autotrust/JEV-27B: fast, calibrated decisions and full reasoning from one open model

AutoTrust AI introduces autotrust/JEV-27B, an Apache-2.0 open-weights model designed for rapid, calibrated “System 1” decisions such as binary judgments, multiple-choice routing, and scored assessments, while retaining the original Qwen3.8-27B model’s separate “System 2” generation and reasoning capabilities. The model uses a detachable LoRA decision adapter and small classification head while freezing the backbone, which the authors say preserves byte-identical HumanEval performance and enables both modes to run from one vLLM server. In the authors’ evaluations, JEV-27B averaged 84.07 across six public decision benchmarks versus 83.85 for the closed TypeSafe Jev 1.13 teacher, reproduced its probability distributions with mean KL divergence of about 0.017 on held-out teacher-labeled data, and achieved 96–98% of Jev’s accuracy on an independent benchmark. The project reports median decision latency of 137 milliseconds on a B200 GPU, operation on a single H100 at roughly 100 decisions per second, and training costs of about 9.2 B200-hours for 0.4% of the backbone’s parameters. The authors caution that the model can inherit the teacher’s mistakes and option-order sensitivity, that some reported comparisons are self-run and in-distribution, and that high-stakes deployments should use confidence thresholds and human oversight.

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