项目将Jev用于多种决策目的，并组合使用其接口。属性判断和评分被广泛使用，而动作选择、内容过滤以及模型和工具选择的使用则因领域而异。这些模式表明，Jev作为一个可复用的决策组件，其功能随周围工作流程而变化。与此同时，公众关注度集中在路由和接口代理上，并未与项目数量保持一致。我们的研究结果为Jev新兴生态系统提供了定量视角，并为跨不同应用场景的通用决策模型的设计与评估提供了参考。

英文摘要

Jev is a fast, low-cost decision model that answers natural-language questions with choices, binary judgments, and scores. As its public ecosystem grows rapidly, it remains unclear how Jev is used across applications and how public attention relates to project distribution. To answer these questions, we conduct a large-scale, data-driven analysis of 2,170 publicly available Jev projects collected from GitHub as of September 22, 2026. We find rapid early growth in Jev's public ecosystem, with both new projects and integration into existing repositories. Across diverse domains, projects use Jev for multiple decision purposes and combine its interfaces. Attribute judgment and scoring are widely used, while the use of action selection, content filtering, and model and tool selection varies across domains. These patterns suggest that Jev serves as a reusable decision component whose functionality varies with the surrounding workflow. Meanwhile, public attention is concentrated in routing and interface agents and does not track project counts. Our findings provide a quantitative view of Jev's emerging ecosystem and inform the design and evaluation of general-purpose decision models across diverse application contexts.

发表机构

• Sun Yat-sen University（中山大学）
• The Chinese University of Hong Kong（香港中文大学）

机构由 AI 辅助整理，请以论文原文为准。