用拉格朗日力学统一可解释机器学习理论 The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics

精读摘要

可解释性已成为理解、调试和控制 AI 计算不可或缺的工具，但该领域缺乏能演绎式设计可解释方法的通用理论，导致文献碎片化、评估协议不一致。研究团队提出「标准可解释模型」（SIM），一个基于拉格朗日力学的通用理论框架，使可解释方法可以从原理出发演绎地设计。若成立，这将把可解释性研究从「经验试凑」推向「理论推导」。 Interpretability lacks general theories to deductively design interpretable methods, resulting in a fragmented literature and inconsistent evaluation protocols. The Standard Interpretable Model (SIM) is a general theory grounded in Lagrangian mechanics that enables the deductive design of interpretable methods. It aims to move interpretability from empirical tinkering toward principled theory.