This repository releases the DeBERTa-v3-large argument-quality regressor used in Belief Engine's judgment layer. Fine-tuned on the IBM Argument Quality Ranking dataset's WA human ratings, it takes an English argument and returns a quality score in [0, 1]. This is a reproducibility release of a fine-tuned checkpoint using established argument-quality regression methods. It provides the exact scorer used in the framework, verified inference, evaluation tools, and a documented recipe for training a new checkpoint. It has 435,062,785 parameters and a 1.74 GB weight file; the original December 2025 weights are preserved unchanged. Download the published model, scorer, and training/evaluation…