基于刑法逻辑的可解释量刑预测模型，精度接近理论极限
Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability
- 构建基于中国刑法的机制式量刑模型SMS，实现法律逻辑可解释
- 在无数据平稳性假设下证明预测精度理论上限，实测精度逼近该上限
- 自建中文故意伤害案件数据集，适合司法研究与合规系统开发
现有量刑预测研究多依赖端到端模型，忽视量刑内在逻辑且缺乏可解释性，难以满足学术与司法实践需求。本文提出新型饱和机制量刑模型（SMS），其基础为中国刑法，具备天然法律可解释性，并配套设计了动量最小均方（MLMS）自适应算法。针对该算法，建立无需数据平稳性与独立性假设的预测精度数学理论，给出已知参数条件下最优预测器能达到的理论精度上界。同时构建中国故意伤害案件（CIBH）数据集，实验证明所提方法预测精度接近理论最优上界，验证了模型适用性与算法准确性。
原文摘要 · Abstract (English)
Existing research on judicial sentencing prediction predominantly relies on end-to-end models, which often neglect the inherent sentencing logic and lack interpretability-a critical requirement for both scholarly research and judicial practice. To address this challenge, we make three key contributions:First, we propose a novel Saturated Mechanistic Sentencing (SMS) model, which provides inherent legal interpretability by virtue of its foundation in China's Criminal Law. We also introduce the corresponding Momentum Least Mean Squares (MLMS) adaptive algorithm for this model. Second, for the MLMS algorithm based adaptive sentencing predictor, we establish a mathematical theory on the accuracy of adaptive prediction without resorting to any stationarity and independence assumptions on the data. We also provide a best possible upper bound for the prediction accuracy achievable by the best predictor designed in the known parameters case. Third, we construct a Chinese Intentional Bodily Harm (CIBH) dataset. Utilizing this real-world data, extensive experiments demonstrate that our approach achieves a prediction accuracy that is not far from the best possible theoretical upper bound, validating both the model's suitability and the algorithm's accuracy.
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