# Jev in the Wild: A Data-Driven Analysis of the Jev Model's Functionality, Applications and Ecosystem

arXiv: https://arxiv.org/abs/2609.30216 · HTML: https://mlsanity.com/paper/2609.30216/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2026.

## Built on by (most cited first, 7 of 7)

- [Typed Decision Models: An Early Evidence Audit and Evaluation Checklist](https://mlsanity.com/paper/2609.32160/) (2026)
- [Benchmarking System One decision models against trained classifiers and language models for automated decision gates](https://mlsanity.com/paper/2610.00346/) (2026)
- [Labels Override Definitions in Jev-Style Typed Decision Models](https://mlsanity.com/paper/2610.02586/) (2026)
- [Evaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation](https://mlsanity.com/paper/2609.33401/) (2026)
- [A First Glance at Jev for Network Traffic Classification: Accuracy, Processing Time, and Cost](https://mlsanity.com/paper/2610.00376/) (2026)
- [Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks](https://mlsanity.com/paper/2610.11978/) (2026)
- [When Faster Decisions Do Not Make Better Agents: An Empirical Study of Jev in Agent Workflows](https://mlsanity.com/paper/2610.13092/) (2026)

## Abstract (arXiv)

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.