Researchers have introduced the Standard Interpretable Model (SIM), a general theory of interpretable machine learning based on Lagrangian mechanics. The SIM aims to fill the gap between theories and methods in interpretability by providing a deductive framework for designing interpretable methods. It summarizes what interpretability means for a target user, derives symmetries and constraints from these premises, and shapes the landscape of an optimal interpretable model. The authors empirically demonstrate that the SIM identifies limitations in existing methods and highlights underexplored research directions. --- Why it matters: This work matters to AI researchers because it provides a unified framework for designing interpretable methods, which is crucial for understanding, debugging, and controlling complex AI models. By shedding light on the limitations of existing approaches, the SIM can help researchers develop more effective and transparent methods for machine learning interpretability. Source: https://arxiv.org/abs/2606.12289