Darwin-27B-ZTC: A Single-Pass Judge and a Quantitative Look at Its Calibration

Darwin-27B-ZTC is presented as a zero-token classifier designed to replace generative LLM judging with direct, single-pass probability estimation for typed decisions such as free-form correctness, multiple-choice selection, and quality scoring. The approach aims to avoid sequential decoding latency, sampling variance, and generation settings that can complicate comparisons, while providing a deterministic probability distribution intended for confidence-based routing, large-scale grading, and safety gating. On the zero-shot typed-decisions general split, the model reportedly achieved 0.743 overall accuracy across 2,000 judgments, with stronger performance on free-form correctness than on choice and ordinal scoring, alongside reported calibration metrics of 0.097 Brier score and 0.204 KL divergence. The article emphasizes that accuracy and calibration should be evaluated separately, recommends reliability diagrams and expected calibration error for threshold-setting, and notes that calibration may degrade under distribution shift, ordinal scoring remains comparatively weak, and training configuration details have not been disclosed.

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