MEDIA PREVIEW
Overview & result
JevBenchmarkLab is described as an open harness with 200 ground-truth cases across 20 suites, covering consistency, adversarial failures, option-order bias, probability drift, and latency.
From the original post
Jev doesn’t need another demo. It needs a stress test.
Jev Benchmark Lab runs 200 ground-truth cases across 20 suites, exposing accuracy, consistency, adversarial failures, option-order bias, answer flips, probability drift and latency.
Let the numbers decide.
https://github.com/ersinkoc/JevBenchmarkLab
How Jev fits in the loop
- The harness sends fixed decision tasks to Jev and records structured outputs.
- It compares outputs with expected labels and repeated-run behavior.
- It produces reports that separate quality, calibration, and latency evidence.
How to reproduce
- Clone the repository and identify the datasets, expected labels, and report-generation command.
- Run one suite twice with fixed inputs to inspect consistency and answer flips.
- Record latency percentiles and calibration separately from task accuracy.
Why this build matters
It is useful infrastructure for the evaluation side of JevForAgents, where reproducibility matters more than a single impressive demo number.
Limitations
- The reviewed post does not establish that the suites represent production traffic.
- A benchmark harness can reveal failure modes but cannot guarantee generalization to a new domain.
- Results should be labeled as community evidence until independently rerun.