Inside the JEV Ecosystem: 13 Answer Verifiers on One Test Set

A comparison of 13 answer-verification systems on a shared 2,018-item factual-correctness test set found that VIDRAFT’s 397B ZTC model and TypeSafe AI’s JEV were statistically tied at the top, with AUC scores of 0.7364 and 0.7350, respectively, while a simple baseline using answer length and formatting reached 0.7036 and exceeded eight systems. The evaluation used per-domain, size-weighted AUC and paired bootstrap intervals to avoid inflated pooled scores and uncertain rank distinctions, while separate tests of direct LLM judging showed GPT-5.2 performing competitively but at substantially higher cost. Results also indicated that model size alone did not consistently predict verification quality, as smaller models sometimes outperformed larger related models in particular domains. In an agent-routing experiment that sent the lowest-scored 20% of answers for stronger-model retries, ZTC improved final accuracy from 74.83% to 76.16%, whereas JEV slightly reduced it, illustrating that a verifier’s practical utility depends on the precision of selecting incorrect answers rather than AUC alone. The project publishes scores, labels, and grading code, while withholding source items due to licensing and access restrictions, and includes nonfunctional or unresolved reproductions on its leaderboard for transparency.

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